US20220110238A1 - Machine control using a predictive map - Google Patents

Abstract

One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.

Description

FIELD OF THE DESCRIPTION
• The present description relates to agricultural machines, forestry machines, construction machines and turf management machines.
BACKGROUND
• There are a wide variety of different types of agricultural machines. Some agricultural machines include harvesters, such as combine harvesters, sugar cane harvesters, cotton harvesters, self-propelled forage harvesters, and windrowers. Some harvesters can also be fitted with different types of heads to harvest different types of crops.
• A variety of different conditions in fields have a number of deleterious effects on the harvesting operation. Therefore, an operator may attempt to modify control of the harvester, upon encountering such conditions during the harvesting operation.
• The discussion above is merely provided for general background information and is not intended to be used as an aid in determining the scope of the claimed subject matter.
SUMMARY
• One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.
• This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. The claimed subject matter is not limited to examples that solve any or all disadvantages noted in the background.
BRIEF DESCRIPTION OF THE DRAWINGS
• FIG. 1 is a partial pictorial, partial schematic illustration of one example of a combine harvester.
• FIG. 2 is a block diagram showing some portions of an agricultural harvester in more detail, according to some examples of the present disclosure.
• FIGS. 3A-3B (collectively referred to herein asFIG. 3) show a flow diagram illustrating an example of operation of an agricultural harvester in generating a map.
• FIG. 4 is a block diagram showing one example of a predictive model generator and a predictive map generator.
• FIG. 5 is a flow diagram showing an example of operation of an agricultural harvester in receiving a vegetative index map, detecting a characteristic, and generating a functional predictive biomass map for use in controlling the agricultural harvester during a harvesting operation.
• FIG. 6 is a block diagram showing one example of a predictive model generator and a predictive map generator.
• FIG. 7 shows a flow diagram illustrating one example of the operation of an agricultural harvester in receiving a prior information map and detecting an in-situ sensor input in generating a functional predictive map.
• FIG. 8 is a block diagram showing one example of in-situ sensor(s).
• FIG. 9 is a block diagram showing one example of a control zone generator.
• FIG. 10 is a flow diagram illustrating one example of the operation of the control zone generator shown inFIG. 8.
• FIG. 11 is a flow diagram showing an example of the operation of a control system in selecting a target settings value to control the agricultural harvester.
• FIG. 12 is a block diagram showing one example of an operator interface controller.
• FIG. 13 is a flow diagram illustrating one example of an operator interface controller.
• FIG. 14 is a pictorial illustration showing one example of an operator interface display.
• FIG. 15 is a block diagram showing one example of an agricultural harvester in communication with a remote server environment.
• FIGS. 16-18 show examples of mobile devices that can be used in an agricultural harvester.
• FIG. 19 is a block diagram showing one example of a computing environment that can be used in an agricultural harvester and the architectures illustrated in previous figures.
DETAILED DESCRIPTION
• For the purposes of promoting an understanding of the principles of the present disclosure, reference will now be made to the examples illustrated in the drawings, and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the disclosure is intended. Any alterations and further modifications to the described devices, systems, methods, and any further application of the principles of the present disclosure are fully contemplated as would normally occur to one skilled in the art to which the disclosure relates. In particular, it is fully contemplated that the features, components, and/or steps described with respect to one example may be combined with the features, components, and/or steps described with respect to other examples of the present disclosure.
• The present description relates to using in-situ data taken concurrently with an agricultural operation, in combination with prior data, to generate a predictive map and, more particularly, a predictive biomass map. In some examples, the predictive biomass map can be used to control an agricultural work machine, such as an agricultural harvester. Biomass, as used herein, refers to an amount of above ground vegetation material, such as crop plants and weed plants, in a given area or location. Often, the amount is measured in terms of weight, for instance, weight per given area, such as tons per acre. Various characteristics can be indicative of biomass (referred to herein as biomass characteristics) and can be used to predict the biomass on a field of interest. For example, biomass characteristics can include various vegetation characteristics, such as vegetation height (e.g., the height of the vegetation above the surface of the field, such as the height of the crop or crop canopy above the surface of the field), vegetation density (the amount of crop matter in a given volume, which can be derived from the crop mass and crop volume), vegetation mass (such as a weight of the vegetation or the weight of vegetation components), or vegetation volume (how much of the given area or location is taken up by the vegetation, that is the space that the vegetation occupies or contains). It will be noted that the vegetation characteristics can include individual characteristics of different vegetation types, for instance, vegetation characteristics may be crop characteristics or weed characteristics. For instance, vegetation characteristics may include crop height, crop density, crop mass, or crop volume. Thus, as used herein, vegetation characteristics, such as vegetation height, vegetation density, vegetation mass, or vegetation volume may include or comprise crop height, crop density, crop mass, or crop volume. In another example, biomass characteristics can include various machine characteristics of the agricultural harvester, such as machine settings, operating characteristics, or machine performance characteristics. For example, a force, such as a fluid pressure or torque, used to drive a threshing rotor of the agricultural harvester can be a machine characteristic indicative of the biomass.
• The performance of an agricultural harvester may be affected when the agricultural harvester engages areas of the field with variances in biomass. For instance, if the machine settings of the agricultural harvester are set on the basis of an expected or desired throughput, the variance in biomass can cause the throughput to vary, and, thus, the machine settings can be suboptimal for effectively processing the vegetation, including the crop. As mentioned above, the operator can attempt to predict the biomass ahead of the machine. Additionally, some systems, such as feedback control systems, reactively adjust the forward ground speed of the agricultural harvester in an attempt to maintain a desired throughput. This can be done by attempting to identify the biomass based on sensor inputs, such as from sensors that sense a variable indicative of biomass. However, such arrangements can be prone to error and can be too slow to react to an upcoming change in biomass to effectively alter the operation of the machine to control throughput, such as by changing the forward speed of the harvester. For instance, such systems are typically reactive in that adjustments to the machine settings are made only after the vegetation has been encountered by the machine in attempt to reduce further error, such as in a feedback control system.
• Some current systems provide vegetative index maps. A vegetative index map illustratively maps vegetative index values (which may be indicative of vegetative growth) across different geographic locations in a field of interest. One example of a vegetative index includes a normalized difference vegetation index (NDVI). There are many other vegetative indices that are within the scope of the present disclosure. In some examples, a vegetative index may be derived from sensor readings of one or more bands of electromagnetic radiation reflected by the plants. Without limitations, these bands may be in the microwave, infrared, visible or ultraviolet portions of the electromagnetic spectrum.
• A vegetative index map can be used to identify the presence and location of vegetation. In some examples, a vegetative index map enables crops to be identified and georeferenced in the presence of bare soil, crop residue, or other plants, such as weeds. In other examples, a vegetative index map enables the detection of various crop characteristics, such as crop growth and crop health or vigor, across different geographic locations in a field of interest. However, a vegetative index map may not accurately or reliably indicate other vegetation characteristics, such as vegetation characteristics indicative of a biomass of vegetation. Thus, in some instances, such as in accounting for biomass, a vegetative index map may have reduced usefulness in predicting how to control an agricultural harvester as the agricultural harvester moves through the field.
• The present discussion thus proceeds with respect to systems that receive a vegetative index map of a field or map generated during a prior operation and also use an in-situ sensor to detect a variable indicative of biomass during a harvesting operation. In some instances, the in-situ sensor may detect a height, a density, a mass, or a volume of vegetation in an area or location on the field, for example, an area in front of a header attached to an agricultural harvester, such asheader102 ofagricultural harvester100. The detected height, density, mass, or volume of vegetation can be indicative of a biomass of the vegetation. For instance, by knowing the height of vegetation, such as the height of the crops or crop canopy above the surface of the field, a biomass of the vegetation can be estimated. This is because there is a relationship between the height of the vegetation and the biomass of the vegetation, generally, the greater the height, the greater the biomass. Other vegetation characteristics, such as density, mass, or volume, also have a relationship with biomass, such that a value of the vegetation characteristic can be correlated to biomass and thus a biomass of vegetation can be estimated. By detecting one or more of these vegetation characteristics a biomass level value can be predicted, for example, high, medium, or low biomass. In some examples, more finite values, such as predicted weight values, can also be predicted. By way of illustration, a detected crop height that is relatively high (relative to general or known heights of specific vegetation, such as specific crops or a specific genotype of a crop) can indicate a resultingly high biomass. In some examples, a single characteristic can be detected and used for the estimation of biomass. For instance, given a detected vegetation height, other vegetation characteristics, such as vegetation density, vegetation volume, or vegetation mass, can be estimated based on, for instance, vegetative index values, historical data, prior operation data (such as data obtained from a seeding map, which may contain genotype data, seed spacing data, seed depth data, and various other seeding characteristics data), crop genotype data (e.g., species data, hybrid data, cultivar data, etc.), operator or user input, third-party information, expert knowledge, machine learning, as well as a variety of other information, or combinations thereof. In some examples, a combination of characteristics can be detected and used for the estimation of biomass, for example, a combination of vegetation height, vegetation density, vegetation mass, or vegetation volume.
• In another example, the in-situ sensor may detect a force, such as a fluid pressure or torque, required to drive a threshing rotor as an agricultural harvester processes crops, such asagricultural harvester100. For example, the force used to drive the threshing rotor at a given setting, such as a given speed (e.g., RPM) setting, can be affected by the load on the drive system, such as an engine assembly or a hydraulic motor assembly. The force required to drive the threshing rotor at a given setting in an empty machine condition (where there is no vegetation being processed) can be known. Thus, additional force used to drive the threshing rotor at a given setting when the harvester is processing vegetation can be indicative of a biomass of the vegetation being processed as the biomass of the vegetation will increase the load on the drive system.
• The systems generate a model that models a relationship between the vegetative index values on the vegetative index map or the values on the map generated from the prior operation and the output values from the in-situ sensor. The model is used to generate a functional predictive biomass map that predicts, for example, biomass at different locations in the field. The functional predictive biomass map, generated during the harvesting operation, can be presented to an operator or other user or used in automatically controlling a harvester during the harvesting operation, or both.
• In other examples, the present discussion proceeds with respect to systems that receive a map, such as a prior information map, a map generated on the basis of a prior operation, or a functional predictive map, for instance a predictive biomass map, and also use an in-situ sensor to detect a variable indicative of one or more characteristics during a harvesting operation. For example, the in-situ sensor may sense an agricultural characteristic, such as a non-machine characteristic, a machine characteristic of the agricultural harvester, or operator command inputs. An agricultural characteristic is any characteristic which may affect an agricultural operation. It will be noted, however, that the in-situ sensor can detect a value indicative of any of a number of characteristics and is not limited to the characteristics described herein. The systems generate a model that models a relationship between the values on the received map and the output values from the in-situ sensor. The model is used to generate a functional predictive map that predicts, for example, agricultural characteristics, such as non-machine characteristics, for instance, characteristics of the field or vegetation, machine characteristics of the agricultural harvester, such as machine settings, machine operating characteristics, or machine performance characteristics, or operator command inputs at different areas of the field based on the values from the received map at those areas. The functional predictive map, generated during the harvesting operation, can be presented to an operator or other user or used in automatically controlling an agricultural harvester during the harvesting operation, or both. The functional predictive map can be used to control one or more of the controllable subsystems on the agricultural harvester.
• FIG. 1 is a partial pictorial, partial schematic, illustration of a self-propelledagricultural harvester100. In the illustrated example,agricultural harvester100 is a combine harvester. Further, although combine harvesters are provided as examples throughout the present disclosure, it will be appreciated that the present description is also applicable to other types of harvesters, such as cotton harvesters, sugarcane harvesters, self-propelled forage harvesters, windrowers, or other agricultural work machines. Consequently, the present disclosure is intended to encompass the various types of harvesters described and is, thus, not limited to combine harvesters. Moreover, the present disclosure is directed to other types of work machines, such as agricultural seeders and sprayers, construction equipment, forestry equipment, and turf management equipment where generation of a predictive map may be applicable. Consequently, the present disclosure is intended to encompass these various types of harvesters and other work machines and is, thus, not limited to combine harvesters.
• As shown inFIG. 1,agricultural harvester100 illustratively includes anoperator compartment101, which can have a variety of different operator interface mechanisms, for controllingagricultural harvester100.Agricultural harvester100 includes front-end equipment, such as aheader102, and a cutter generally indicated at104.Agricultural harvester100 also includes afeeder house106, afeed accelerator108, and a thresher generally indicated at110. Thefeeder house106 and thefeed accelerator108 form part of amaterial handling subsystem125.Header102 is pivotally coupled to aframe103 ofagricultural harvester100 alongpivot axis105. One ormore actuators107 drive movement ofheader102 aboutaxis105 in the direction generally indicated byarrow109. Thus, a vertical position of header102 (the header height) aboveground111 over which theheader102 travels is controllable by actuatingactuator107. While not shown inFIG. 1,agricultural harvester100 may also include one or more actuators that operate to apply a tilt angle, a roll angle, or both to theheader102 or portions ofheader102. Tilt refers to an angle at which thecutter104 engages the crop. The tilt angle is increased, for example, by controllingheader102 to point adistal edge113 ofcutter104 more toward the ground. The tilt angle is decreased by controllingheader102 to point thedistal edge113 ofcutter104 more away from the ground. The roll angle refers to the orientation ofheader102 about the front-to-back longitudinal axis ofagricultural harvester100.
• Thresher110 illustratively includes a threshingrotor112 and a set ofconcaves114. Further,agricultural harvester100 also includes aseparator116.Agricultural harvester100 also includes a cleaning subsystem or cleaning shoe (collectively referred to as cleaning subsystem118) that includes a cleaningfan120,chaffer122, andsieve124. Thematerial handling subsystem125 also includesdischarge beater126,tailings elevator128,clean grain elevator130, as well as unloadingauger134 andspout136. The clean grain elevator moves clean grain intoclean grain tank132.Agricultural harvester100 also includes aresidue subsystem138 that can includechopper140 andspreader142.Agricultural harvester100 also includes a propulsion subsystem that includes an engine that drivesground engaging components144, such as wheels or tracks. In some examples, a combine harvester within the scope of the present disclosure may have more than one of any of the subsystems mentioned above. In some examples,agricultural harvester100 may have left and right cleaning subsystems, separators, etc., which are not shown inFIG. 1.
• In operation, and by way of overview,agricultural harvester100 illustratively moves through a field in the direction indicated byarrow147. Asagricultural harvester100 moves, header102 (and the associated reel164) engages the crop to be harvested and gathers the crop towardcutter104. An operator ofagricultural harvester100 can be a local human operator, a remote human operator, or an automated system. An operator command is a command by an operator. The operator ofagricultural harvester100 may determine one or more of a height setting, a tilt angle setting, or a roll angle setting forheader102. For example, the operator inputs a setting or settings to a control system, described in more detail below, that controlsactuator107. The control system may also receive a setting from the operator for establishing the tilt angle and roll angle of theheader102 and implement the inputted settings by controlling associated actuators, not shown, that operate to change the tilt angle and roll angle of theheader102. Theactuator107 maintainsheader102 at a height aboveground111 based on a height setting and, where applicable, at desired tilt and roll angles. Each of the height, roll, and tilt settings may be implemented independently of the others. The control system responds to header error (e.g., the difference between the height setting and measured height ofheader104 aboveground111 and, in some examples, tilt angle and roll angle errors) with a responsiveness that is determined based on a selected sensitivity level. If the sensitivity level is set at a greater level of sensitivity, the control system responds to smaller header position errors, and attempts to reduce the detected errors more quickly than when the sensitivity is at a lower level of sensitivity.
• Returning to the description of the operation ofagricultural harvester100, after crops are cut bycutter104, the severed crop material is moved by a conveyor infeeder house106 towardfeed accelerator108, which accelerates the crop material intothresher110. The crop material is threshed byrotor112 rotating the crop againstconcaves114. The force used to drive (or power)rotor112 can be sensed, and the sensed force, or sensed indication of force, can be used to determine a biomass being threshed. For instance, the fluid pressure, such a hydraulic or pneumatic pressure, that is used to driverotor112 can be sensed, and the sensed fluid pressure can be used to determine a biomass being processed byagricultural harvester100. In another example, the torque used to driverotor112 can be sensed, and the sensed torque can be used to determine a biomass being processed byagricultural harvester100. Threshing rotor drive force can be used as an indication of the biomass being processed by the thresher inagricultural harvester100, as the threshing rotor drive force is the force, such as torque or pressure, used to maintain the threshingrotor112 at a desired speed. The threshing rotor drive force correlates (along with various other machine settings, such as concave settings) with the biomass moving through the thresher inagricultural harvester100 at a particular time. In some instances, threshingrotor112 can be driven (or powered) by other power systems, and the power from those other power systems that is used to operate the threshing rotor can be sensed and used as an indication of a biomass being processed through the thresher inagricultural harvester100.
• The threshed crop material is moved by a separator rotor inseparator116 where a portion of the residue is moved bydischarge beater126 toward theresidue subsystem138. The portion of residue transferred to theresidue subsystem138 is chopped byresidue chopper140 and spread on the field byspreader142. In other configurations, the residue is released from theagricultural harvester100 in a windrow. In other examples, theresidue subsystem138 can include weed seed eliminators (not shown) such as seed baggers or other seed collectors, or seed crushers or other seed destroyers.
• Grain falls to cleaningsubsystem118.Chaffer122 separates some larger pieces of material from the grain, and sieve124 separates some of finer pieces of material from the clean grain. Clean grain falls to an auger that moves the grain to an inlet end ofclean grain elevator130, and theclean grain elevator130 moves the clean grain upwards, depositing the clean grain inclean grain tank132. Residue is removed from thecleaning subsystem118 by airflow generated by cleaningfan120.Cleaning fan120 directs air along an airflow path upwardly through the sieves and chaffers. The airflow carries residue rearwardly inagricultural harvester100 toward theresidue handling subsystem138.
• Tailings elevator128 returns tailings tothresher110 where the tailings are re-threshed. Alternatively, the tailings also may be passed to a separate re-threshing mechanism by a tailings elevator or another transport device where the tailings are re-threshed as well.
• FIG. 1 also shows that, in one example,agricultural harvester100 includesground speed sensor146, one or moreseparator loss sensors148, aclean grain camera150, a forward lookingimage capture mechanism151, which may be in the form of a stereo or mono camera, and one ormore loss sensors152 provided in thecleaning subsystem118.
• Ground speed sensor146 senses the travel speed ofagricultural harvester100 over the ground.Ground speed sensor146 may sense the travel speed of theagricultural harvester100 by sensing the speed of rotation of the ground engaging components (such as wheels or tracks), a drive shaft, an axel, or other components. In some instances, the travel speed may be sensed using a positioning system, such as a global positioning system (GPS), a dead reckoning system, a long range navigation (LORAN) system, or a wide variety of other systems or sensors that provide an indication of travel speed.
• Loss sensors152 illustratively provide an output signal indicative of the quantity of grain loss occurring in both the right and left sides of thecleaning subsystem118. In some examples,sensors152 are strike sensors which count grain strikes per unit of time or per unit of distance traveled to provide an indication of the grain loss occurring at thecleaning subsystem118. The strike sensors for the right and left sides of thecleaning subsystem118 may provide individual signals or a combined or aggregated signal. In some examples,sensors152 may include a single sensor as opposed to separate sensors provided for eachcleaning subsystem118.
• Separator loss sensor148 provides a signal indicative of grain loss in the left and right separators, not separately shown inFIG. 1. Theseparator loss sensors148 may be associated with the left and right separators and may provide separate grain loss signals or a combined or aggregate signal. In some instances, sensing grain loss in the separators may also be performed using a wide variety of different types of sensors as well.
• Agricultural harvester100 may also include other sensors and measurement mechanisms. For instance, agricultural harvester100 may include one or more of the following sensors: a header height sensor that senses a height of header102 above ground111; stability sensors that sense oscillation or bouncing motion (such as oscillation frequency and amplitude) of agricultural harvester100; a residue setting sensor that is configured to sense whether agricultural harvester100 is configured to chop the residue, produce a windrow, etc.; a cleaning shoe fan speed sensor to sense the speed of fan120; a concave clearance sensor that senses a size of the clearance between the rotor112 and concaves114; a threshing rotor speed sensor that senses a rotor speed of rotor112; a force sensor that senses a force used to drive threshing rotor112, such as a pressure sensor that senses a fluid pressure used to drive threshing rotor112 or a torque sensor that senses a torque used to drive threshing rotor112; a chaffer clearance sensor that senses the size of openings in chaffer122; a sieve clearance sensor that senses the size of openings in sieve124; a material other than grain (MOG) moisture sensor that senses a moisture level of the MOG passing through agricultural harvester100; one or more machine setting sensors configured to sense various configurable settings of agricultural harvester100; a machine orientation sensor that senses the orientation of agricultural harvester100; and crop property sensors that sense a variety of different types of crop properties, such as crop height, crop density, crop volume, crop mass, and other crop properties. Crop property sensors may also be configured to sense characteristics of the severed crop material as the crop material is being processed byagricultural harvester100. For example, in some instances, the crop property sensors may sense grain quality such as broken grain, MOG levels; grain constituents such as starches and protein; and grain feed rate as the grain travels through thefeeder house106,clean grain elevator130, or elsewhere in theagricultural harvester100. The crop property sensors may also sense the feed rate of biomass throughfeeder house106, through theseparator116 or elsewhere inagricultural harvester100. The crop property sensors may also sense the feed rate as a mass flow rate of grain throughelevator130 or through other portions of theagricultural harvester100 or provide other output signals indicative of other sensed variables. Crop property sensors can include one or more yield sensors that sense crop yield being harvested by the agricultural harvester.
• In one example, various machine settings can be set or controlled to achieve a desired performance. The machine settings can include such things as concave clearance, rotor speed, sieve and chaffer settings, and cleaning fan speed. Other machine settings can also be controlled. These machine settings can illustratively be set or controlled based on an expected throughput, that is, the amount of material processed byagricultural harvester100 per unit of time. Thus, if the biomass varies spatially in the field and the ground speed ofagricultural harvester100 remains constant, then the throughput will change with biomass. In some examples, the biomass being processed is indicated by sensing the force used to drive threshingrotor112 at a desired speed, and the ground speed ofagricultural harvester100 is varied in an attempt to maintain the desired throughput. In other examples, forward lookingimage capture mechanism151 can be used to estimate one or more of a vegetation height, a vegetation density, a vegetation volume, and a vegetation mass in a given area of the field ahead ofagricultural harvester100 to predict a biomass that is about to be processed byagricultural harvester100. Other vegetation characteristics may also be estimated using the captured image(s) from the forward lookingimage capture mechanism151. In such examples, the vegetation characteristics can be converted into a georeferenced biomass value indicative of the biomass that is about to be engaged byagricultural harvester100 in an upcoming area of the field. The machine speed, as well as various other machine settings, such as header height, can be controlled based on the estimated biomass to maintain the desired throughput.
• Prior to describing howagricultural harvester100 generates a functional predictive biomass map and uses the functional predictive biomass map for control, a brief description of some of the items onagricultural harvester100 and their respective operations will first be described. The description ofFIGS. 2 and 3 describe receiving a general type of prior information map and combining information from the prior information map with a georeferenced sensor signal generated by an in-situ sensor, where the sensor signal is indicative of a characteristic in the field, such as characteristics of crop present in the field. Characteristics of the field may include, but are not limited to, characteristics of a field such as slope, weed intensity, weed type, soil moisture, surface quality; characteristics of vegetation properties, such as vegetation height, vegetation volume, vegetation moisture, vegetation mass, and vegetation density; characteristics of crop properties, such as crop height, crop volume, crop moisture, crop mass, crop density, and crop state; characteristics of grain properties such as grain moisture, grain size, grain test weight; and characteristics of machine performance such as loss levels, job quality, fuel consumption, and power utilization. A relationship between the characteristic values obtained from in-situ sensor signals and the prior information map values is identified, and that relationship is used to generate a new functional predictive map. A functional predictive map predicts values at different geographic locations in a field, and one or more of those values may be used for controlling a machine, such as one or more subsystems of an agricultural harvester. In some instances, a functional predictive map can be presented to a user, such as an operator of an agricultural work machine, which may be an agricultural harvester. A functional predictive map may be presented to a user visually, such as via a display, haptically, or audibly. The user may interact with the functional predictive map to perform editing operations and other user interface operations. In some instances, a functional predictive map can be used for one or more of controlling an agricultural work machine, such as an agricultural harvester, presentation to an operator or other user, and presentation to an operator or user for interaction by the operator or user.
• After the general approach is described with respect toFIGS. 2 and 3, a more specific approach for generating a functional predictive biomass map that can be presented to an operator or user, or used to controlagricultural harvester100, or both is described with respect toFIGS. 4 and 5. Again, while the present discussion proceeds with respect to the agricultural harvester and, particularly, a combine harvester, the scope of the present disclosure encompasses other types of agricultural harvesters or other agricultural work machines.
• FIG. 2 is a block diagram showing some portions of an exampleagricultural harvester100.FIG. 2 shows thatagricultural harvester100 illustratively includes one or more processors orservers201,data store202,geographic position sensor204,communication system206, and one or more in-situ sensors208 that sense one or more agricultural characteristics of a field concurrent with a harvesting operation. An agricultural characteristic can include any characteristic that can have an effect of the harvesting operation. Some examples of agricultural characteristics include characteristics of the harvesting machine, the field, the plants on the field, and the weather. Other types of agricultural characteristics are also included. The in-situ sensors208 generate values corresponding to the sensed characteristics. Theagricultural harvester100 also includes a predictive model or relationship generator (collectively referred to hereinafter as “predictive model generator210”),predictive map generator212,control zone generator213,control system214, one or morecontrollable subsystems216, and anoperator interface mechanism218. Theagricultural harvester100 can also include a wide variety of otheragricultural harvester functionality220. The in-situ sensors208 include, for example, on-board sensors222, remote sensors224, andother sensors226 that sense characteristics of a field during the course of an agricultural operation.Predictive model generator210 illustratively includes a prior information variable-to-in-situvariable model generator228, andpredictive model generator210 can includeother items230.Control system214 includescommunication system controller229,operator interface controller231, asettings controller232,path planning controller234, feedrate controller236, header andreel controller238,draper belt controller240, deckplate position controller242,residue system controller244,machine cleaning controller245,zone controller247, andsystem214 can includeother items246.Controllable subsystems216 include machine andheader actuators248,propulsion subsystem250,steering subsystem252,residue subsystem138,machine cleaning subsystem254, andsubsystems216 can include a wide variety ofother subsystems256.
• FIG. 2 also shows thatagricultural harvester100 can receiveprior information map258. As described below, theprior information map258 includes, for example, a vegetative index map or a vegetation map from a prior operation. However,prior information map258 may also encompass other types of data that were obtained prior to a harvesting operation or a map from a prior operation.FIG. 2 also shows that anoperator260 may operate theagricultural harvester100. Theoperator260 interacts withoperator interface mechanisms218. In some examples,operator interface mechanisms218 may include joysticks, levers, a steering wheel, linkages, pedals, buttons, dials, keypads, user actuatable elements (such as icons, buttons, etc.) on a user interface display device, a microphone and speaker (where speech recognition and speech synthesis are provided), among a wide variety of other types of control devices. Where a touch sensitive display system is provided,operator260 may interact withoperator interface mechanisms218 using touch gestures. These examples described above are provided as illustrative examples and are not intended to limit the scope of the present disclosure. Consequently, other types ofoperator interface mechanisms218 may be used and are within the scope of the present disclosure.
• Prior information map258 may be downloaded ontoagricultural harvester100 and stored indata store202, usingcommunication system206 or in other ways. In some examples,communication system206 may be a cellular communication system, a system for communicating over a wide area network or a local area network, a system for communicating over a near field communication network, or a communication system configured to communicate over any of a variety of other networks or combinations of networks.Communication system206 may also include a system that facilitates downloads or transfers of information to and from a secure digital (SD) card or a universal serial bus (USB) card or both.
• Geographic position sensor204 illustratively senses or detects the geographic position or location ofagricultural harvester100.Geographic position sensor204 can include, but is not limited to, a global navigation satellite system (GNSS) receiver that receives signals from a GNSS satellite transmitter.Geographic position sensor204 can also include a real-time kinematic (RTK) component that is configured to enhance the precision of position data derived from the GNSS signal.Geographic position sensor204 can include a dead reckoning system, a cellular triangulation system, or any of a variety of other geographic position sensors.
• In-situ sensors208 may be any of the sensors described above with respect toFIG. 1. In-situ sensors208 include on-board sensors222 that are mounted on-boardagricultural harvester100. Such sensors may include, for instance, a perception sensor (e.g., a forward looking mono or stereo camera system and image processing system), image sensors that are internal to agricultural harvester100 (such as the clean grain camera or cameras mounted to identify weed seeds that are exitingagricultural harvester100 through the residue subsystem or from the cleaning subsystem). The in-situ sensors208 also include remote in-situ sensors224 that capture in-situ information. In-situ data include data taken from a sensor on-board the agricultural harvester or taken by any sensor where the data are detected during the harvesting operation.
• Predictive model generator210 generates a model that is indicative of a relationship between the values sensed by the in-situ sensor208 and a value mapped to the field by theprior information map258. For example, if theprior information map258 maps a vegetative index value to different locations in the field, and the in-situ sensor208 senses a value indicative of biomass, then prior information variable-to-in-situvariable model generator228 generates a predictive biomass model that models the relationship between the vegetative index value and the biomass value, such that a biomass value, for a location in the field, can be predicted based on the vegetative index value corresponding to that location. This is because the biomass, at any given location in the field, may be affected by or have a relationship to a characteristic indicated by the vegetative index values contained in theprior information map258, such as crop growth or crop health associated with the corresponding locations in the field. The predictive biomass model can also be generated based on vegetative index values from theprior information map258 and multiple in-situ data values generated by in-situ sensors208.Predictive map generator212 uses the predictive biomass model generated bypredictive model generator210 to generate a functional predictive biomass map that predicts the value of biomass or a biomass characteristic, such as vegetation height, vegetation density, vegetation volume, or vegetation volume, or vegetation characteristics of specific types of vegetation, such as crops, for instance, crop height, crop density, crop volume, or crop mass. In other examples, the biomass characteristic may be a force used to drive the threshing rotor. The predicted biomass or biomass characteristic values are generated using both the values sensed by the in-situ sensor or sensors208 (which may be the sensed values of biomass or a biomass characteristic) at different locations in the field and the values of the characteristic mapped in theprior information map258, such as vegetative index values, corresponding to those locations in the field.
• In some examples, the type of values in the functionalpredictive map263 may be the same as the in-situ data type sensed by the in-situ sensors208. In some instances, the type of values in the functionalpredictive map263 may have different units from the data sensed by the in-situ sensors208. In some examples, the type of values in the functionalpredictive map263 may be different from the data type sensed by the in-situ sensors208 but have a relationship to the type of data type sensed by the in-situ sensors208. For example, in some examples, the data type sensed by the in-situ sensors208 may be indicative of the type of values in the functionalpredictive map263. In some examples, the type of data in the functionalpredictive map263 may be different than the data type in theprior information map258. In some instances, the type of data in the functionalpredictive map263 may have different units from the data in theprior information map258. In some examples, the type of data in the functionalpredictive map263 may be different from the data type in theprior information map258 but has a relationship to the data type in theprior information map258. For example, in some examples, the data type in theprior information map258 may be indicative of the type of data in the functionalpredictive map263. In some examples, the type of data in the functionalpredictive map263 is different than one of, or both of the in-situ data type sensed by the in-situ sensors208 and the data type in theprior information map258. In some examples, the type of data in the functionalpredictive map263 is the same as one of, or both of, of the in-situ data type sensed by the in-situ sensors208 and the data type inprior information map258. In some examples, the type of data in the functionalpredictive map263 is the same as one of the in-situ data type sensed by the in-situ sensors208 or the data type in theprior information map258, and different than the other.
• Continuing with the preceding example in whichprior information map258 is a vegetative index map and in-situ sensor208 senses a value indicative of biomass,predictive map generator212 uses the vegetative index values inprior information map258 and the model generated bypredictive model generator210 to generate a functionalpredictive map263 that predicts the biomass at different locations in the field.Predictive map generator212 thus outputspredictive map264.
• As shown inFIG. 2,predictive map264 predicts the value of a sensed characteristic (sensed by in-situ sensors208), or a characteristic related to the sensed characteristic, at various locations across the field based upon a prior information value inprior information map258 at those locations and using the predictive model. For example, ifpredictive model generator210 has generated a predictive model indicative of a relationship between a vegetative index value and biomass, then, given the vegetative index value at different locations across the field,predictive map generator212 generates apredictive map264 that predicts the value of the biomass at different locations across the field. The vegetative index value, obtained from the vegetative index map, at those locations and the relationship between the vegetative index value and biomass, obtained from the predictive model, are used to generate thepredictive map264.
• Some variations in the data types that are mapped in theprior information map258, the data types sensed by in-situ sensors208, and the data types predicted on thepredictive map264 will now be described.
• In some examples, the data type in theprior information map258 is different from the data type sensed by in-situ sensors208, yet the data type in thepredictive map264 is the same as the data type sensed by the in-situ sensors208. For instance, theprior information map258 may be a vegetative index map, and the variable sensed by the in-situ sensors208 may be vegetation height. Thepredictive map264 may then be a predictive vegetation height map that maps predicted vegetation height values to different geographic locations in the field. In another example, theprior information map258 may be a vegetative index map, and the variable sensed by the in-situ sensors208 may be vegetation density. Thepredictive map264 may then be a predictive vegetation density map that maps predicted vegetation density values to different geographic locations in the field.
• Also, in some examples, the data type in theprior information map258 is different from the data type sensed by in-situ sensors208, and the data type in thepredictive map264 is different from both the data type in theprior information map258 and the data type sensed by the in-situ sensors208. For instance, theprior information map258 may be a vegetative index map, and the variable sensed by the in-situ sensors208 may be crop height. In such an example, thepredictive map264 may be a predictive biomass map that maps predicted biomass values to different geographic locations in the field. In another example, theprior information map258 may be a vegetative index map, and the variable sensed by the in-situ sensors208 may be threshing rotor drive force. In such an example, thepredictive map264 may be a predictive biomass map that maps predicted biomass values to different geographic locations in the field.
• In some examples, theprior information map258 is from a prior pass through the field during a prior operation and the data type is different from the data type sensed by in-situ sensors208, yet the data type in thepredictive map264 is the same as the data type sensed by the in-situ sensors208. For instance, theprior information map258 may be a seed population map generated during planting, and the variable sensed by the in-situ sensors208 may be stalk size. Thepredictive map264 may then be a predictive stalk size map that maps predicted stalk size values to different geographic locations in the field. In another example, theprior information map258 may be a seeding hybrid map, and the variable sensed by the in-situ sensors208 may be crop state such as standing crop or down crop. Thepredictive map264 may then be a predictive crop state map that maps predicted crop state values to different geographic locations in the field.
• In some examples, theprior information map258 is from a prior pass through the field during a prior operation and the data type is the same as the data type sensed by in-situ sensors208, and the data type in thepredictive map264 is also the same as the data type sensed by the in-situ sensors208. For instance, theprior information map258 may be a yield map generated during a previous year, and the variable sensed by the in-situ sensors208 may be yield. Thepredictive map264 may then be a predictive yield map that maps predicted yield values to different geographic locations in the field. In such an example, the relative yield differences in the georeferencedprior information map258 from the prior year can be used bypredictive model generator210 to generate a predictive model that models a relationship between the relative yield differences on theprior information map258 and the yield values sensed by in-situ sensors208 during the current harvesting operation. The predictive model is then used bypredictive map generator210 to generate a predictive yield map.
• In another example, theprior information map258 may be a weed intensity map generated during a prior operation, such as from a sprayer, and the variable sensed by the in-situ sensors208 may be weed intensity. Thepredictive map264 may then be a predictive weed intensity map that maps predicted weed intensity values to different geographic locations in the field. In such an example, a map of the weed intensities at time of spraying is geo-referenced recorded and provided toagricultural harvester100 as aprior information map258 of weed intensity. In-situ sensors208 can detect weed intensity at geographic locations in the field andpredictive model generator210 may then build a predictive model that models a relationship between weed intensity at time of harvest and weed intensity at time of spraying. This is because the sprayer will have impacted the weed intensity at time of spraying, but weeds may still crop up in similar areas again by harvest. However, the weed areas at harvest are likely to have different intensity based on timing of the harvest, weather, weed type, among other things.
• In some examples,predictive map264 can be provided to thecontrol zone generator213.Control zone generator213 groups adjacent portions of an area into one or more control zones based on data values ofpredictive map264 that are associated with those adjacent portions. A control zone may include two or more contiguous portions of an area, such as a field, for which a control parameter corresponding to the control zone for controlling a controllable subsystem is constant. For example, a response time to alter a setting ofcontrollable subsystems216 may be inadequate to satisfactorily respond to changes in values contained in a map, such aspredictive map264. In that case,control zone generator213 parses the map and identifies control zones that are of a defined size to accommodate the response time of thecontrollable subsystems216. In another example, control zones may be sized to reduce wear from excessive actuator movement resulting from continuous adjustment. In some examples, there may be a different set of control zones for eachcontrollable subsystem216 or for groups ofcontrollable subsystems216. The control zones may be added to thepredictive map264 to obtain predictivecontrol zone map265. Predictivecontrol zone map265 can thus be similar topredictive map264 except that predictivecontrol zone map265 includes control zone information defining the control zones. Thus, a functionalpredictive map263, as described herein, may or may not include control zones. Bothpredictive map264 and predictivecontrol zone map265 are functionalpredictive maps263. In one example, a functionalpredictive map263 does not include control zones, such aspredictive map264. In another example, a functionalpredictive map263 does include control zones, such as predictivecontrol zone map265. In some examples, multiple crops may be simultaneously present in a field if an intercrop production system is implemented. In that case,predictive map generator212 andcontrol zone generator213 are able to identify the location and characteristics of the two or more crops and then generatepredictive map264 and predictivecontrol zone map265 accordingly.
• It will also be appreciated thatcontrol zone generator213 can cluster values to generate control zones and the control zones can be added to predictivecontrol zone map265, or a separate map, showing only the control zones that are generated. In some examples, the control zones may be used for controlling or calibratingagricultural harvester100 or both. In other examples, the control zones may be presented to theoperator260 and used to control or calibrateagricultural harvester100, and, in other examples, the control zones may be presented to theoperator260 or another user or stored for later use.
• Predictive map264 or predictivecontrol zone map265 or both are provided to controlsystem214, which generates control signals based upon thepredictive map264 or predictivecontrol zone map265 or both. In some examples,communication system controller229 controlscommunication system206 to communicate thepredictive map264 or predictivecontrol zone map265 or control signals based on thepredictive map264 or predictivecontrol zone map265 to other agricultural harvesters that are harvesting in the same field. In some examples,communication system controller229 controls thecommunication system206 to send thepredictive map264, predictivecontrol zone map265, or both to other remote systems.
• Operator interface controller231 is operable to generate control signals to controloperator interface mechanisms218. Theoperator interface controller231 is also operable to present thepredictive map264 or predictivecontrol zone map265 or other information derived from or based on thepredictive map264, predictivecontrol zone map265, or both tooperator260.Operator260 may be a local operator or a remote operator. As an example,controller231 generates control signals to control a display mechanism to display one or both ofpredictive map264 and predictivecontrol zone map265 for theoperator260.Controller231 may generate operator actuatable mechanisms that are displayed and can be actuated by the operator to interact with the displayed map. The operator can edit the map by, for example, correcting a biomass displayed on the map, based on the operator's observation.Settings controller232 can generate control signals to control various settings on theagricultural harvester100 based uponpredictive map264, the predictivecontrol zone map265, or both. For instance,settings controller232 can generate control signals to control machine andheader actuators248. In response to the generated control signals, the machine andheader actuators248 operate to control, for example, one or more of the sieve and chaffer settings, concave clearance, rotor settings, cleaning fan speed settings, header height, header functionality, reel speed, reel position, draper functionality (whereagricultural harvester100 is coupled to a draper header), corn header functionality, internal distribution control andother actuators248 that affect the other functions of theagricultural harvester100.Path planning controller234 illustratively generates control signals to controlsteering subsystem252 to steeragricultural harvester100 according to a desired path.Path planning controller234 can control a path planning system to generate a route foragricultural harvester100 and can controlpropulsion subsystem250 andsteering subsystem252 to steeragricultural harvester100 along that route.Feed rate controller236 can control various subsystems, such aspropulsion subsystem250 andmachine actuators248, to control a feed rate (throughput) based upon thepredictive map264 or predictivecontrol zone map265 or both. For instance, asagricultural harvester100 approaches an upcoming area of crop on the field having a biomass value above a selected threshold, feedrate controller236 may reduce the speed ofmachine100 to maintain constant feed rate of biomass through the machine. Header andreel controller238 can generate control signals to control a header or a reel or other header functionality.Draper belt controller240 can generate control signals to control a draper belt or other draper functionality based upon thepredictive map264, predictivecontrol zone map265, or both. Deckplate position controller242 can generate control signals to control a position of a deck plate included on a header based onpredictive map264 or predictivecontrol zone map265 or both, andresidue system controller244 can generate control signals to control aresidue subsystem138 based uponpredictive map264 or predictivecontrol zone map265, or both.Machine cleaning controller245 can generate control signals to controlmachine cleaning subsystem254. For instance, based upon the different types of seeds or weeds passed throughmachine100, a particular type of machine cleaning operation or a frequency with which a cleaning operation is performed may be controlled. Other controllers included on theagricultural harvester100 can control other subsystems based on thepredictive map264 or predictivecontrol zone map265 or both as well.
• FIGS. 3A and 3B (collectively referred to herein asFIG. 3) show a flow diagram illustrating one example of the operation ofagricultural harvester100 in generating apredictive map264 and predictivecontrol zone map265 based uponprior information map258.
• At280,agricultural harvester100 receivesprior information map258. Examples ofprior information map258 or receivingprior information map258 are discussed with respect toblocks281,282,284 and286. As discussed above,prior information map258 maps values of a variable, corresponding to a first characteristic, to different locations in the field, as indicated atblock282. As indicated at block281, receiving theprior information map258 may involve selecting one or more of a plurality of possible prior information maps that are available. For instance, one prior information map may be a vegetative index map generated from aerial imagery. Another prior information map may be a map generated during a prior pass through the field which may have been performed by a different machine performing a previous operation in the field, such as a sprayer or other machine. The process by which one or more prior information maps are selected can be manual, semi-automated, or automated. Theprior information map258 is based on data collected prior to a current harvesting operation. This is indicated by block284. For instance, the data may be collected based on aerial images taken during a previous year, or earlier in the current growing season, or at other times.
• The data used in the generation ofprior information map258 may be obtained in ways other than aerial imaging. For instance,agricultural harvester100 may be fitted with a sensor, such as a perception sensor (e.g., forward looking image capture mechanism151), that identifies vegetation characteristics such as vegetation height, vegetation density, vegetation mass, or vegetation volume, during a prior operation. In other instances, other vegetation characteristics may be identified and used. In another example,agricultural harvester100 may be fitted with a sensor that senses a force, or an indication of force, used to drive threshingrotor112, such as a pressure sensor that senses the fluid pressure used to drive threshingrotor112 or a torque sensor that senses a torque used to drive threshingrotor112, as the threshingrotor112 processes crops harvested byagricultural harvester100 during a prior operation. The data detected by the sensors during a previous year's harvest may be used as data to generate theprior information map258. The sensed data may be combined with other data to generate theprior information map258. For example, based upon a vegetation height, vegetation density, vegetation mass, or vegetation volume of the vegetation being harvested or encountered byagricultural harvester100 at different locations in the field, and based upon other factors, such as vegetation type; the weather conditions, such as the weather conditions during the vegetation's growth; or soil characteristics, such as moisture, the biomass can be predicted so that theprior information map258 maps the predicted biomass in the field. The data for theprior information map258 can be transmitted toagricultural harvester100 usingcommunication system206 and stored indata store202. The data for theprior information map258 can be provided toagricultural harvester100 usingcommunication system206 in other ways as well, and this is indicated byblock286 in the flow diagram ofFIG. 3. In some examples, theprior information map258 can be received bycommunication system206.
• Upon commencement of a harvesting operation, in-situ sensors208 generate sensor signals indicative of one or more in-situ data values indicative of a characteristic, for example, a vegetation characteristic, such as biomass or a biomass characteristic, as indicated by block288. Examples of in-situ sensors208 are discussed with respect toblocks222,290, and226. As explained above, the in-situ sensors208 include on-board sensors222; remote in-situ sensors224, such as UAV-based sensors flown at a time to gather in-situ data, shown inblock290; or other types of in-situ sensors, designated by in-situ sensors226. In some examples, data from on-board sensors is georeferenced using position, heading, or speed data fromgeographic position sensor204.
• Predictive model generator210 controls the prior information variable-to-in-situvariable model generator228 to generate a model that models a relationship between the mapped values contained in theprior information map258 and the in-situ values sensed by the in-situ sensors208 as indicated by block292. The characteristics or data types represented by the mapped values in theprior information map258 and the in-situ values sensed by the in-situ sensors208 may be the same characteristics or data type or different characteristics or data types.
• The relationship or model generated bypredictive model generator210 is provided topredictive map generator212.Predictive map generator212 generates apredictive map264 that predicts a value of the characteristic sensed by the in-situ sensors208 at different geographic locations in a field being harvested, or a different characteristic that is related to the characteristic sensed by the in-situ sensors208, using the predictive model and theprior information map258, as indicated by block294.
• It should be noted that, in some examples, theprior information map258 may include two or more different maps or two or more different map layers of a single map. Each map layer may represent a different data type from the data type of another map layer or the map layers may have the same data type that were obtained at different times. Each map in the two or more different maps or each layer in the two or more different map layers of a map maps a different type of variable to the geographic locations in the field. In such an example,predictive model generator210 generates a predictive model that models the relationship between the in-situ data and each of the different variables mapped by the two or more different maps or the two or more different map layers. Similarly, the in-situ sensors208 can include two or more sensors each sensing a different type of variable. Thus, thepredictive model generator210 generates a predictive model that models the relationships between each type of variable mapped by theprior information map258 and each type of variable sensed by the in-situ sensors208.Predictive map generator212 can generate a functionalpredictive map263 that predicts a value for each sensed characteristic sensed by the in-situ sensors208 (or a characteristic related to the sensed characteristic) at different locations in the field being harvested using the predictive model and each of the maps or map layers in theprior information map258.
• Predictive map generator212 configures thepredictive map264 so that thepredictive map264 is actionable (or consumable) bycontrol system214.Predictive map generator212 can provide thepredictive map264 to thecontrol system214 or to controlzone generator213 or both. Some examples of different ways in which thepredictive map264 can be configured or output are described with respect toblocks296,295,299 and297. For instance,predictive map generator212 configurespredictive map264 so thatpredictive map264 includes values that can be read bycontrol system214 and used as the basis for generating control signals for one or more of the different controllable subsystems of theagricultural harvester100, as indicated by block296.
• Control zone generator213 can divide thepredictive map264 into control zones based on the values on thepredictive map264. Contiguously-geolocated values that are within a threshold value of one another can be grouped into a control zone. The threshold value can be a default threshold value, or the threshold value can be set based on an operator input, based on an input from an automated system, or based on other criteria. A size of the zones may be based on a responsiveness of thecontrol system214, thecontrollable subsystems216, based on wear considerations, or on other criteria as indicated byblock295.Predictive map generator212 configurespredictive map264 for presentation to an operator or other user.Control zone generator213 can configure predictivecontrol zone map265 for presentation to an operator or other user. This is indicated by block299. When presented to an operator or other user, the presentation of thepredictive map264 or predictivecontrol zone map265 or both may contain one or more of the predictive values on thepredictive map264 correlated to geographic location, the control zones on predictivecontrol zone map265 correlated to geographic location, and settings values or control parameters that are used based on the predicted values onpredictive map264 or zones on predictivecontrol zone map265. The presentation can, in another example, include more abstracted information or more detailed information. The presentation can also include a confidence level that indicates an accuracy with which the predictive values onpredictive map264 or the zones on predictivecontrol zone map265 conform to measured values that may be measured by sensors onagricultural harvester100 asagricultural harvester100 moves through the field. Further where information is presented to more than one location, an authentication and authorization system can be provided to implement authentication and authorization processes. For instance, there may be a hierarchy of individuals that are authorized to view and change maps and other presented information. By way of example, an on-board display device may show the maps in near real time locally on the machine, or the maps may also be generated at one or more remote locations, or both. In some examples, each physical display device at each location may be associated with a person or a user permission level. The user permission level may be used to determine which display markers are visible on the physical display device and which values the corresponding person may change. As an example, a local operator ofagricultural harvester100 may be unable to see the information corresponding to thepredictive map264 or make any changes to machine operation. A supervisor, such as a supervisor at a remote location, however, may be able to see thepredictive map264 on the display but be prevented from making any changes. A manager, who may be at a separate remote location, may be able to see all of the elements onpredictive map264 and also be able to change thepredictive map264. In some instances, thepredictive map264 accessible and changeable by a manager located remotely may be used in machine control. This is one example of an authorization hierarchy that may be implemented. Thepredictive map264 or predictivecontrol zone map265 or both can be configured in other ways as well, as indicated byblock297.
• Atblock298, input fromgeographic position sensor204 and other in-situ sensors208 are received by the control system. Particularly, atblock300,control system214 detects an input from thegeographic position sensor204 identifying a geographic location ofagricultural harvester100.Block302 represents receipt by thecontrol system214 of sensor inputs indicative of trajectory or heading ofagricultural harvester100, and block304 represents receipt by thecontrol system214 of a speed ofagricultural harvester100.Block306 represents receipt by thecontrol system214 of other information from various in-situ sensors208.
• Atblock308,control system214 generates control signals to control thecontrollable subsystems216 based on thepredictive map264 or predictivecontrol zone map265 or both and the input from thegeographic position sensor204 and any other in-situ sensors208. Atblock310,control system214 applies the control signals to the controllable subsystems. It will be appreciated that the particular control signals that are generated, and the particularcontrollable subsystems216 that are controlled, may vary based upon one or more different things. For example, the control signals that are generated and thecontrollable subsystems216 that are controlled may be based on the type ofpredictive map264 or predictivecontrol zone map265 or both that is being used. Similarly, the control signals that are generated and thecontrollable subsystems216 that are controlled and the timing of the control signals can be based on various latencies of crop flow through theagricultural harvester100 and the responsiveness of thecontrollable subsystems216.
• By way of example, a generatedpredictive map264 in the form of a predictive biomass map can be used to control one ormore subsystems216. For instance, the predictive biomass map can include biomass or biomass characteristic values georeferenced to locations within the field being harvested. The biomass or biomass characteristic values from the predictive biomass map can be extracted and used to control, for example, the steering andpropulsion subsystems252 and250. By controlling the steering andpropulsion subsystems252 and250, a feed rate of material moving through theagricultural harvester100 can be controlled. Similarly, the header height can be controlled to take in more or less material, and, thus, the header height can also be controlled to control feed rate of material through theagricultural harvester100. In other examples, if thepredictive map264 maps a biomass forward ofagricultural harvester100 being greater along one portion ofheader102 than another portion ofheader102, resulting in a different biomass entering one side ofheader102 than the other side ofheader102, control ofheader102 may be implemented. For example, a draper speed on one side ofheader102 may be increased or decreased relative to the draper speed on the other side ofheader102 to account for the difference in biomass. Thus,draper belt controller240 can be used, based on georeferenced values present in the predictive biomass map, to control draper speeds of the draper belts onheader102. The preceding example involving biomass and using a predictive biomass map is provided merely as an example. Consequently, a wide variety of other control signals can be generated using values obtained from a predictive biomass map or other type of predictive map to control one or more of thecontrollable subsystems216.
• Atblock312, a determination is made as to whether the harvesting operation has been completed. If harvesting is not completed, the processing advances to block314 where in-situ sensor data fromgeographic position sensor204 and in-situ sensors208 (and perhaps other sensors) continue to be read.
• In some examples, atblock316,agricultural harvester100 can also detect learning trigger criteria to perform machine learning on one or more of thepredictive map264, predictivecontrol zone map265, the model generated bypredictive model generator210, the zones generated bycontrol zone generator213, one or more control algorithms implemented by the controllers in thecontrol system214, and other triggered learning.
• The learning trigger criteria can include any of a wide variety of different criteria. Some examples of detecting trigger criteria are discussed with respect toblocks318,320,321,322 and324. For instance, in some examples, triggered learning can involve recreation of a relationship used to generate a predictive model when a threshold amount of in-situ sensor data are obtained from in-situ sensors208. In such examples, receipt of an amount of in-situ sensor data from the in-situ sensors208 that exceeds a threshold triggers or causes thepredictive model generator210 to generate a new predictive model that is used bypredictive map generator212. Thus, asagricultural harvester100 continues a harvesting operation, receipt of the threshold amount of in-situ sensor data from the in-situ sensors208 triggers the creation of a new relationship represented by a predictive model generated bypredictive model generator210. Further, newpredictive map264, predictivecontrol zone map265, or both can be regenerated using the new predictive model.Block318 represents detecting a threshold amount of in-situ sensor data used to trigger creation of a new predictive model.
• In other examples, the learning trigger criteria may be based on how much the in-situ sensor data from the in-situ sensors208 are changing, such as over time or compared to previous values. For example, if variations within the in-situ sensor data (or the relationship between the in-situ sensor data and the information in prior information map258) are within a selected range or is less than a defined amount, or below a threshold value, then a new predictive model is not generated by thepredictive model generator210. As a result, thepredictive map generator212 does not generate a newpredictive map264, predictivecontrol zone map265, or both. However, if variations within the in-situ sensor data are outside of the selected range, are greater than the defined amount, or are above the threshold value, for example, then thepredictive model generator210 generates a new predictive model using all or a portion of the newly received in-situ sensor data that thepredictive map generator212 uses to generate a newpredictive map264. Atblock320, variations in the in-situ sensor data, such as a magnitude of an amount by which the data exceeds the selected range or a magnitude of the variation of the relationship between the in-situ sensor data and the information in theprior information map258, can be used as a trigger to cause generation of a new predictive model and predictive map. Keeping with the examples described above, the threshold, the range, and the defined amount can be set to default values; set by an operator or user interaction through a user interface; set by an automated system; or set in other ways.
• Other learning trigger criteria can also be used. For instance, ifpredictive model generator210 switches to a different prior information map (different from the originally selected prior information map258), then switching to the different prior information map may trigger relearning bypredictive model generator210,predictive map generator212,control zone generator213,control system214, or other items. In another example, transitioning ofagricultural harvester100 to a different topography or to a different control zone may be used as learning trigger criteria as well.
• In some instances,operator260 can also edit thepredictive map264 or predictivecontrol zone map265 or both. The edits can change a value on thepredictive map264, change a size, shape, position, or existence of a control zone on predictivecontrol zone map265, or both.Block321 shows that edited information can be used as learning trigger criteria.
• In some instances, it may also be thatoperator260 observes that automated control of a controllable subsystem, is not what the operator desires. In such instances, theoperator260 may provide a manual adjustment to the controllable subsystem reflecting that theoperator260 desires the controllable subsystem to operate in a different way than is being commanded bycontrol system214. Thus, manual alteration of a setting by theoperator260 can cause one or more ofpredictive model generator210 to relearn a model,predictive map generator212 to regeneratemap264,control zone generator213 to regenerate one or more control zones on predictivecontrol zone map265, andcontrol system214 to relearn a control algorithm or to perform machine learning on one or more of thecontroller components232 through246 incontrol system214 based upon the adjustment by theoperator260, as shown in block322.Block324 represents the use of other triggered learning criteria.
• In other examples, relearning may be performed periodically or intermittently based, for example, upon a selected time interval such as a discrete time interval or a variable time interval, as indicated byblock326.
• If relearning is triggered, whether based upon learning trigger criteria or based upon passage of a time interval, as indicated byblock326, then one or more of thepredictive model generator210,predictive map generator212,control zone generator213, andcontrol system214 performs machine learning to generate a new predictive model, a new predictive map, a new control zone, and a new control algorithm, respectively, based upon the learning trigger criteria. The new predictive model, the new predictive map, and the new control algorithm are generated using any additional data that has been collected since the last learning operation was performed. Performing relearning is indicated by block328.
• If the harvesting operation has been completed, operation moves fromblock312 to block330 where one or more of thepredictive map264, predictivecontrol zone map265, and predictive model generated bypredictive model generator210 are stored. Thepredictive map264, predictivecontrol zone map265, and predictive model may be stored locally ondata store202 or sent to a remote system usingcommunication system206 for later use.
• It will be noted that, while some examples herein describepredictive model generator210 andpredictive map generator212 receiving a prior information map in generating a predictive model and a functional predictive map, respectively, in other examples, thepredictive model generator210 andpredictive map generator212 can receive, in generating a predictive model and a functional predictive map, respectively other types of maps, including predictive maps, such as a functional predictive map generated during the harvesting operation.
• FIG. 4 is a block diagram of a portion of theagricultural harvester100 shown inFIG. 1. Particularly,FIG. 4 shows, among other things, examples of thepredictive model generator210 and thepredictive map generator212 in more detail.FIG. 4 also illustrates information flow among the various components shown. Thepredictive model generator210 receives avegetative index map332 as a prior information map.Predictive model generator210 also receives ageographic location334, or an indication of a geographic location, fromgeographic position sensor204. In-situ sensors208 illustratively include a biomass sensor, such asbiomass sensor336, as well as aprocessing system338. In some instances,biomass sensor336 may be located on-board of theagricultural harvester100. Theprocessing system338 processes sensor data generated from on-board biomass sensor336 to generate processed data, some examples of which are described below.
• In some examples,biomass sensor336 may be an optical sensor, such as a camera, a stereo camera, a mono camera, lidar, or radar, that generates images of an area of a field to be harvested. In some instances, the optical sensor may be arranged on theagricultural harvester100, or a header attached to theagricultural harvester100, to collect images of an area adjacent to theagricultural harvester100, such as in an area that lies in front of, to the side of, rearwardly of, or in another direction relative to theagricultural harvester100 asagricultural harvester100 moves through the field during a harvesting operation. The optical sensor may also be located on or inside of theagricultural harvester100 to obtain images of one or more portions of the exterior or interior of theagricultural harvester100.Processing system338 processes one or more images obtained via thebiomass sensor336 to generate processed image data identifying one or more characteristics of crops in the image. Vegetation characteristics detected by theprocessing system338 may include a height of vegetation present in the image, a volume of vegetation in an image, a mass of vegetation present in the image, or a density of crop in the image. In another example,biomass sensor336 may be a force sensor that generates sensor signals indicative of a force, such as a fluid pressure or a torque, used to drive threshingrotor112 ofagricultural harvester100 to indicate a biomass being processed byagricultural harvester100 during the course of a harvesting operation.
• In-situ sensor208 may be or include other types of sensors, such as a camera located along a path by which severed vegetation material travels in agricultural harvester100 (referred to hereinafter as “process camera”). A process camera may be located internal to theagricultural harvester100 and may capture images of vegetation material as the vegetation material moves through or is expelled from theagricultural harvester100. For instance, a process camera may be configured to detect vegetation material coming through the feeder house ofagricultural harvester100. Process cameras may obtain images of severed vegetation material, andimage processing system338 is operable to detect the biomass or biomass characteristics of the vegetation material as it moves through or is expelled fromagricultural harvester100. In other examples, in-situ sensor208 may include a material distribution sensor that measures the volume or mass of material at two or more locations. The measurements may be absolute or relative. In some examples, electromagnetic or ultrasonic sensors may be used to measure time of flight, phase shift, or binocular disparities of one or more signals reflected by material surfaces at distances relative to a reference surface. In other examples, emitted signal or subatomic particle backscatter, absorption, attenuation, or transmission may be used to measure the material distribution. In other example, material properties, such as electrical permittivity, may be used to measure the distribution. Other approaches may be used as well. It will be noted that these are merely some examples of in-situ sensor208 orbiomass sensor336, or both, and that various other sensors may be used.
• In other examples,biomass sensor336 can rely on wavelength(s) of electromagnetic energy, and the way the electromagnetic energy is reflected by, absorbed by, attenuated by, or transmitted through vegetation. Thebiomass sensor336 may sense other electromagnetic properties of vegetation, such as electrical permittivity, when the severed vegetation material passes between two capacitive plates. Thebiomass sensor336 may also rely on mechanical properties of vegetation, such as a signal generated when a portion of the vegetation (e.g., grain) impacts a piezoelectric sheet or when an impact by a portion of the vegetation is detected by a microphone or accelerometer. Other material properties and sensors may also be used. In some examples,biomass sensor336 may be an ultrasonic sensor, a capacitive sensor, an electrical permittivity sensor, or a mechanical sensor, that senses vegetation inside or outside ofagricultural harvester100. In some examples,biomass sensor336 may be a light attenuation sensor or a reflectance sensor. In some examples, raw or processed data frombiomass sensor336 may be presented tooperator260 viaoperator interface mechanism218.Operator260 may be on-board theagricultural harvester100 or at a remote location. Theprocessing system338 is operable to detect the biomass being harvested byagricultural harvester100, as well as various biomass characteristics of the vegetation, such as vegetation height, vegetation volume, vegetation mass, or vegetation density, corresponding to the vegetation being encountered by theagricultural harvester100 during the course of a harvesting operation.
• The present discussion proceeds with respect to an example in whichbiomass sensor336 senses biomass or a biomass characteristic, such as an optical sensor that generates an image indicative of biomass or a biomass characteristic, or in which thebiomass sensor336 is a force sensor, such as a pressure sensor or torque sensor, that senses a force used to drive threshingrotor112 as an indication of biomass. It will be appreciated that these are just some examples, and the sensors mentioned above, as well as other examples ofbiomass sensor336, are contemplated herein as well. As shown inFIG. 4, the examplepredictive model generator210 includes one or more of a vegetation height-to-vegetativeindex model generator342, a vegetation density-to-vegetativeindex model generator344, a vegetation mass-to-vegetativeindex model generator345, a vegetation volume-to-vegetative index model generator346, and a threshing rotor drive force-to-vegetativeindex model generator347. In other examples, thepredictive model generator210 may include additional, fewer, or different components than those shown in the example ofFIG. 4. Consequently, in some examples, thepredictive model generator210 may includeother items348 as well, which may include other types of predictive model generators to generate other types of vegetation characteristic models, for example, other characteristics indicative of biomass-to-vegetative index model generators. In some examples, themodel generators342,344,345, and346 may also include, as the vegetation characteristic, crop characteristics, such as crop height, crop density, crop mass, and crop volume. In other examples, predictive model generator may include one or more of a crop height-to-vegetative index model generator, a crop density-to-vegetative index model generator, a crop mass-to-vegetative index model generator, or a crop volume-to-vegetive index model generator.
• Vegetation height-to-vegetativeindex model generator342 identifies a relationship between vegetation height detected in processeddata340, at a geographic location corresponding to the processeddata340, and vegetative index values from thevegetative index map332 corresponding to the same location in the field where the vegetation height corresponds. Based on the relationship established by vegetation height-to-vegetativeindex model generator342, vegetation height-to-vegetativeindex model generator342 generates a predictive biomass model. The predictive biomass model is used by vegetationheight map generator352 to predict, at any given location in the field, vegetation height at that location in the field, based upon a georeferenced vegetative index value contained in thevegetative index map332, corresponding to that location in the field.
• Vegetation density-to-vegetativeindex model generator344 identifies a relationship between a vegetation density level represented in the processeddata340, at a geographic location corresponding to the processeddata340, and the vegetative index value corresponding to the same geographic location. Again, the vegetative index value is the georeferenced value contained in thevegetative index map332. Based on the relationship established by vegetation density-to-vegetativeindex model generator344, vegetation density-to-vegetativeindex model generator344 generates a predictive biomass model. The predictive biomass model is used by vegetation density map generator354 to predict, at any given location in the field, the vegetation density at that location in the field, based upon a georeferenced vegetative index value contained in thevegetative index map332, corresponding to that location in the field.
• Vegetation mass-to-vegetativeindex model generator345 identifies a relationship between the vegetation mass represented in the processeddata340, at a geographic location in the field corresponding to the processeddata340, and the vegetative index value from thevegetative index map332 corresponding to the same location. Based on the relationship established by vegetation mass-to-vegetativeindex model generator345, vegetation mass-to-vegetativeindex model generator345 generates a predictive biomass model. The predictive biomass model is used by vegetation mass map generator355 to predict, at any given location in the field, vegetation mass at that location in the field, based upon a georeferenced vegetative index value contained in thevegetative index map332, corresponding to that location in the field.
• Vegetation volume-to-vegetative index model generator346 identifies a relationship between the vegetation volume represented in the processeddata340, at a geographic location in the field corresponding to the processeddata340, and the vegetative index value from thevegetative index map332 corresponding to that same location. Based on the relationship established by vegetation volume-to-vegetative index model generator346, vegetation volume-to-vegetative index model generator346 generates a predictive biomass model. The predictive biomass model is used by vegetationvolume map generator356 to predict, at any given location in the field, the vegetation volume at that location in the field, based upon a georeferenced vegetative index value contained in thevegetative index map332, corresponding to that location in the field.
• Threshing rotor drive force-to-vegetativeindex model generator347 identifies a relationship between the threshing rotor drive force represented in the processeddata340, at a geographic location in the field corresponding to the processed data, and the vegetative index value from thevegetative index map332 corresponding to that same location. Based on the relationship established by threshing rotor drive force-to-vegetativeindex model generator347, threshing rotor drive force-to-vegetativeindex model generator347 generates a predictive biomass model. The predictive biomass model is used by threshing rotor driveforce map generator357 to predict, at any given location in the field, the threshing rotor drive force at that location in the field, based upon a georeferenced vegetative index value contained in thevegetative index map332, corresponding to that location in the field.
• In light of the above, thepredictive model generator210 is operable to produce a plurality of predictive biomass models, such as one or more of the predictive biomass models generated bymodel generators342,344,345,346,347, and348. In another example, two or more of the predictive biomass models described above may be combined into a single predictive biomass model that predicts two or more biomass characteristics, such as vegetation height (e.g., crop height, weed height, etc.), vegetation density (e.g., crop density, weed density, etc.), vegetation mass (e.g., crop mass, weed mass, etc.), vegetation volume (e.g., crop volume, weed volume, etc.), or threshing rotor drive force, based upon the vegetative index value at different locations in the field. Any of these biomass models, or combinations thereof, are represented collectively bypredictive biomass model350 inFIG. 4.
• Thepredictive biomass model350 is provided topredictive map generator212. In the example ofFIG. 4,predictive map generator212 includes a vegetationheight map generator352, a vegetation density map generator354, a vegetation mass map generator355, a vegetationvolume map generator356, and a threshing rotor driveforce map generator357. In other examples, thepredictive map generator212 may include additional, fewer, or different map generators. Thus, in some examples, thepredictive map generator212 may includeother items358 which may include other types of map generators to generate biomass maps for other types of characteristics. For example,predictive map generator212 may include one or more of a crop height map generator, a crop density map generator, a crop mass map generator, or a crop volume generator. Additionally, in other examples,map generators352,354,355, or356 may map, as vegetation characteristics, crop characteristics such as crop height, crop density, crop mass, or crop volume. Vegetationheight map generator352 receives thepredictive biomass model350 and generates a predictive map that predicts the vegetation height at different locations in the field, based on thepredictive biomass model350 and based on the vegetative index values contained in thevegetative index map332 at those locations in the field.
• Vegetation density map generator354 receives thepredictive biomass model350 and generates a predictive map that predicts the vegetation density at different locations in the field based upon thepredictive biomass model350 and the vegetative index values, contained in thevegetative index map332, at those locations in the field. Vegetation mass map generator355 receives thepredictive biomass model350 and generates a predictive map that predicts vegetation mass at different locations in the field based upon thepredictive biomass model350 and based on the vegetative index value contained in thevegetative index map332 at those locations in the field. Vegetationvolume map generator356 receives thepredictive biomass model350 and generates a predictive map that predicts vegetation volume at different locations in the field based upon thepredictive biomass model350 and based on the vegetative index values contained in thevegetative index map332 at those locations in the field. Threshing rotor driveforce map generator357 receives thepredictive biomass model350 and generates a predictive map that predicts threshing rotor drive force at different locations in the field based upon thepredictive biomass model350 and based on the vegetative index values contained in thevegetative index map332 at those locations in the field.Other map generator358 can generate a predictive map that predicts other characteristics, such as crop characteristics, for instance, crop height, crop density, crop mass, or crop volume, at different locations in the field based upon the vegetative index values at those locations in the field and thepredictive biomass model350.
• Predictive map generator212 outputs one or more predictive biomass maps360 that are predictive of biomass or biomass characteristic values at different geographic locations across the field. In one example, the one or more predictive biomass maps360 predicts one or more of vegetation height, vegetation density, vegetation mass, vegetation volume, or threshing rotor drive force. In another example, the one or more predictive biomass maps360 predicts one or more of crop height, crop density, crop mass, or crop volume. In other examples, vegetation height, vegetation density, vegetation mass, or vegetation volume may include indications of crop height, crop density, crop mass, or crop volume, respectively. Each of the predictive biomass maps360 predicts the respective characteristic at different locations in a field. Each of the generated predictive biomass maps360 may be provided to controlzone generator213,control system214, or both.Control zone generator213 generates control zones and incorporates those control zones into the functional predictive map, i.e., functionalpredictive biomass map360 to provide functionalpredictive biomass map360 with control zones.predictive map264 The functional predictive map360 (with or without control zones) may be provided to controlsystem214, which generates control signals to control one or more of thecontrollable subsystems216 based upon the functional predictive map360 (with or without control zones).
• FIG. 5 is a flow diagram of an example of operation ofpredictive model generator210 andpredictive map generator212 in generating thepredictive biomass model350 and thepredictive biomass map360, respectively. Atblock362,predictive model generator210 andpredictive map generator212 receive a priorvegetative index map332. At block364,processing system338 receives one or more sensor signals from in-situ sensors208, such asbiomass sensor336. As discussed above, the in-situ sensor208, such asbiomass sensor336, may be anoptical sensor368, such as a camera (e.g., a forward looking camera), lidar, radar, or another optical sensing device looking internally to or externally of a combine harvester; a threshing rotordrive force sensor369, such as a pressure sensor that senses a fluid pressure used to drive the threshing rotor or a torque sensor that senses a torque used to drive threshing rotor. Still further, other types of in-situ sensors, such as another type of biomass sensor, as indicated byblock370, are within the scope of the present disclosure.
• Atblock372,processing system338 processes the one or more received sensor signals to generate sensor data indicative of a characteristic of biomass sensed by the in-situ sensor208, such asbiomass sensor336. Atblock374, the sensor data may be indicative of vegetation height, such as crop height, that may exist at a location, such as at a location in front of a combine harvester. In some instances, as indicated atblock376, the sensor data may be indicative of density of vegetation, such as a density of crops in front ofagricultural harvester100. In some instances, as indicated byblock377, the sensor data may be indicative of vegetation mass, such as a mass of the crop or a crop component, being processed byagricultural harvester100. A crop component can include parts of the crop plant that comprise less than the entirety of the crop plant, for example, the stalk or stem, leaves, a head or an ear, a cob, a grain, oil, protein, water, or starch, and, thus, crop component mass can be the mass of a component of the crop plant, such as stalk mass, leaf mass, ear mass, grain mass, oil mass, protein mass, water mass, or starch mass, as well as mass of various other crop components. The mass of the crop component can be used as an indicator of biomass. In some instances, as indicated atblock378, the sensor data may be indicative of vegetation volume, such as a volume of crops in front ofagricultural harvester100. In some instances, as indicated byblock379, the sensor data may be indicative of threshing rotor drive force, such as a fluid pressure or torque used to drive threshingrotor112 asagricultural harvester100 processes vegetation material. The sensor data can include other data as well, as indicated byblock380.
• Atblock382,predictive model generator210 obtains the geographic location corresponding to the sensor data. For instance, thepredictive model generator210 can obtain the geographic position fromgeographic position sensor204 and determine, based upon machine delays, machine speed, etc., a precise geographic location where the sensor signal was generated or from which thesensor data340 was derived. For instance, in the example in which the sensor data is indicative of a threshing rotor drive force, a time offset can be determined to identify the location on the field where the vegetation being processed by the threshing rotor was located, for example, based on location, heading, or speed data of theagricultural harvester100. Thus, the threshing rotor drive force can be correlated to the appropriate location on the field.
• Atblock384,predictive model generator210 generates one or more predictive biomass models, such asbiomass model350, that model a relationship between a vegetative index value obtained from a prior information map, such asprior information map258, and a characteristic being sensed by the in-situ sensor208 or a related characteristic. For instance,predictive model generator210 may generate a predictive biomass model that models the relationship between a vegetative index value and a sensed characteristic including vegetation height, such as crop height, vegetation density, such as crop density, vegetation mass, such as crop mass or crop component mass, vegetation volume, such as crop volume, or threshing rotor drive force indicated by the sensor data obtained from in-situ sensor208.
• Atblock386, the predictive biomass model, such aspredictive biomass model350, is provided topredictive map generator212 which generates apredictive biomass map360 that maps a predicted biomass value or a biomass characteristic value based on the vegetative index map and thepredictive biomass model350. For instance, in some examples, thepredictive biomass map360 predicts a biomass value, such as predicted biomass levels (e.g., high, medium, low) or more finite examples, such as weight (e.g., kilograms, pounds, etc.). In some examples,predictive biomass map360 predicts a biomass characteristic value, such as predicted vegetation height, such as crop height, as indicated byblock387. In some examples, thepredictive biomass map360 predicts vegetation density, such as crop density, as indicated byblock388. In some examples, thepredictive biomass map360 predicts vegetation mass, such as crop mass or crop component mass, as indicated byblock389. In some examples, thepredictive biomass map360 predicts vegetation volume, such as crop volume, as indicated byblock390. In some examples, the predictive biomass map predicts threshing rotor drive force, as indicated byblock391, and, in still other examples, thepredictive biomass map360 predicts other items, as indicated byblock392. It should be noted that, atblock386, thepredictive biomass map360 can predict any number of combinations of characteristics together, for instance, vegetation height along with vegetation density, vegetation mass, vegetation volume, or threshing rotor drive force. Further, thepredictive biomass map360 can be generated during the course of an agricultural operation. Thus, as an agricultural harvester is moving through a field performing an agricultural operation, thepredictive biomass map360 is generated as the agricultural operation is being performed.
• Atblock394,predictive map generator212 outputs thepredictive biomass map360. Atblock391, predictivebiomass map generator212 outputs the predictive biomass map for presentation to and possible interaction byoperator260. At block393,predictive map generator212 may configure thepredictive biomass map360 for consumption bycontrol system214. Atblock395,predictive map generator212 can also provide thepredictive biomass map360 to controlzone generator213 for generation of control zones. Atblock397,predictive map generator212 configures thepredictive biomass map360 in other ways as well. The predictive biomass map360 (with or without the control zones) is provided to controlsystem214. Atblock396,control system214 generates control signals to control thecontrollable subsystems216 based upon thepredictive biomass map360.
• Control system214 can generate control signals to control header (or other machine) actuator(s)248.Control system214 can generate control signals to controlpropulsion subsystem250.Control system214 can generate control signals to controlsteering subsystem252.Control system214 can generate control signals to controlresidue subsystem138.Control system214 can generate control signals to controlmachine cleaning subsystem254.Control system214 can generate control signals to controlthresher110.Control system214 can generate control signals to controlmaterial handling subsystem125.Control system214 can generate control signals to controlcrop cleaning subsystem118.Control system214 can generate control signals to controlcommunication system206.Control system214 can generate control signals to controloperator interface mechanisms218.Control system214 can generate control signals to control various othercontrollable subsystems256.
• In an example in whichcontrol system214 receives a functional predictive map or a functional predictive map with control zones added, thepath planning controller234controls steering subsystem252 to steeragricultural harvester100. In another example in whichcontrol system214 receives afunctional predictive map or a functional predictive map with control zones added, theresidue system controller244 controlsresidue subsystem138. In another example in whichcontrol system214 receives a functional predictive map or a functional predictive map with control zones added, thesettings controller232 controls thresher settings ofthresher110. In another example in whichcontrol system214 receives a functional predictive map or a functional predictive map with control zones added, thesettings controller232 or anothercontroller246 controlsmaterial handling subsystem125. In another example in whichcontrol system214 receives a functional predictive map or a functional predictive map with control zones added, thesettings controller232 controlscrop cleaning subsystem118. In another example in whichcontrol system214 receives a functional predictive map or a functional predictive map with control zones added, themachine cleaning controller245 controlsmachine cleaning subsystem254 onagricultural harvester100. In another example in whichcontrol system214 receives a functional predictive map or a functional predictive map with control zones added, thecommunication system controller229 controlscommunication system206. In another example in whichcontrol system214 receives a functional predictive map or a functional predictive map with control zones added, theoperator interface controller231 controlsoperator interface mechanisms218 onagricultural harvester100. In another example in whichcontrol system214 receives the functional predictive map or the functional predictive map with control zones added, the deckplate position controller242 controls machine/header actuators248 to control a deck plate onagricultural harvester100. In another example in whichcontrol system214 receives the functional predictive map or the functional predictive map with control zones added, thedraper belt controller240 controls machine/header actuators248 to control a draper belt onagricultural harvester100. In another example in whichcontrol system214 receives the functional predictive map or the functional predictive map with control zones added, theother controllers246 control othercontrollable subsystems256 onagricultural harvester100.
• In one example,control system214 may receive a functional predictive map or a functional predictive map with control zones added, and feedrate controller236 can controlpropulsion subsystem250 to control a speed ofagricultural harvester100 based upon the functional predictive map (with or without control zones), such as to control a feed rate of vegetation material.
• It can thus be seen that the present system receives a map that maps a characteristic value, such as a vegetative index value, to different locations in a field and uses one or more in-situ sensors that sense in-situ sensor data that is indicative of a characteristic, such as vegetation height, vegetation density, vegetation mass, vegetation volume, or threshing rotor drive force, and generates a model that models a relationship between the characteristic sensed using the in-situ sensor, or a related characteristic, and the characteristic mapped in the prior information map. Thus, the present system generates a functional predictive map using a model, in-situ data, and a prior information map and may configure the generated functional predictive map for consumption by a control system, for presentation to a local or remote operator or other user, or both. For example, the control system may use the map to control one or more systems of a combine harvester.
• FIG. 6 is a block diagram of an example portion of theagricultural harvester100 shown inFIG. 1. Particularly,FIG. 6 shows, among other things, examples ofpredictive model generator210 andpredictive map generator212. In the illustrated example, theprior information map258 is aprior operation map400.Prior operation map400 may include biomass characteristic values (such as vegetation characteristic values) at various locations in the field from a prior operation on the field. For example,prior operation map400 may be a map that includes biomass characteristic values at various locations in the field generated during a prior operation on the field in the same harvesting season, such as a spraying operation performed prior to harvesting operation.FIG. 6 also shows thatpredictive model generator210 and predictive map generator can receive, alternatively or in addition toprior information map258, a functional predictive biomass map, such as functionalpredictive biomass map360. Functionalpredictive biomass map360 can be used similarly asprior information map258 in thatmodel generator210 models a relationship between information provided by functionalpredictive biomass map360 and characteristics sensed by in-situ sensors208, andmap generator212 can, thus, use the model to generate a functional predictive map that predicts the characteristics sensed by the in-situ sensors208, or a characteristic indicative of the sensed characteristic, at different locations in the field based upon one or more of the values in thefunctional biomass map360 at those locations in the field and based on the predictive model. As illustrated inFIG. 6,predictive model generator210 andpredictive map generator212 can also receiveother maps401, for instance other prior information maps or other predictive maps, such as other predictive biomass maps generated in other ways than functionalpredictive biomass map360.
• Also, in the example shown inFIG. 6, in-situ sensor208 can include one or more of an agriculturalcharacteristic sensor402,operator input sensor404, and aprocessing system406. In-situ sensors208 can includeother sensors408 as well.
• Agriculturalcharacteristic sensor402 senses values indicative of agricultural characteristics.Operator input sensor404 senses various operator inputs. The inputs can be setting inputs for controlling the settings onagricultural harvester100 or other control inputs, such as steering inputs and other inputs. Thus, whenoperator260 changes a setting or provides a commanded input through anoperator interface mechanism218, such an input is detected byoperator input sensor404, which provides a sensor signal indicative of that sensed operator input.
• Processing system406 may receive the sensor signals from one or more of agriculturalcharacteristic sensor402, andoperator input sensor404 and generate an output indicative of the sensed variable. For instance,processing system406 may receive a sensor input from agriculturalcharacteristic sensor402 and generate an output indicative of an agricultural characteristic.Processing system406 may also receive an input fromoperator input sensor404 and generate an output indicative of the sensed operator input.
• Predictive model generator210 may include biomass-to-agriculturalcharacteristic model generator416 and biomass-to-command model generator422. In other examples,predictive model generator210 can include additional, fewer, orother model generators424. For example,predictive model generator210 may include specific biomass characteristic model generators, such as a vegetation height-to-agricultural characteristic model generator, a vegetation density-to-agricultural characteristic model generator, a vegetation mass-to-agricultural characteristic model generator, a vegetation volume-to-agricultural characteristic model generator, or a threshing rotor drive force-to-agricultural characteristic model generator. Similarly,predictive model generator210 may include a vegetation height-to-command model generator, a vegetation density-to-command model generator, a vegetation mass-to-command model generator, a vegetation volume-to-command model generator, or a threshing rotor drive force-to-command model generator. Themodel generator210 may also include a combination ofother model generators424.Predictive model generator210 may receive ageographic location334, or an indication of a geographic location, fromgeographic position sensor204 and generate apredictive model426 that models a relationship between the information in one or more of the maps and one or more of the agricultural characteristic sensed by agriculturalcharacteristic sensor402 and operator input commands sensed byoperator input sensor404.
• Biomass-to-agriculturalcharacteristic model generator416 generates a relationship between biomass values (which may be onpredictive biomass map360,prior operation map400, or other map401) and the agricultural characteristic sensed by agriculturalcharacteristic sensor402. Biomass-to-agriculturalcharacteristic model generator416 generates apredictive model426 that corresponds to this relationship.
• Biomass-to-operatorcommand model generator422 generates a model that models the relationship between biomass values as reflected onpredictive biomass map360,prior operation map400, orother map401 and operator input commands that are sensed byoperator input sensor404. Biomass-to-operatorcommand model generator422 generates apredictive model426 that corresponds to this relationship.
• Other model generators424 may include, for example, specific biomass characteristic model generators, such as a vegetation height-to-agricultural characteristic model generator, a vegetation density-to-agricultural characteristic model generator, a vegetation mass-to-agricultural characteristic model generator, a vegetation volume-to-agricultural characteristic model generator, or a threshing rotor drive force-to-agricultural characteristic model generator. Similarly,predictive model generator210 may include a vegetation height-to-command model generator, a vegetation density-to-command model generator, a vegetation mass-to-command model generator, a vegetation volume-to-command model generator, or a threshing rotor drive force-to-command model generator. Themodel generator210 may also include a combination ofother model generators424.
• Predictive model426 generated by thepredictive model generator210 can include one or more of the predictive models that may be generated by biomass-to-agriculturalcharacteristic model generator416 and biomass-to-operatorcommand model generator422, and other model generators that may be included as part ofother items424.
• In the example ofFIG. 6,predictive map generator212 includes predictive agriculturalcharacteristic map generator428 and a predictive operator command map generator432. In other examples,predictive map generator212 can include additional, fewer, orother map generators434.
• Predictive agriculturalcharacteristic map generator428 receives apredictive model426 that models the relationship between a biomass characteristic and an agricultural characteristic sensed by agricultural characteristic sensor402 (such as a predictive model generated by biomass-to-agricultural characteristic model generator416), and one or more of the prior information maps258 or functionalpredictive biomass map360, orother maps401. Predictive agriculturalcharacteristic map generator428 generates a functional predictive agricultural characteristic map436 that predicts agricultural characteristic values (or the agricultural characteristics of which the values are indicative) at different locations in the field based upon one or more of the biomass values or biomass characteristic values in one or more of the prior information maps258 or the functionalpredictive biomass map360, orother map401 at those locations in the field and based onpredictive model426.
• Predictive operator command map generator432 receives apredictive model426 that models the relationship between a biomass characteristic and operator command inputs detected by operator input sensor404 (such as a predictive model generated by biomass-to-command model generator422), and one or more of the prior information maps258 or the functionalpredictive biomass map360, orother maps401. Predictive operator command map generator432 generates a functional predictiveoperator command map440 that predicts operator command inputs at different locations in the field based upon one or more of the biomass values or biomass characteristic values from theprior information map258 or the functionalpredictive biomass map360, orother map401, at those locations in the field and based on thepredictive model426.
• Predictive map generator212 outputs one or more of the functionalpredictive maps436 and440. Each of the functionalpredictive maps436 and440 may be provided to controlzone generator213,control system214, or both.Control zone generator213 generates control zones and incorporates the control zones to provide a functional predictive map436 with control zones or a functionalpredictive map440 with control zones, or both. Any or all of functional predictive maps436 and440 (with or without control zones), which generates control signals to control one or more of thecontrollable subsystems216 based upon one or all of the functional predictive maps436 and440 (with or without control zones). Any or all of the maps436 and440 (with or without control zones) may be presented tooperator260 or another user.
• FIG. 7 shows a flow diagram illustrating one example of the operation ofpredictive model generator210 andpredictive map generator212 in generating one or morepredictive models426 and one or more functionalpredictive maps436 and440. Atblock442,predictive model generator210 andpredictive map generator212 receive a map. The map received bypredictive model generator210 orpredictive map generator212 in generating one or morepredictive models426 and one or more functionalpredictive maps436 and440 may beprior information map258, such as aprior operation map400 created using data obtained during a prior operation in a field. The map received bypredictive model generator210 orpredictive map generator212 in generating one or morepredictive models426 and one or more functionalpredictive maps436 and440 may be functionalpredictive biomass map360. As indicated byblock401, other maps can be received as well, such as other prior information maps or other predictive maps, for instance, other predictive biomass maps.
• At block444,predictive model generator210 receives a sensor signal containing sensor data from an in-situ sensor208. The in-situ sensor can be one or more of an agriculturalcharacteristic sensor402 and anoperator input sensor404. Agriculturalcharacteristic sensor402 senses an agricultural characteristic.Operator input sensor404 senses an operator input command.Predictive model generator210 can receive other in-situ sensor inputs from various other in-situ sensors208 as well, as indicated byblock408. Some other examples of in-situ sensors208 are shown inFIG. 8.
• Atblock454,processing system406 processes the data contained in the sensor signal or signals received from the in-situ sensor orsensors208 to obtain processeddata409, shown inFIG. 6. The data contained in the sensor signal or signals can be in a raw format that is processed to receive processeddata409. For example, a temperature sensor signal includes electrical resistance data, this electrical resistance data can be processed into temperature data. In other examples, processing may comprise digitizing, encoding, formatting, scaling, filtering, or classifying data. The processeddata409 may be indicative of one or more of an agricultural characteristic or an operator input command. The processeddata409 is provided topredictive model generator210.
• Returning toFIG. 7, atblock456,predictive model generator210 also receives ageographic location334, or an indication of a geographic location, fromgeographic position sensor204, as shown inFIG. 6. Thegeographic location334 may be correlated to the geographic location from which the sensed variable or variables, sensed by in-situ sensors208, were taken. For instance, thepredictive model generator210 can obtain thegeographic location334 fromgeographic position sensor204 and determine, based upon machine delays, machine speed, etc., a precise geographic location from which the processeddata409 was derived.
• Atblock458,predictive model generator210 generates one or morepredictive models426 that model a relationship between a mapped value in a received map and a characteristic represented in the processeddata409. For example, in some instances, the mapped value in a received map may be a biomass value or a biomass characteristic value, such as a vegetation height value, a vegetation density value, a vegetation mass value, a vegetation volume value, or a threshing rotor drive force value, and thepredictive model generator210 generates a predictive model using the mapped value of a received map and a characteristic sensed by in-situ sensors208, as represented in the processeddata409, or a related characteristic, such as a characteristic that correlates to the characteristic sensed by in-situ sensors208. Themodel generator210 can include a biomass-to-agricultural characteristic model generator, as indicated by block460, a biomass-to-command model generator, as indicated byblock464, or various other model generators, as indicated byblock465.
• The one or morepredictive models426 are provided topredictive map generator212. Atblock466,predictive map generator212 generates one or more functional predictive maps. The functional predictive maps may be functional predictive agricultural characteristic map436 and a functional predictiveoperator command map440, or any combination of these maps. Functional predictive agricultural characteristic map438 predicts agricultural characteristic values (or agricultural characteristics indicated by the values) at different locations in the field. Functional predictiveoperator command map440 predicts desired or likely operator command inputs at different locations in the field. Further, one or more of the functionalpredictive maps438 and440 can be generated during the course of an agricultural operation. Thus, asagricultural harvester100 is moving through a field performing an agricultural operation, the one or morepredictive maps438 and440 are generated as the agricultural operation is being performed.
• Atblock468,predictive map generator212 outputs the one or more functionalpredictive maps436 and440. At block470,predictive map generator212 may configure the map for presentation to and possible interaction by anoperator260 or another user. At block472,predictive map generator212 may configure the map for consumption bycontrol system214. Atblock474,predictive map generator212 can provide the one or morepredictive maps427 and440 to controlzone generator213 for generation and incorporation of control zones to provide a functional predictive map436 with control zones or a functionalpredictive map440 with control zones, or both. Atblock476,predictive map generator212 configures the one or morepredictive maps436 and440 in other ways. The one or more functional predictive maps438 (with or without control zones) and440 (with or without control zones) may be presented tooperator260 or another user or provided to controlsystem214 as well.
• At block478,control system214 then generates control signals to control the controllable subsystems based upon the one or more functional predictive maps438 and440 (or the functionalpredictive maps438 and440 having control zones) as well as an input from thegeographic position sensor204.
• In an example in whichcontrol system214 receives a functional predictive map or a functional predictive map with control zones added, thepath planning controller234controls steering subsystem252 to steeragricultural harvester100. In another example in whichcontrol system214 receives afunctional predictive map or a functional predictive map with control zones added, theresidue system controller244 controlsresidue subsystem138. In another example in whichcontrol system214 receives a functional predictive map or a functional predictive map with control zones added, thesettings controller232 controls thresher settings ofthresher110. In another example in whichcontrol system214 receives a functional predictive map or a functional predictive map with control zones added, thesettings controller232 or anothercontroller246 controlsmaterial handling subsystem125. In another example in whichcontrol system214 receives a functional predictive map or a functional predictive map with control zones added, thesettings controller232 controlscrop cleaning subsystem118. In another example in whichcontrol system214 receives a functional predictive map or a functional predictive map with control zones added, themachine cleaning controller245 controlsmachine cleaning subsystem254 onagricultural harvester100. In another example in whichcontrol system214 receives a functional predictive map or a functional predictive map with control zones added, thecommunication system controller229 controlscommunication system206. In another example in whichcontrol system214 receives a functional predictive map or a functional predictive map with control zones added, theoperator interface controller231 controlsoperator interface mechanisms218 onagricultural harvester100. In another example in whichcontrol system214 receives the functional predictive map or the functional predictive map with control zones added, the deckplate position controller242 controls machine/header actuators to control a deck plate onagricultural harvester100. In another example in whichcontrol system214 receives the functional predictive map or the functional predictive map with control zones added, thedraper belt controller240 controls machine/header actuators to control a draper belt onagricultural harvester100. In another example in whichcontrol system214 receives the functional predictive map or the functional predictive map with control zones added, theother controllers246 control othercontrollable subsystems256 onagricultural harvester100.
• In one example,control system214 may receive a functional predictive map or a functional predictive map with control zones added, and feedrate controller236 can controlpropulsion subsystem250 to control a speed ofagricultural harvester100 based upon the functional predictive map (with or without control zones), such as to control a feed rate of vegetation material.
• FIG. 8 shows a block diagram illustrating examples of in-situ sensors208. Some of the sensors shown inFIG. 8, or different combinations of them, may have both asensor402 and aprocessing system406, while others may act assensor402 described with respect toFIGS. 6 and 7 where theprocessing system406 is separate. Some of the possible in-situ sensors208 shown inFIG. 8 are shown and described above with respect to previous FIGS. and are similarly numbered.FIG. 8 shows that in-situ sensors208 can include operator input sensors980,machine sensors982, harvested material property sensors984, field and soil property sensors985, environmentalcharacteristic sensors987, and they may include a wide variety ofother sensors226. Operator input sensors980 may be sensors that sense operator inputs throughoperator interface mechanisms218. Therefore, operator input sensors980 may sense user movement of linkages, joysticks, a steering wheel, buttons, dials, or pedals. Operator input sensors480 can also sense user interactions with other operator input mechanisms, such as with a touch sensitive screen, with a microphone where speech recognition is utilized, or any of a wide variety of other operator input mechanisms.
• Machine sensors982 may sense different characteristics ofagricultural harvester100. For instance, as discussed above,machine sensors982 may includemachine speed sensors146,separator loss sensor148,clean grain camera150, forward lookingimage capture mechanism151,loss sensors152 orgeographic position sensor204, examples of which are described above.Machine sensors982 can also includemachine setting sensors991 that sense machine settings. Some examples of machine settings were described above with respect toFIG. 1. Front-end equipment (e.g., header)position sensor993 can sense the position of theheader102,reel164,cutter104, or other front-end equipment relative to the frame ofagricultural harvester100. For instance,sensors993 may sense the height ofheader102 above the ground.Machine sensors982 can also include front-end equipment (e.g., header) orientation sensors495.Sensors995 may sense the orientation ofheader102 relative toagricultural harvester100, or relative to the ground.Machine sensors982 may includestability sensors997.Stability sensors997 sense oscillation or bouncing motion (and amplitude) ofagricultural harvester100.Machine sensors982 may also includeresidue setting sensors999 that are configured to sense whetheragricultural harvester100 is configured to chop the residue, produce a windrow, or deal with the residue in another way.Machine sensors982 may include cleaning shoefan speed sensor951 that senses the speed of cleaningfan120.Machine sensors982 may includeconcave clearance sensors953 that sense the clearance between therotor112 andconcaves114 onagricultural harvester100.Machine sensors982 may includechaffer clearance sensors955 that sense the size of openings inchaffer122. Themachine sensors982 may include threshingrotor speed sensor957 that senses a rotor speed ofrotor112.Machine sensors982 may includerotor pressure sensor959 that senses the pressure used to driverotor112.Machine sensors982 may include sieve clearance sensor961 that senses the size of openings insieve124. Themachine sensors982 may includeMOG moisture sensor963 that senses a moisture level of the MOG passing throughagricultural harvester100.Machine sensors982 may include machine orientation sensor965 that senses the orientation ofagricultural harvester100.Machine sensors982 may include material feed rate sensors967 that sense the feed rate of material as the material travels throughfeeder house106,clean grain elevator130, or elsewhere inagricultural harvester100.Machine sensors982 can includebiomass sensors969 that sense the biomass traveling throughfeeder house106, throughseparator116, or elsewhere inagricultural harvester100. Themachine sensors982 may include fuel consumption sensor971 that senses a rate of fuel consumption over time ofagricultural harvester100.Machine sensors982 may include power utilization sensor973 that senses power utilization inagricultural harvester100, such as which subsystems are utilizing power, or the rate at which subsystems are utilizing power, or the distribution of power among the subsystems inagricultural harvester100.Machine sensors982 may includetire pressure sensors977 that sense the inflation pressure intires144 ofagricultural harvester100.Machine sensor982 may include a wide variety of other machine performance sensors, or machine characteristic sensors, indicated by block975. The machine performance sensors and machine characteristic sensors975 may sense machine performance or characteristics ofagricultural harvester100.
• Harvested material property sensors984 may sense characteristics of the severed crop material as the crop material is being processed byagricultural harvester100. The crop properties may include such things as crop type, crop moisture, grain quality (such as broken grain), MOG levels, grain constituents such as starches and protein, MOG moisture, and other crop material properties. Other sensors could sense straw “toughness”, adhesion of corn to ears, and other characteristics that might be beneficially used to control processing for better grain capture, reduced grain damage, reduced power consumption, reduced grain loss, etc.
• Field and soil property sensors985 may sense characteristics of the field and soil. The field and soil properties may include soil moisture, soil compactness, the presence and location of standing water, soil type, and other soil and field characteristics.
• Environmentalcharacteristic sensors987 may sense one or more environmental characteristics. The environmental characteristics may include such things as wind direction and wind speed, precipitation, fog, dust level or other obscurants, or other environmental characteristics.
• FIG. 9 shows a block diagram illustrating one example ofcontrol zone generator213.Control zone generator213 includes work machine actuator (WMA)selector486, controlzone generation system488, and regimezone generation system490.Control zone generator213 may also includeother items492. Controlzone generation system488 includes control zonecriteria identifier component494, control zoneboundary definition component496, target settingidentifier component498, andother items520. Regimezone generation system490 includes regime zonecriteria identification component522, regime zone boundary definition component524, settings resolveridentifier component526, andother items528. Before describing the overall operation ofcontrol zone generator213 in more detail, a brief description of some of the items incontrol zone generator213 and the respective operations thereof will first be provided.
• Agricultural harvester100, or other work machines, may have a wide variety of different types of controllable actuators that perform different functions. The controllable actuators onagricultural harvester100 or other work machines are collectively referred to as work machine actuators (WMAs). Each WMA may be independently controllable based upon values on a functional predictive map, or the WMAs may be controlled as sets based upon one or more values on a functional predictive map. Therefore,control zone generator213 may generate control zones corresponding to each individually controllable WMA or corresponding to the sets of WMAs that are controlled in coordination with one another.
• WMA selector486 selects a WMA or a set of WMAs for which corresponding control zones are to be generated. Controlzone generation system488 then generates the control zones for the selected WMA or set of WMAs. For each WMA or set of WMAs, different criteria may be used in identifying control zones. For example, for one WMA, the WMA response time may be used as the criteria for defining the boundaries of the control zones. In another example, wear characteristics (e.g., how much a particular actuator or mechanism wears as a result of movement thereof) may be used as the criteria for identifying the boundaries of control zones. Control zonecriteria identifier component494 identifies particular criteria that are to be used in defining control zones for the selected WMA or set of WMAs. Control zoneboundary definition component496 processes the values on a functional predictive map under analysis to define the boundaries of the control zones on that functional predictive map based upon the values in the functional predictive map under analysis and based upon the control zone criteria for the selected WMA or set of WMAs.
• Target settingidentifier component498 sets a value of the target setting that will be used to control the WMA or set of WMAs in different control zones. For instance, if the selected WMA ispropulsion subsystem250 or header orother machine actuators248 and the functional predictive map under analysis is a functional predictive biomass map360 (with control zones) that maps predictive biomass values or biomass characteristic values indicative of a biomass at different locations across the field, then the target setting in each control zone may be a target speed setting or a target header setting based on biomass values or biomass characteristic values contained in the functionalpredictive biomass map360 within the identified control zone. This is because, given a biomass of the vegetation at a location in the field to be harvested byagricultural harvester100, controlling the speed of theagricultural harvester100 or the header setting (such as header height), along with other machine settings, will correspondingly control a feed rate of vegetation through theagricultural harvester100.
• In some examples, whereagricultural harvester100 is to be controlled based on a current or future location of theagricultural harvester100, multiple target settings may be possible for a WMA at a given location. In that case, the target settings may have different values and may be competing. Thus, the target settings need to be resolved so that only a single target setting is used to control the WMA. For example, where the WMA is an actuator inpropulsion system250 that is being controlled in order to control the speed ofagricultural harvester100, multiple different competing sets of criteria may exist that are considered by controlzone generation system488 in identifying the control zones and the target settings for the selected WMA in the control zones. For instance, different target settings for controlling machine speed or header settings (such as height) may be generated based upon, for example, a detected or predicted agricultural characteristic value, a detected or predicted biomass value or biomass characteristic value, a detected or predicted vegetative index value, a detected or predicted feed rate value, a detected or predicted fuel efficiency value, a detected or predicted grain loss value, or a combination of these. It will be noted that these are merely examples, and target settings for various WMAs can be based on various other values or combinations of values. However, at any given time, theagricultural harvester100 cannot travel over the ground at multiple speeds or with multiple header heights simultaneously. Rather, at any given time, theagricultural harvester100 travels at a single speed and has a single header height. Thus, one of the competing target settings is selected to control the speed ofagricultural harvester100 or a height of the header ofagricultural harvester100.
• Therefore, in some examples, regimezone generation system490 generates regime zones to resolve multiple different competing target settings. Regime zonecriteria identification component522 identifies the criteria that are used to establish regime zones for the selected WMA or set of WMAs on the functional predictive map under analysis. Some criteria that can be used to identify or define regime zones include, for example, agricultural characteristics, biomass values or biomass characteristic values (such as vegetation height, vegetation mass, vegetation density, vegetation volume, thresher rotor drive force, etc.), vegetative index values, as well as a variety of other criteria (for instance, crop type or crop variety based on an as-planted map or another source of the crop type or crop variety), weed type, weed intensity, or crop state (such as whether the crop is down, partially down or standing), as well as any number of other criteria. These are merely some examples of the criteria that can be used to identify or define regime zones. Just as each WMA or set of WMAs may have a corresponding control zone, different WMAs or sets of WMAs may have a corresponding regime zone. Regime zone boundary definition component524 identifies the boundaries of regime zones on the functional predictive map under analysis based on the regime zone criteria identified by regime zonecriteria identification component522.
• In some examples, regime zones may overlap with one another. For instance, a biomass regime zone may overlap with a portion of or an entirety of a crop state regime zone. In such an example, the different regime zones may be assigned to a precedence hierarchy so that, where two or more regime zones overlap, the regime zone assigned with a greater hierarchical position or importance in the precedence hierarchy has precedence over the regime zones that have lesser hierarchical positions or importance in the precedence hierarchy. The precedence hierarchy of the regime zones may be manually set or may be automatically set using a rules-based system, a model-based system, or another system. As one example, where a biomass regime zone overlaps with a crop state regime zone, the crop state regime zone may be assigned a greater importance in the precedence hierarchy than the biomass regime zone so that the crop state zone takes precedence.
• In addition, each regime zone may have a unique settings resolver for a given WMA or set of WMAs. Settingsresolver identifier component526 identifies a particular settings resolver for each regime zone identified on the functional predictive map under analysis and a particular settings resolver for the selected WMA or set of WMAs.
• Once the settings resolver for a particular regime zone is identified, that settings resolver may be used to resolve competing target settings, where more than one target setting is identified based upon the control zones. The different types of settings resolvers can have different forms. For instance, the settings resolvers that are identified for each regime zone may include a human choice resolver in which the competing target settings are presented to an operator or other user for resolution. In another example, the settings resolver may include a neural network or other artificial intelligence or machine learning system. In such instances, the settings resolvers may resolve the competing target settings based upon a predicted or historic quality metric corresponding to each of the different target settings. As an example, an increased vehicle speed setting may reduce the time to harvest a field and reduce corresponding time-based labor and equipment costs but may increase grain losses. A reduced vehicle speed setting may increase the time to harvest a field and increase corresponding time-based labor and equipment costs but may decrease grain losses. When grain loss or time to harvest is selected as a quality metric, the predicted or historic value for the selected quality metric, given the two competing vehicle speed settings values, may be used to resolve the speed setting. In some instances, the settings resolvers may be a set of threshold rules that may be used instead of, or in addition to, the regime zones. An example of a threshold rule may be expressed as follows:
• 
  If predicted biomass level values within 20 feet of the header of theagricultural harvester 100 are greater thanxkilograms (wherexis a selected or predetermined value), then use the target setting value that is chosen based on feed rate over other competing target settings, otherwise use the target setting value based on grain loss over other competing target setting values.
• The settings resolvers may be logical components that execute logical rules in identifying a target setting. For instance, the settings resolver may resolve target settings while attempting to minimize harvest time or minimize the total harvest cost or maximize harvested grain or based on other variables that are computed as a function of the different candidate target settings. A harvest time may be minimized when an amount to complete a harvest is reduced to at or below a selected threshold. A total harvest cost may be minimized where the total harvest cost is reduced to at or below a selected threshold. Harvested grain may be maximized where the amount of harvested grain is increased to at or above a selected threshold.
• FIG. 9 is a flow diagram illustrating one example of the operation ofcontrol zone generator213 in generating control zones and regime zones for a map that thecontrol zone generator213 receives for zone processing (e.g., for a map under analysis).
• Atblock530,control zone generator213 receives a map under analysis for processing. In one example, as shown atblock532, the map under analysis is a functional predictive map. For example, the map under analysis may be one of the functionalpredictive maps438 or440. In another example, the map under analysis may be the functionalpredictive biomass map360.Block534 indicates that the map under analysis can be other maps as well.
• Atblock536,WMA selector486 selects a WMA or a set of WMAs for which control zones are to be generated on the map under analysis. Atblock538, control zonecriteria identification component494 obtains control zone definition criteria for the selected WMAs or set of WMAs.Block540 indicates an example in which the control zone criteria are or include wear characteristics of the selected WMA or set of WMAs.Block542 indicates an example in which the control zone definition criteria are or include a magnitude and variation of input source data, such as the magnitude and variation of the values on the map under analysis or the magnitude and variation of inputs from various in-situ sensors208.Block544 indicates an example in which the control zone definition criteria are or include physical machine characteristics, such as the physical dimensions of the machine, a speed at which different subsystems operate, or other physical machine characteristics.Block546 indicates an example in which the control zone definition criteria are or include a responsiveness of the selected WMA or set of WMAs in reaching newly commanded setting values.Block548 indicates an example in which the control zone definition criteria are or include machine performance metrics.Block549 indicates an example in which the control zone definition criteria are time based, meaning thatagricultural harvester100 will not cross the boundary of a control zone until a selected amount of time has elapsed sinceagricultural harvester100 entered a particular control zone. In some instances, the selected amount of time may be a minimum amount of time. Thus, in some instances, the control zone definition criteria may prevent theagricultural harvester100 from crossing a boundary of a control zone until at least the selected amount of time has elapsed.Block550 indicates an example in which the control zone definition criteria are or includes operator preferences.Block551 indicates an example in which the control zone definition criteria are based on a selected size value. For example, a control zone definition criterion that is based on a selected size value may preclude definition of a control zone that is smaller than the selected size. In some instances, the selected size may be a minimum size.Block552 indicates an example in which the control zone definition criteria are or include other items as well.
• Atblock554, regime zonecriteria identification component522 obtains regime zone definition criteria for the selected WMA or set of WMAs.Block556 indicates an example in which the regime zone definition criteria are based on a manual input fromoperator260 or another user.Block558 illustrates an example in which the regime zone definition criteria are based on agricultural characteristics.Block560 illustrates an example in which the regime zone definition criteria are based on biomass values or biomass characteristic values, such as vegetation height values, vegetation density values, vegetation mass values, vegetation volume values, or threshing rotor drive force values.Block562 illustrates an example in which the regime zone definition criteria are based on vegetative index values.Block564 indicates an example in which the regime zone definition criteria are or include other criteria as well, for instance, yield, crop type or crop variety, weed type, weed intensity, or crop state, such as whether the crop is down, partially down, or standing, as well as any number of other criteria.
• Atblock566, control zoneboundary definition component496 generates the boundaries of control zones on the map under analysis based upon the control zone criteria. Regime zone boundary definition component524 generates the boundaries of regime zones on the map under analysis based upon the regime zone criteria.Block568 indicates an example in which the zone boundaries are identified for the control zones and the regime zones.Block570 shows that target settingidentifier component498 identifies the target settings for each of the control zones. The control zones and regime zones can be generated in other ways as well, and this is indicated byblock572.
• Atblock574, settings resolveridentifier component526 identifies the settings resolver for the selected WMAs in each regime zone defined by regimes zone boundary definition component524. As discussed above, the regime zone resolver can be ahuman resolver576, an artificial intelligence or machinelearning system resolver578, aresolver580 based on predicted or historic quality for each competing target setting, a rules-basedresolver582, a performance criteria-basedresolver584, orother resolvers586.
• Atblock588,WMA selector486 determines whether there are more WMAs or sets of WMAs to process. If additional WMAs or sets of WMAs are remaining to be processed, processing reverts to block436 where the next WMA or set of WMAs for which control zones and regime zones are to be defined is selected. When no additional WMAs or sets of WMAs for which control zones or regime zones are to be generated are remaining, processing moves to block590 wherecontrol zone generator213 outputs a map with control zones, target settings, regime zones, and settings resolvers for each of the WMAs or sets of WMAs. As discussed above, the outputted map can be presented tooperator260 or another user; the outputted map can be provided to controlsystem214; or the outputted map can be output in other ways.
• FIG. 11 illustrates one example of the operation ofcontrol system214 in controllingagricultural harvester100 based upon a map that is output bycontrol zone generator213. Thus, at block592,control system214 receives a map of the worksite. In some instances, the map can be a functional predictive map that may include control zones and regime zones, as represented by block594. In some instances, the received map may be a functional predictive map that excludes control zones and regime zones.Block596 indicates an example in which the received map of the worksite can be a prior information map having control zones and regime zones identified on it.Block598 indicates an example in which the received map can include multiple different maps or multiple different map layers.Block610 indicates an example in which the received map can take other forms as well.
• At block612,control system214 receives a sensor signal fromgeographic position sensor204. The sensor signal fromgeographic position sensor204 can include data that indicates thegeographic location614 ofagricultural harvester100, thespeed616 ofagricultural harvester100, the heading618 ofagricultural harvester100, orother information620. Atblock622,zone controller247 selects a regime zone, and, atblock624,zone controller247 selects a control zone on the map based on the geographic position sensor signal. Atblock626,zone controller247 selects a WMA or a set of WMAs to be controlled. Atblock628,zone controller247 obtains one or more target settings for the selected WMA or set of WMAs. The target settings that are obtained for the selected WMA or set of WMAs may come from a variety of different sources. For instance, block630 shows an example in which one or more of the target settings for the selected WMA or set of WMAs is based on an input from the control zones on the map of the worksite.Block632 shows an example in which one or more of the target settings is obtained from human inputs fromoperator260 or another user. Block634 shows an example in which the target settings are obtained from an in-situ sensor208.Block636 shows an example in which the one or more target settings is obtained from one or more sensors on other machines working in the same field either concurrently withagricultural harvester100 or from one or more sensors on machines that worked in the same field in the past.Block638 shows an example in which the target settings are obtained from other sources as well.
• Atblock640,zone controller247 accesses the settings resolver for the selected regime zone and controls the settings resolver to resolve competing target settings into a resolved target setting. As discussed above, in some instances, the settings resolver may be a human resolver in whichcase zone controller247 controlsoperator interface mechanisms218 to present the competing target settings tooperator260 or another user for resolution. In some instances, the settings resolver may be a neural network or other artificial intelligence or machine learning system, andzone controller247 submits the competing target settings to the neural network, artificial intelligence, or machine learning system for selection. In some instances, the settings resolver may be based on a predicted or historic quality metric, on threshold rules, or on logical components. In any of these latter examples,zone controller247 executes the settings resolver to obtain a resolved target setting based on the predicted or historic quality metric, based on the threshold rules, or with the use of the logical components.
• Atblock642, withzone controller247 having identified the resolved target setting,zone controller247 provides the resolved target setting to other controllers incontrol system214, which generate and apply control signals to the selected WMA or set of WMAs based upon the resolved target setting. For instance, where the selected WMA is a machine orheader actuator248,zone controller247 provides the resolved target setting tosettings controller232 or header/real controller238 or both to generate control signals based upon the resolved target setting, and those generated control signals are applied to the machine orheader actuators248. Atblock644, if additional WMAs or additional sets of WMAs are to be controlled at the current geographic location of the agricultural harvester100 (as detected at block612), then processing reverts to block626 where the next WMA or set of WMAs is selected. The processes represented byblocks626 through644 continue until all of the WMAs or sets of WMAs to be controlled at the current geographical location of theagricultural harvester100 have been addressed. If no additional WMAs or sets of WMAs are to be controlled at the current geographic location of theagricultural harvester100 remain, processing proceeds to block646 wherezone controller247 determines whether additional control zones to be considered exist in the selected regime zone. If additional control zones to be considered exist, processing reverts to block624 where a next control zone is selected. If no additional control zones are remaining to be considered, processing proceeds to block648 where a determination as to whether additional regime zones are remaining to be consider.Zone controller247 determines whether additional regime zones are remaining to be considered. If additional regimes zone are remaining to be considered, processing reverts to block622 where a next regime zone is selected.
• Atblock650,zone controller247 determines whether the operation thatagricultural harvester100 is performing is complete. If not, thezone controller247 determines whether a control zone criterion has been satisfied to continue processing, as indicated byblock652. For instance, as mentioned above, control zone definition criteria may include criteria defining when a control zone boundary may be crossed by theagricultural harvester100. For example, whether a control zone boundary may be crossed by theagricultural harvester100 may be defined by a selected time period, meaning thatagricultural harvester100 is prevented from crossing a zone boundary until a selected amount of time has transpired. In that case, atblock652,zone controller247 determines whether the selected time period has elapsed. Additionally,zone controller247 can perform processing continually. Thus,zone controller247 does not wait for any particular time period before continuing to determine whether an operation of theagricultural harvester100 is completed. Atblock652,zone controller247 determines that it is time to continue processing, then processing continues at block612 wherezone controller247 again receives an input fromgeographic position sensor204. It will also be appreciated thatzone controller247 can control the WMAs and sets of WMAs simultaneously using a multiple-input, multiple-output controller instead of controlling the WMAs and sets of WMAs sequentially.
• FIG. 12 is a block diagram showing one example of anoperator interface controller231. In an illustrated example,operator interface controller231 includes operator inputcommand processing system654, othercontroller interaction system656,speech processing system658, andaction signal generator660. Operator inputcommand processing system654 includesspeech handling system662, touchgesture handling system664, andother items666. Othercontroller interaction system656 includes controllerinput processing system668 andcontroller output generator670.Speech processing system658 includestrigger detector672,recognition component674,synthesis component676, naturallanguage understanding system678,dialog management system680, andother items682.Action signal generator660 includes visualcontrol signal generator684, audiocontrol signal generator686, hapticcontrol signal generator688, andother items690. Before describing operation of the exampleoperator interface controller231 shown inFIG. 12 in handling various operator interface actions, a brief description of some of the items inoperator interface controller231 and the associated operation thereof is first provided.
• Operator inputcommand processing system654 detects operator inputs onoperator interface mechanisms218 and processes those inputs for commands.Speech handling system662 detects speech inputs and handles the interactions withspeech processing system658 to process the speech inputs for commands. Touchgesture handling system664 detects touch gestures on touch sensitive elements inoperator interface mechanisms218 and processes those inputs for commands.
• Othercontroller interaction system656 handles interactions with other controllers incontrol system214. Controllerinput processing system668 detects and processes inputs from other controllers incontrol system214, andcontroller output generator670 generates outputs and provides those outputs to other controllers incontrol system214.Speech processing system658 recognizes speech inputs, determines the meaning of those inputs, and provides an output indicative of the meaning of the spoken inputs. For instance,speech processing system658 may recognize a speech input fromoperator260 as a settings change command in whichoperator260 iscommanding control system214 to change a setting for acontrollable subsystem216. In such an example,speech processing system658 recognizes the content of the spoken command, identifies the meaning of that command as a settings change command, and provides the meaning of that input back tospeech handling system662.Speech handling system662, in turn, interacts withcontroller output generator670 to provide the commanded output to the appropriate controller incontrol system214 to accomplish the spoken settings change command.
• Speech processing system658 may be invoked in a variety of different ways. For instance, in one example,speech handling system662 continuously provides an input from a microphone (being one of the operator interface mechanisms218) tospeech processing system658. The microphone detects speech fromoperator260, and thespeech handling system662 provides the detected speech tospeech processing system658.Trigger detector672 detects a trigger indicating thatspeech processing system658 is invoked. In some instances, whenspeech processing system658 is receiving continuous speech inputs fromspeech handling system662,speech recognition component674 performs continuous speech recognition on all speech spoken byoperator260. In some instances,speech processing system658 is configured for invocation using a wakeup word. That is, in some instances, operation ofspeech processing system658 may be initiated based on recognition of a selected spoken word, referred to as the wakeup word. In such an example, whererecognition component674 recognizes the wakeup word, therecognition component674 provides an indication that the wakeup word has been recognized to triggerdetector672.Trigger detector672 detects thatspeech processing system658 has been invoked or triggered by the wakeup word. In another example,speech processing system658 may be invoked by anoperator260 actuating an actuator on a user interface mechanism, such as by touching an actuator on a touch sensitive display screen, by pressing a button, or by providing another triggering input. In such an example,trigger detector672 can detect thatspeech processing system658 has been invoked when a triggering input via a user interface mechanism is detected.Trigger detector672 can detect thatspeech processing system658 has been invoked in other ways as well.
• Oncespeech processing system658 is invoked, the speech input fromoperator260 is provided tospeech recognition component674.Speech recognition component674 recognizes linguistic elements in the speech input, such as words, phrases, or other linguistic units. Naturallanguage understanding system678 identifies a meaning of the recognized speech. The meaning may be a natural language output, a command output identifying a command reflected in the recognized speech, a value output identifying a value in the recognized speech, or any of a wide variety of other outputs that reflect the understanding of the recognized speech. For example, the naturallanguage understanding system678 andspeech processing system568, more generally, may understand of the meaning of the recognized speech in the context ofagricultural harvester100.
• In some examples,speech processing system658 can also generate outputs that navigateoperator260 through a user experience based on the speech input. For instance,dialog management system680 may generate and manage a dialog with the user in order to identify what the user wishes to do. The dialog may disambiguate a user's command; identify one or more specific values that are needed to carry out the user's command; or obtain other information from the user or provide other information to the user or both.Synthesis component676 may generate speech synthesis which can be presented to the user through an audio operator interface mechanism, such as a speaker. Thus, the dialog managed bydialog management system680 may be exclusively a spoken dialog or a combination of both a visual dialog and a spoken dialog.
• Action signal generator660 generates action signals to controloperator interface mechanisms218 based upon outputs from one or more of operator inputcommand processing system654, othercontroller interaction system656, andspeech processing system658. Visualcontrol signal generator684 generates control signals to control visual items inoperator interface mechanisms218. The visual items may be lights, a display screen, warning indicators, or other visual items. Audiocontrol signal generator686 generates outputs that control audio elements ofoperator interface mechanisms218. The audio elements include a speaker, audible alert mechanisms, horns, or other audible elements. Hapticcontrol signal generator688 generates control signals that are output to control haptic elements ofoperator interface mechanisms218. The haptic elements include vibration elements that may be used to vibrate, for example, the operator's seat, the steering wheel, pedals, or joysticks used by the operator. The haptic elements may include tactile feedback or force feedback elements that provide tactile feedback or force feedback to the operator through operator interface mechanisms. The haptic elements may include a wide variety of other haptic elements as well.
• FIG. 13 is a flow diagram illustrating one example of the operation ofoperator interface controller231 in generating an operator interface display on anoperator interface mechanism218, which can include a touch sensitive display screen.FIG. 13 also illustrates one example of howoperator interface controller231 can detect and process operator interactions with the touch sensitive display screen.
• Atblock692,operator interface controller231 receives a map. Block694 indicates an example in which the map is a functional predictive map, and block696 indicates an example in which the map is another type of map. Atblock698,operator interface controller231 receives an input fromgeographic position sensor204 identifying the geographic location of theagricultural harvester100. As indicated inblock700, the input fromgeographic position sensor204 can include the heading, along with the location, ofagricultural harvester100.Block702 indicates an example in which the input fromgeographic position sensor204 includes the speed ofagricultural harvester100, and block704 indicates an example in which the input fromgeographic position sensor204 includes other items.
• Atblock706, visualcontrol signal generator684 inoperator interface controller231 controls the touch sensitive display screen inoperator interface mechanisms218 to generate a display showing all or a portion of a field represented by the received map.Block708 indicates that the displayed field can include a current position marker showing a current position of theagricultural harvester100 relative to the field.Block710 indicates an example in which the displayed field includes a next work unit marker that identifies a next work unit (or area on the field) in whichagricultural harvester100 will be operating.Block712 indicates an example in which the displayed field includes an upcoming area display portion that displays areas that are yet to be processed byagricultural harvester100, and block714 indicates an example in which the displayed field includes previously visited display portions that represent areas of the field thatagricultural harvester100 has already processed.Block716 indicates an example in which the displayed field displays various characteristics of the field having georeferenced locations on the map. For instance, if the received map is a predictive biomass map, such aspredictive biomass map360, the displayed field may show the different biomass values or biomass characteristic values georeferenced within the displayed field. In other examples, the received map may be another map, such as other predictive maps, such aspredictive maps436 or440, and, thus, the displayed field may show different characteristic values, such as agricultural characteristic values or operator command values in the field georeferenced within the displayed field. The mapped characteristics can be shown in the previously visited areas (as shown in block714), in the upcoming areas (as shown in block712), and in the next work unit (as shown in block710).Block718 indicates an example in which the displayed field includes other items as well.
• FIG. 14 is a pictorial illustration showing one example of auser interface display720 that can be generated on a touch sensitive display screen. In other implementations, theuser interface display720 may be generated on other types of displays. The touch sensitive display screen may be mounted in the operator compartment ofagricultural harvester100 or on the mobile device or elsewhere.User interface display720 will be described prior to continuing with the description of the flow diagram shown inFIG. 13.
• In the example shown inFIG. 14,user interface display720 illustrates that the touch sensitive display screen includes a display feature for operating amicrophone722 and aspeaker724. Thus, the touch sensitive display may be communicably coupled to themicrophone722 and thespeaker724.Block726 indicates that the touch sensitive display screen can include a wide variety of user interface control actuators, such as buttons, keypads, soft keypads, links, icons, switches, etc. Theoperator260 can actuator the user interface control actuators to perform various functions.
• In the example shown inFIG. 14,user interface display720 includes a field display portion728 that displays at least a portion of the field in which theagricultural harvester100 is operating. The field display portion728 is shown with acurrent position marker708 that corresponds to a current position ofagricultural harvester100 in the portion of the field shown in field display portion728. In one example, the operator may control the touch sensitive display in order to zoom into portions of field display portion728 or to pan or scroll the field display portion728 to show different portions of the field. Anext work unit730 is shown as an area of the field directly in front of thecurrent position marker708 ofagricultural harvester100. Thecurrent position marker708 may also be configured to identify the direction of travel ofagricultural harvester100, a speed of travel ofagricultural harvester100 or both. InFIG. 14, the shape of thecurrent position marker708 provides an indication as to the orientation of theagricultural harvester100 within the field which may be used as an indication of a direction of travel of theagricultural harvester100.
• The size of thenext work unit730 marked on field display portion728 may vary based upon a wide variety of different criteria. For instance, the size ofnext work unit730 may vary based on the speed of travel ofagricultural harvester100. Thus, when theagricultural harvester100 is traveling faster, then the area of thenext work unit730 may be larger than the area ofnext work unit730 ifagricultural harvester100 is traveling more slowly. In another example, the size of thenext work unit730 may vary based on the dimensions of theagricultural harvester100, including equipment on agricultural harvester100 (such as header102). For example, the width of thenext work unit730 may vary based on a width ofheader102. Field display portion728 is also shown displaying previously visitedarea714 andupcoming areas712. Previously visitedareas714 represent areas that are already harvested whileupcoming areas712 represent areas that still need to be harvested. The field display portion728 is also shown displaying different characteristics of the field. In the example illustrated inFIG. 14, the map that is being displayed is a predictive biomass map, such as functionalpredictive biomass map360. Therefore, a plurality of biomass markers are displayed on field display portion728. There are a set ofbiomass display markers732 shown in the already visitedareas714. There are also a set ofbiomass display markers732 shown in theupcoming areas712, and there are a set ofbiomass display markers732 shown in thenext work unit730.FIG. 14 shows that thebiomass display markers732 are made up of different symbols that indicate an area of similar biomass or biomass characteristics. In the example shown inFIG. 14, the ! symbol represents areas of high vegetation height; the * symbol represents areas of medium vegetation height; and the # symbol represents an area of low vegetation height. Vegetation height is merely one example, other biomass characteristics, such as vegetation density, vegetation mass, vegetation volume, threshing rotor drive force, as well as other biomass characteristics can also be displayed. The field display portion728 shows different measured or predicted values (or characteristics indicated by the values) that are located at different areas within the field and represents those measured or predicted values (or characteristics indicated by or derived from the values) with a variety ofdisplay markers732. As shown, the field display portion728 includes display markers, particularlybiomass display markers732 in the illustrated example ofFIG. 14, at particular locations associated with particular locations on the field being displayed. In some instances, each location of the field may have a display marker associated therewith. Thus, in some instances, a display marker may be provided at each location of the field display portion728 to identify the nature of the characteristic being mapped for each particular location of the field. Consequently, the present disclosure encompasses providing a display marker, such as the biomass display marker732 (as in the context of the present example ofFIG. 14), at one or more locations on the field display portion728 to identify the nature, degree, etc., of the characteristic being displayed, thereby identifying the characteristic at the corresponding location in the field being displayed. As described earlier, thedisplay markers732 may be made up of different symbols, and, as described below, the symbols may be any display feature such as different colors, shapes, patterns, intensities, text, icons, or other display features. In some instances, each location of the field may have a display marker associated therewith. Thus, in some instances, a display marker may be provided at each location of the field display portion728 to identify the nature of the characteristic being mapped for each particular location of the field. Consequently, the present disclosure encompasses providing a display marker, such as the loss level display marker732 (as in the context of the present example ofFIG. 11), at one or more locations on the field display portion728 to identify the nature, degree, etc., of the characteristic being displayed, thereby identifying the characteristic at the corresponding location in the field being displayed.
• In other examples, the map being displayed may be one or more of the maps described herein, including information maps, prior information maps, the functional predictive maps, such as predictive maps or predictive control zone maps, or a combination thereof. Thus, the markers and characteristics being displayed will correlate to the information, data, characteristics, and values provided by the one or more maps being displayed.
• In the example ofFIG. 14,user interface display720 also has acontrol display portion738.Control display portion738 allows the operator to view information and to interact withuser interface display720 in various ways.
• The actuators and display markers inportion738 may be displayed as, for example, individual items, fixed lists, scrollable lists, drop down menus, or drop down lists. In the example shown inFIG. 14,display portion738 shows information for the three different ear size categories that correspond to the three symbols mentioned above.Display portion738 also includes a set of touch sensitive actuators with which theoperator260 can interact by touch. For example, theoperator260 may touch the touch sensitive actuators with a finger to activate the respective touch sensitive actuator. As shown inFIG. 14,display portion738 includes interactive tabs, such asbiomass tab762,vegetation height tab763,vegetation density tab764,vegetation mass tab765,vegetation volume tab766, threshing rotordrive force tab768, andother tab770. Activating one of the tabs can modify which values are displayed inportions728 and738. For instance, as shown,vegetation height tab763 is activated, and, thus, the values mapped on portion728 and shown inportion738 correspond to vegetation height values on the field. When theoperator260 touches thetab762, touchgesture handling system664updates portion728 and738 to display characteristics relating to biomass values. When theoperator260 touches thetab764, touchgesture handling system664updates portion728 and738 to display characteristics relating to vegetation density. When theoperator260 touches thetab765, touchgesture handling system664updates portions728 and738 to display characteristics relating to vegetation mass. When the operator touchestab766, toughgesture handling system664updates portions728 and738 to display characteristics relating to vegetation volume. When the operator touches thetab768, touchgesture handling system664updates portions728 and738 to display characteristic relating to threshing rotor drive force. When theoperator260 touches thetab770, touchgesture handling system664updates portion728 and738 to display other biomass characteristics.
• As shown inFIG. 14,display portion738 includes an interactive flag display portion, indicated generally at741. Interactiveflag display portion741 includes aflag column739 that shows flags that have been automatically or manually set.Flag actuator740 allowsoperator260 to mark a location, such as the current location of the agricultural harvester, or another location on the field designated by the operator and add information indicating the characteristic, such as vegetation height, found at the current location. For instance, when theoperator260 actuates theflag actuator740 by touching theflag actuator740, touchgesture handling system664 inoperator interface controller231 identifies the current location as one whereagricultural harvester100 encountered high vegetation height. When theoperator260 touches thebutton742, touchgesture handling system664 identifies the current location as a location whereagricultural harvester100 encountered medium vegetation height. When theoperator260 touches thebutton744, touchgesture handling system664 identifies the current location as a location whereagricultural harvester100 encountered low vegetation height. Upon actuation of one of theflag actuators740,742, or744, touchgesture handling system664 can control visualcontrol signal generator684 to add a symbol corresponding to the identified characteristic on field display portion728 at a location the user identifies. In this way, areas of the field where the predicted value did not accurately represent an actual value can be marked for later analysis, and can also be used in machine learning. In other examples, the operator may designate areas ahead of or around theagricultural harvester100 by actuating one of theflag actuators740,742, or744 such that control of theagricultural harvester100 can be undertaken based on the value designated by theoperator260.
• Display portion738 also includes an interactive marker display portion, indicated generally at743. Interactivemarker display portion743 includes asymbol column746 that displays the symbols corresponding to each category of values or characteristics (in the case ofFIG. 14, ear size) that is being tracked on the field display portion728.Display portion738 also includes an interactive designator display portion, indicated generally at745. Interactivedesignator display portion745 includes adesignator column748 that shows the designator (which may be a textual designator or other designator) identifying the category of values or characteristics (in the case ofFIG. 14, vegetation height). Without limitation, the symbols insymbol column746 and the designators indesignator column748 can include any display feature such as different colors, shapes, patterns, intensities, text, icons, or other display features, and can be customizable by interaction of an operator ofagricultural harvester100.
• Display portion738 also includes an interactive value display portion, indicated generally at747. Interactivevalue display portion747 includes avalue display column750 that displays selected values. The selected values correspond to the characteristics or values being tracked or displayed, or both, on field display portion728. The selected values can be selected by an operator of theagricultural harvester100. The selected values invalue display column750 define a range of values or a value by which other values, such as predicted values, are to be classified. Thus, in the example inFIG. 14, a predicted or measured vegetation height (such as heights of small grain plants or wheat plants) meeting or greater than 1500 centimeters is classified as “a high vegetation height” and a predicted or measured vegetation height (such as heights of small grain plants or wheat plants) meeting or less than 600 centimeters is classified as “low vegetation height.” In some examples, the selected values may include a range, such that a predicted or measured value that is within the range of the selected value will be classified under the corresponding designator. As shown inFIG. 14, “medium vegetation height” includes a range of 601-1499 centimeters such that a predicted or measured vegetation height (such as heights of small grain plants or wheat plants) falling with the range of 601-1499 centimeters is classified as “medium vegetation height”. These are merely examples. The selected values invalue display column750 are adjustable by an operator ofagricultural harvester100. In one example, theoperator260 can select the particular part of field display portion728 for which the values incolumn750 are to be displayed. Thus, the values incolumn750 can correspond to values indisplay portions712,714 or730.
• Display portion738 also includes an interactive threshold display portion, indicated generally at749. Interactivethreshold display portion749 includes a thresholdvalue display column752 that displays action threshold values. Action threshold values incolumn752 may be threshold values corresponding to the selected values invalue display column750. If the predicted or measured values of characteristics being tracked or displayed, or both, satisfy the corresponding action threshold values in thresholdvalue display column752, then controlsystem214 takes the action identified incolumn754. In some instances, a measured or predicted value may satisfy a corresponding action threshold value by meeting or exceeding the corresponding action threshold value. In one example,operator260 can select a threshold value, for example, in order to change the threshold value by touching the threshold value in thresholdvalue display column752. Once selected, theoperator260 may change the threshold value. The threshold values incolumn752 can be configured such that the designated action is performed when the measured or predicted value of the characteristic exceeds the threshold value, equals the threshold value, or is less than the threshold value. In some instances, the threshold value may represent a range of values, or range of deviation from the selected values invalue display column750, such that a predicted or measured characteristic value that meets or falls within the range satisfies the threshold value. For instance, in the example of vegetation height, a predicted vegetation height that falls within 10% of 1500 centimeters will satisfy the corresponding action threshold value (of within 10% of 1500 centimeters) and an action, such as decreasing the speed of the agricultural harvester, will be taken bycontrol system214. In other examples, the threshold values in column thresholdvalue display column752 are separate from the selected values invalue display column750, such that the values invalue display column750 define the classification and display of predicted or measured values, while the action threshold values define when an action is to be taken based on the measured or predicted values. For example, while a predicted or measured vegetation height of 1200 centimeters may be designated as a “medium vegetation height” for purposes of classification and display, the action threshold value may be 1300 centimeters such that no action will be taken until the vegetation height satisfies the threshold value. In other examples, the threshold values in thresholdvalue display column752 may include distances or times. For instance, in the example of a distance, the threshold value may be a threshold distance from the area of the field where the measured or predicted value is georeferenced that theagricultural harvester100 must be before an action is taken. For example, a threshold distance value of 5 feet would mean that an action will be taken when the agricultural harvester is at or within 5 feet of the area of the field where the measured or predicted value is georeferenced. In an example where the threshold value is time, the threshold value may be a threshold time for theagricultural harvester100 to reach the area of the field where the measured or predictive value is georeferenced. For instance, a threshold value of 5 seconds would mean that an action will be taken when theagricultural harvester100 is 5 seconds away from the area of the field where the measured or predicted value is georeferenced. In such an example, the current location and travel speed of the agricultural harvester can be accounted for.
• Display portion738 also includes an interactive action display portion, indicated generally at751. Interactiveaction display portion751 includes anaction display column754 that displays action identifiers that indicate actions to be taken when a predicted or measured value satisfies an action threshold value in thresholdvalue display column752.Operator260 can touch the action identifiers incolumn754 to change the action that is to be taken. When a threshold is satisfied, an action may be taken. For instance, at the bottom ofcolumn754, an adjust header position (such adjusting the height, pitch, or roll of the header relative to the ground or to the frame of the agricultural harvester) and an adjust travel speed (such as increasing or reducing the speed at which the agricultural harvester travels over the field) are identified as actions that will be taken if the measured or predicted value meets the threshold value incolumn752. In some examples, when a threshold is met, multiple actions may be taken. For instance, a threshing rotor speed may be adjusted, a power output to the material handling subsystem components may be adjusted, and a concave clearance may be adjusted. These are merely some examples.
• The actions that can be set incolumn754 can be any of a wide variety of different types of actions. For example, the actions can include a keep out action which, when executed, inhibitsagricultural harvester100 from further harvesting in an area. The actions can include a speed change action which, when executed, changes the travel speed ofagricultural harvester100 through the field. The actions can include a setting change action for changing a setting of an internal actuator or another WMA or set of WMAs or for implementing a settings change action that changes a setting, such as a header position setting, for instance, a header height setting, a header pitch setting (e.g. tilt fore-to-aft), or a header roll setting (e.g., tilt side-to-side). These are examples only, and a wide variety of other actions are contemplated herein.
• The items shown onuser interface display720 can be visually controlled. Visually controlling theinterface display720 may be performed to capture the attention ofoperator260. For instance, the items can be controlled to modify the intensity, color, or pattern with which the items are displayed. Additionally, the items may be controlled to flash. The described alterations to the visual appearance of the items are provided as examples. Consequently, other aspects of the visual appearance of the items may be altered. Therefore, the items can be modified under various circumstances in a desired manner in order, for example, to capture the attention ofoperator260. Additionally, while a particular number of items are shown onuser interface display720, this need not be the case. In other examples, more or less items, including more or less of a particular item can be included onuser interface display720.
• Returning now to the flow diagram ofFIG. 13, the description of the operation ofoperator interface controller231 continues. Atblock760,operator interface controller231 detects an input setting a flag and controls the touch sensitiveuser interface display720 to display the flag on field display portion728. The detected input may be an operator input, as indicated at762, or an input from another controller, as indicated at764. Atblock766,operator interface controller231 detects an in-situ sensor input indicative of a measured characteristic of the field from one of the in-situ sensors208. Atblock768, visualcontrol signal generator684 generates control signals to controluser interface display720 to display actuators for modifyinguser interface display720 and for modifying machine control. For instance, block770 represents that one or more of the actuators for setting or modifying the values incolumns739,746, and748 can be displayed. Thus, the user can set flags and modify characteristics of those flags.Block772 represents that action threshold values incolumn752 are displayed.Block776 represents that the actions incolumn754 are displayed, and block778 represents that the selected value incolumn750 is displayed.Block780 indicates that a wide variety of other information and actuators can be displayed onuser interface display720 as well.
• Atblock782, operator inputcommand processing system654 detects and processes operator inputs corresponding to interactions with theuser interface display720 performed by theoperator260. Where the user interface mechanism on whichuser interface display720 is displayed is a touch sensitive display screen, interaction inputs with the touch sensitive display screen by theoperator260 can be touch gestures784. In some instances, the operator interaction inputs can be inputs using a point and clickdevice786 or otheroperator interaction inputs788.
• Atblock790,operator interface controller231 receives signals indicative of an alert condition. For instance, block792 indicates that signals may be received by controllerinput processing system668 indicating that detected or predicted values in satisfy threshold conditions present incolumn752. As explained earlier, the threshold conditions may include values being below a threshold, at a threshold, or above a threshold.Block794 shows thataction signal generator660 can, in response to receiving an alert condition, alert theoperator260 by using visualcontrol signal generator684 to generate visual alerts, by using audiocontrol signal generator686 to generate audio alerts, by using hapticcontrol signal generator688 to generate haptic alerts, or by using any combination of these. Similarly, as indicated byblock796,controller output generator670 can generate outputs to other controllers incontrol system214 so that those controllers perform the corresponding action identified incolumn754.Block798 shows thatoperator interface controller231 can detect and process alert conditions in other ways as well.
• Block900 shows thatspeech handling system662 may detect and process inputs invokingspeech processing system658.Block902 shows that performing speech processing may include the use ofdialog management system680 to conduct a dialog with theoperator260. Block904 shows that the speech processing may include providing signals tocontroller output generator670 so that control operations are automatically performed based upon the speech inputs.
• Table 1, below, shows an example of a dialog betweenoperator interface controller231 andoperator260. In Table 1,operator260 uses a trigger word or a wakeup word that is detected bytrigger detector672 to invokespeech processing system658. In the example shown in Table 1, the wakeup word is “Johnny”.

• TABLE 1
  Operator: “Johnny, tell me about current biomass characteristics.”
  Operator Interface Controller: “Vegetation height at current location is
  high.”
  Operator: “Johnny, what should I do because of the vegetation height?”
  Operator Interface Controller: “Reduce speed to achieve desired feed rate.”

• Table 2 shows an example in whichspeech synthesis component676 provides an output to audiocontrol signal generator686 to provide audible updates on an intermittent or periodic basis. The interval between updates may be time-based, such as every five minutes, or coverage or distance-based, such as every five acres, or exception-based, such as when a measured value is greater than a threshold value.

• TABLE 2
  Operator Interface Controller: “Over the last 10 minutes, vegetation height
  has been high.”
  Operator Interface Controller: “Next 1 acre predicted vegetation height
  is medium.”
  Operator Interface Controller: “Caution: upcoming change in vegetation
  height. travel speed increased.”

• The example shown in Table 3 illustrates that some actuators or user input mechanisms on the touchsensitive display720 can be supplemented with speech dialog. The example in Table 3 illustrates thataction signal generator660 can generate action signals to automatically mark a biomass or biomass characteristic area in the field being harvested.

• TABLE 3
  Human: “Johnny, mark high vegetation height area.”
  Operator Interface Controller: “High vegetation height area marked.”

• The example shown in Table 4 illustrates thataction signal generator660 can conduct a dialog withoperator260 to begin and end marking of a biomass or biomass characteristic area.

• TABLE 4
  Human: “Johnny, stall marking high vegetation height area.”
  Operator Interface Controller: “Marking high vegetation height area.”
  Human: “Johnny, stop marking high vegetation height area.”
  Operator Interface Controller: “High vegetation height area marking
  stopped.”

• The example shown in Table 5 illustrates that action signal generator160 can generate signals to mark a biomass or biomass characteristic area in a different way than those shown in Tables 3 and 4.

• TABLE 5
  Human: “Johnny, mark next 100 feet as a low vegetation height.”
  Operator Interface Controller: “Next 100 feet marked as a low vegetation
  height.”

• Returning again toFIG. 13, block906 illustrates thatoperator interface controller231 can detect and process conditions for outputting a message or other information in other ways as well. For instance, othercontroller interaction system656 can detect inputs from other controllers indicating that alerts or output messages should be presented tooperator260.Block908 shows that the outputs can be audio messages.Block910 shows that the outputs can be visual messages, and block912 shows that the outputs can be haptic messages. Untiloperator interface controller231 determines that the current harvesting operation is completed, as indicated byblock914, processing reverts to block698 where the geographic location ofharvester100 is updated and processing proceeds as described above to updateuser interface display720.
• Once the operation is complete, then any desired values that are displayed, or have been displayed onuser interface display720, can be saved. Those values can also be used in machine learning to improve different portions ofpredictive model generator210,predictive map generator212,control zone generator213, control algorithms, or other items. Saving the desired values is indicated byblock916. The values can be saved locally onagricultural harvester100, or the values can be saved at a remote server location or sent to another remote system.
• It can thus be seen that a map that shows agricultural characteristic values, such as vegetative index values or biomass values, at different geographic locations of a field being harvested is obtained by an agricultural harvester. An in-situ sensor on the harvester senses a characteristic that has values indicative of an agricultural characteristic, such as an agricultural characteristic (e.g., a biomass characteristic), or an operator command as the agricultural harvester moves through the field. A predictive map generator generates a predictive map that predicts control values for different locations in the field based on the vegetative index values or values of the biomass in the received map and the characteristic sensed by the in-situ sensor. A control system controls controllable subsystem based on the control values in the predictive map.
• A control value is a value upon which an action can be based. A control value, as described herein, can include any value (or characteristics indicated by or derived from the value) that may be used in the control ofagricultural harvester100. A control value can be any value indicative of an agricultural characteristic. A control value can be a predicted value, a measured value, or a detected value. A control value may include any of the values provided by a map, such as any of the maps described herein, for instance, a control value can be a value provided by an information map, a value provided by prior information map, or a value provided predictive map, such as a functional predictive map. A control value can also include any of the characteristics indicated by or derived from the values detected by any of the sensors described herein. In other examples, a control value can be provided by an operator of the agricultural machine, such as a command input by an operator of the agricultural machine.
• The present discussion has mentioned processors and servers. In some examples, the processors and servers include computer processors with associated memory and timing circuitry, not separately shown. The processors and servers are functional parts of the systems or devices to which the processors and servers belong and are activated by and facilitate the functionality of the other components or items in those systems.
• Also, a number of user interface displays have been discussed. The displays can take a wide variety of different forms and can have a wide variety of different user actuatable operator interface mechanisms disposed thereon. For instance, user actuatable operator interface mechanisms may include text boxes, check boxes, icons, links, drop-down menus, search boxes, etc. The user actuatable operator interface mechanisms can also be actuated in a wide variety of different ways. For instance, the user actuatable operator interface mechanisms can be actuated using operator interface mechanisms such as a point and click device, such as a track ball or mouse, hardware buttons, switches, a joystick or keyboard, thumb switches or thumb pads, etc., a virtual keyboard or other virtual actuators. In addition, where the screen on which the user actuatable operator interface mechanisms are displayed is a touch sensitive screen, the user actuatable operator interface mechanisms can be actuated using touch gestures. Also, user actuatable operator interface mechanisms can be actuated using speech commands using speech recognition functionality. Speech recognition may be implemented using a speech detection device, such as a microphone, and software that functions to recognize detected speech and execute commands based on the received speech.
• A number of data stores have also been discussed. It will be noted the data stores can each be broken into multiple data stores. In some examples, one or more of the data stores may be local to the systems accessing the data stores, one or more of the data stores may all be located remote form a system utilizing the data store, or one or more data stores may be local while others are remote. All of these configurations are contemplated by the present disclosure.
• Also, the figures show a number of blocks with functionality ascribed to each block. It will be noted that fewer blocks can be used to illustrate that the functionality ascribed to multiple different blocks is performed by fewer components. Also, more blocks can be used illustrating that the functionality may be distributed among more components. In different examples, some functionality may be added, and some may be removed.
• It will be noted that the above discussion has described a variety of different systems, components, logic, and interactions. It will be appreciated that any or all of such systems, components, logic and interactions may be implemented by hardware items, such as processors, memory, or other processing components, some of which are described below, that perform the functions associated with those systems, components, logic, or interactions. In addition, any or all of the systems, components, logic and interactions may be implemented by software that is loaded into a memory and is subsequently executed by a processor or server or other computing component, as described below. Any or all of the systems, components, logic and interactions may also be implemented by different combinations of hardware, software, firmware, etc., some examples of which are described below. These are some examples of different structures that may be used to implement any or all of the systems, components, logic and interactions described above. Other structures may be used as well.
• FIG. 15 is a block diagram ofagricultural harvester600, which may be similar toagricultural harvester100 shown inFIG. 2. Theagricultural harvester600 communicates with elements in aremote server architecture500. In some examples,remote server architecture500 provides computation, software, data access, and storage services that do not require end-user knowledge of the physical location or configuration of the system that delivers the services. In various examples, remote servers may deliver the services over a wide area network, such as the internet, using appropriate protocols. For instance, remote servers may deliver applications over a wide area network and may be accessible through a web browser or any other computing component. Software or components shown inFIG. 2 as well as data associated therewith, may be stored on servers at a remote location. The computing resources in a remote server environment may be consolidated at a remote data center location, or the computing resources may be dispersed to a plurality of remote data centers. Remote server infrastructures may deliver services through shared data centers, even though the services appear as a single point of access for the user. Thus, the components and functions described herein may be provided from a remote server at a remote location using a remote server architecture. Alternatively, the components and functions may be provided from a server, or the components and functions can be installed on client devices directly, or in other ways.
• In the example shown inFIG. 15, some items are similar to those shown inFIG. 2 and those items are similarly numbered.FIG. 15 specifically shows thatpredictive model generator210 orpredictive map generator212, or both, may be located at aserver location502 that is remote from theagricultural harvester600. Therefore, in the example shown inFIG. 15,agricultural harvester600 accesses systems throughremote server location502.
• FIG. 15 also depicts another example of a remote server architecture.FIG. 15 shows that some elements ofFIG. 2 may be disposed at aremote server location502 while others may be located elsewhere. By way of example,data store202 may be disposed at a location separate fromlocation502 and accessed via the remote server atlocation502. Regardless of where the elements are located, the elements can be accessed directly byagricultural harvester600 through a network such as a wide area network or a local area network; the elements can be hosted at a remote site by a service; or the elements can be provided as a service or accessed by a connection service that resides in a remote location. Also, data may be stored in any location, and the stored data may be accessed by, or forwarded to, operators, users, or systems. For instance, physical carriers may be used instead of, or in addition to, electromagnetic wave carriers. In some examples, where wireless telecommunication service coverage is poor or nonexistent, another machine, such as a fuel truck or other mobile machine or vehicle, may have an automated, semi-automated, or manual information collection system. As thecombine harvester600 comes close to the machine containing the information collection system, such as a fuel truck prior to fueling, the information collection system collects the information from thecombine harvester600 using any type of ad-hoc wireless connection. The collected information may then be forwarded to another network when the machine containing the received information reaches a location where wireless telecommunication service coverage or other wireless coverage—is available. For instance, a fuel truck may enter an area having wireless communication coverage when traveling to a location to fuel other machines or when at a main fuel storage location. All of these architectures are contemplated herein. Further, the information may be stored on theagricultural harvester600 until theagricultural harvester600 enters an area having wireless communication coverage. Theagricultural harvester600, itself, may send the information to another network.
• It will also be noted that the elements ofFIG. 2, or portions thereof, may be disposed on a wide variety of different devices. One or more of those devices may include an on-board computer, an electronic control unit, a display unit, a server, a desktop computer, a laptop computer, a tablet computer, or other mobile device, such as a palm top computer, a cell phone, a smart phone, a multimedia player, a personal digital assistant, etc.
• In some examples,remote server architecture500 may include cybersecurity measures. Without limitation, these measures may include encryption of data on storage devices, encryption of data sent between network nodes, authentication of people or processes accessing data, as well as the use of ledgers for recording metadata, data, data transfers, data accesses, and data transformations. In some examples, the ledgers may be distributed and immutable (e.g., implemented as blockchain).
• FIG. 16 is a simplified block diagram of one illustrative example of a handheld or mobile computing device that can be used as a user's or client's hand helddevice16, in which the present system (or parts of it) can be deployed. For instance, a mobile device can be deployed in the operator compartment ofagricultural harvester100 for use in generating, processing, or displaying the maps discussed above.FIGS. 17-18 are examples of handheld or mobile devices.
• FIG. 16 provides a general block diagram of the components of aclient device16 that can run some components shown inFIG. 2, that interacts with them, or both. In thedevice16, acommunications link13 is provided that allows the handheld device to communicate with other computing devices and under some examples provides a channel for receiving information automatically, such as by scanning. Examples of communications link13 include allowing communication though one or more communication protocols, such as wireless services used to provide cellular access to a network, as well as protocols that provide local wireless connections to networks.
• In other examples, applications can be received on a removable Secure Digital (SD) card that is connected to aninterface15.Interface15 andcommunication links13 communicate with a processor17 (which can also embody processors or servers from other FIGS.) along abus19 that is also connected tomemory21 and input/output (I/O)components23, as well asclock25 andlocation system27.
• I/O components23, in one example, are provided to facilitate input and output operations. I/O components23 for various examples of thedevice16 can include input components such as buttons, touch sensors, optical sensors, microphones, touch screens, proximity sensors, accelerometers, orientation sensors and output components such as a display device, a speaker, and or a printer port. Other I/O components23 can be used as well.
• Clock25 illustratively comprises a real time clock component that outputs a time and date. It can also, illustratively, provide timing functions forprocessor17.
• Location system27 illustratively includes a component that outputs a current geographical location ofdevice16. This can include, for instance, a global positioning system (GPS) receiver, a LORAN system, a dead reckoning system, a cellular triangulation system, or other positioning system.Location system27 can also include, for example, mapping software or navigation software that generates desired maps, navigation routes and other geographic functions.
• Memory21stores operating system29,network settings31,applications33, application configuration settings35,data store37,communication drivers39, andcommunication configuration settings41.Memory21 can include all types of tangible volatile and non-volatile computer-readable memory devices.Memory21 may also include computer storage media (described below).Memory21 stores computer readable instructions that, when executed byprocessor17, cause the processor to perform computer-implemented steps or functions according to the instructions.Processor17 may be activated by other components to facilitate their functionality as well.
• FIG. 17 shows one example in whichdevice16 is atablet computer600. InFIG. 17,computer600 is shown with userinterface display screen602.Screen602 can be a touch screen or a pen-enabled interface that receives inputs from a pen or stylus.Tablet computer600 may also use an on-screen virtual keyboard. Of course,computer600 might also be attached to a keyboard or other user input device through a suitable attachment mechanism, such as a wireless link or USB port, for instance.Computer600 may also illustratively receive voice inputs as well.
• FIG. 18 is similar toFIG. 8 except that the device is asmart phone71.Smart phone71 has a touchsensitive display73 that displays icons or tiles or otheruser input mechanisms75.Mechanisms75 can be used by a user to run applications, make calls, perform data transfer operations, etc. In general,smart phone71 is built on a mobile operating system and offers more advanced computing capability and connectivity than a feature phone.
• Note that other forms of thedevices16 are possible.
• FIG. 19 is one example of a computing environment in which elements ofFIG. 2 can be deployed. With reference toFIG. 19, an example system for implementing some embodiments includes a computing device in the form of acomputer810 programmed to operate as discussed above. Components ofcomputer810 may include, but are not limited to, a processing unit820 (which can comprise processors or servers from previous FIGS.), asystem memory830, and asystem bus821 that couples various system components including the system memory to theprocessing unit820. Thesystem bus821 may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. Memory and programs described with respect toFIG. 2 can be deployed in corresponding portions ofFIG. 19.
• Computer810 typically includes a variety of computer readable media. Computer readable media may be any available media that can be accessed bycomputer810 and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. Computer storage media is different from, and does not include, a modulated data signal or carrier wave. Computer readable media includes hardware storage media including both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed bycomputer810. Communication media may embody computer readable instructions, data structures, program modules or other data in a transport mechanism and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal.
• Thesystem memory830 includes computer storage media in the form of volatile and/or nonvolatile memory or both such as read only memory (ROM)831 and random access memory (RAM)832. A basic input/output system833 (BIOS), containing the basic routines that help to transfer information between elements withincomputer810, such as during start-up, is typically stored inROM831.RAM832 typically contains data or program modules or both that are immediately accessible to and/or presently being operated on by processingunit820. By way of example, and not limitation,FIG. 19 illustratesoperating system834,application programs835,other program modules836, andprogram data837.
• Thecomputer810 may also include other removable/non-removable volatile/nonvolatile computer storage media. By way of example only,FIG. 19 illustrates ahard disk drive841 that reads from or writes to non-removable, nonvolatile magnetic media, anoptical disk drive855, and nonvolatileoptical disk856. Thehard disk drive841 is typically connected to thesystem bus821 through a non-removable memory interface such asinterface840, andoptical disk drive855 are typically connected to thesystem bus821 by a removable memory interface, such asinterface850.
• Alternatively, or in addition, the functionality described herein can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, illustrative types of hardware logic components that can be used include Field-programmable Gate Arrays (FPGAs), Application-specific Integrated Circuits (e.g., ASICs), Application-specific Standard Products (e.g., ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.
• The drives and their associated computer storage media discussed above and illustrated inFIG. 19, provide storage of computer readable instructions, data structures, program modules and other data for thecomputer810. InFIG. 19, for example,hard disk drive841 is illustrated as storingoperating system844,application programs845,other program modules846, andprogram data847. Note that these components can either be the same as or different fromoperating system834,application programs835,other program modules836, andprogram data837.
• A user may enter commands and information into thecomputer810 through input devices such as akeyboard862, amicrophone863, and apointing device861, such as a mouse, trackball or touch pad. Other input devices (not shown) may include a joystick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to theprocessing unit820 through auser input interface860 that is coupled to the system bus, but may be connected by other interface and bus structures. Avisual display891 or other type of display device is also connected to thesystem bus821 via an interface, such as avideo interface890. In addition to the monitor, computers may also include other peripheral output devices such asspeakers897 andprinter896, which may be connected through an outputperipheral interface895.
• Thecomputer810 is operated in a networked environment using logical connections (such as a controller area network—CAN, local area network—LAN, or wide area network WAN) to one or more remote computers, such as aremote computer880.
• When used in a LAN networking environment, thecomputer810 is connected to theLAN871 through a network interface oradapter870. When used in a WAN networking environment, thecomputer810 typically includes amodem872 or other means for establishing communications over theWAN873, such as the Internet. In a networked environment, program modules may be stored in a remote memory storage device.FIG. 19 illustrates, for example, thatremote application programs885 can reside onremote computer880.
• It should also be noted that the different examples described herein can be combined in different ways. That is, parts of one or more examples can be combined with parts of one or more other examples. All of this is contemplated herein.
• Example 1 an agricultural work machine, comprising:
• a communication system that receives a map that includes values of a biomass characteristic corresponding to different geographic locations in a field;
• a geographic position sensor that detects a geographic location of the agricultural work machine;
• an in-situ sensor that detects a value of an agricultural characteristic corresponding to the geographic location;
• a predictive map generator that generates a functional predictive agricultural map of the field that maps predictive control values to the different geographic locations in the field based on the values of the biomass characteristic in the map and based on the value of the agricultural characteristic;
• a controllable subsystem; and
• a control system that generates a control signal to control the controllable subsystem based on the geographic location of the agricultural work machine and based on the control values in the functional predictive agricultural map.
• Example 2 is the agricultural work machine of any or all previous examples, wherein the map is a predictive biomass map generated based on values from a map and values of a biomass characteristic detected in-situ.
• Example 3 is the agricultural work machine of any or all previous examples, wherein the predictive map generator comprises:
• a predictive agricultural characteristic map generator that generates, as the functional predictive agricultural map, a functional predictive agricultural characteristic map that maps, as the predictive control values, predictive values of the agricultural characteristic to the different geographic locations in the field.
• Example 4 is the agricultural work machine of any or all previous examples, wherein the in-situ sensor detects, as the value of the agricultural characteristic, a value of an operator command indicative of a commanded action of the agricultural work machine.
• Example 5 is the agricultural work machine of any or all previous examples, wherein the predictive map generator comprises:
• a predictive operator command map that generates, as the functional predictive agricultural map, a functional predictive operator command map that maps, as the predictive control values, predictive operator command values to the different geographic locations in the field.
• Example 6 is the agricultural work machine of any or all previous examples, wherein the control system comprises:
• a settings controller that generates an operator command control signal indicative of an operator command based on the detected geographic location and the functional predictive operator command map and controls the controllable subsystem based on the operator command control signal to execute the operator command.
• Example 7 is the agricultural work machine of any or all previous examples, wherein the control system generates the control signal to control the control subsystem to adjust a feed rate of material through the agricultural work machine.
• Example 8 is the agricultural work machine of any or all previous examples and further comprising:
• a predictive model generator that generates a predictive agricultural model that models a relationship between the biomass characteristic and the agricultural characteristic based on a value of the biomass characteristic in the map at the geographic location and the value of the agricultural characteristic detected by the in-situ sensor corresponding to the geographic location, wherein the predictive map generator generates the functional predictive agricultural map based on the values of the biomass characteristic in the map and based on the predictive agricultural model.
• Example 9 is the agricultural work machine of any or all previous examples, wherein the control system further comprises:
• an operator interface controller that generates a user interface map representation of the functional predictive agricultural map, the user interface map representation comprising a field portion with one or more markers indicating the predictive control values at one or more geographic locations on the field portion.
• Example 10 is the agricultural work machine of any or all previous examples, wherein the operator interface controller generates the user interface map representation to include an interactive display portion that displays a value display portion indicative of a selected value, an interactive threshold display portion indicative of an action threshold, and an interactive action display portion indicative of a control action to be taken when one of the predictive control values satisfies the action threshold in relation to the selected value, the control system generating the control signal to control the controllable subsystem based on the control action.
• Example 11 is a computer implemented method of controlling an agricultural work machine comprising:
• obtaining a map that includes values of a biomass characteristic corresponding to different geographic locations in a field;
• detecting a geographic location of the agricultural work machine;
• detecting, with an in-situ sensor, a value of an agricultural characteristic corresponding to the geographic location;
• generating a functional predictive agricultural map of the field that maps predictive control values to the different geographic locations in the field based on the values of the biomass characteristic in the map and based on the value of the agricultural characteristic; and
• controlling a controllable subsystem based on the geographic location of the agricultural work machine and based on the control values in the functional predictive agricultural map.
• Example 12 is the computer implemented method of any or all previous examples, wherein obtaining the map comprises:
• obtaining a predictive biomass map that includes, as values of a biomass characteristic, predictive values of the biomass characteristic corresponding to different geographic locations in the field.
• Example 13 is the computer implemented method of any or all previous examples, wherein generating the functional predictive agricultural map comprises:
• generating a functional predictive agricultural characteristic map that maps, as the predictive control values, predictive agricultural characteristic values to the different geographic locations in the field.
• Example 14 is the computer implemented method of any or all previous examples, wherein detecting, with an in-situ sensor, the value of an agricultural characteristic comprises:
• detecting, with the in-situ sensor, as the value of the agricultural characteristic, an operator command indicative of a commanded action of the agricultural work machine.
• Example 15 is the computer implemented method of any or all previous examples, wherein generating the functional predictive agricultural map comprises:
• generating a functional predictive operator command map that maps, as the predictive control values, predictive operator command values to the different geographic locations in the field.
• Example 16 is the computer implemented method of any or all previous examples, wherein controlling the controllable subsystem comprises:
• generating an operator command control signal indicative of an operator command based on the detected geographic location and the functional predictive operator command map; and
• controlling the controllable subsystem based on the operator command control signal to execute the operator command.
• Example 17 is the computer implemented method of any or all previous examples, wherein controlling the controllable subsystem comprises:
• controlling the controllable subsystem to adjust a feed rate of material through the agricultural work machine.
• Example 18 is the computer implemented method of any or all previous examples and further comprising:
• generating a predictive agricultural model that models a relationship between the biomass characteristic and the agricultural characteristic based on a value of the biomass characteristic in the map at the geographic location and the value of the agricultural characteristic detected by the in-situ sensor corresponding to the geographic location, wherein generating the functional predictive agricultural map comprises generating the functional predictive agricultural map based on the values of the biomass characteristic in the map and based on the predictive agricultural model.
• Example 19 is an agricultural work machine comprising:
• a communication system that receives a map that includes values of a biomass characteristic corresponding to different geographic locations in a field;
• a geographic position sensor that detects a geographic location of the agricultural work machine;
• an in-situ sensor that detects a value of an agricultural characteristic corresponding to the geographic location;
• a predictive model generator that generates a predictive agricultural model that models a relationship between the biomass characteristic and the agricultural characteristic based on a value of the biomass characteristic in the map at the geographic location and the value of the agricultural characteristic detected by the in-situ sensor corresponding to the geographic location;
• a predictive map generator that generates a functional predictive agricultural map of the field that maps predictive control values to the different geographic locations in the field based on the values of the biomass characteristic in the map and based on the predictive agricultural model;
• a controllable subsystem; and
• a control system that generates a control signal to control the controllable subsystem based on the geographic position of the agricultural work machine and based on the control values in the functional predictive agricultural map.
• Example 20 is the agricultural work machine of any or all previous examples, wherein the control system comprises at least one of:
• a feed rate controller that generates a feed rate control signal based on the detected geographic location and the functional predictive agricultural map and controls the controllable subsystem based on the feed rate control signal to control a feed rate of material through the agricultural work machine;
• a settings controller that generates a speed control signal based on the detected geographic location and the functional predictive agricultural map and controls the controllable subsystem based on the speed control signal to control a speed of the agricultural work machine;
• a header controller that generates a header control signal based on the detected geographic location and the functional predictive agricultural map and controls the controllable subsystem based on the header control signal to control a distance of at least a portion of a header on the agricultural work machine from a surface of the field; and
• a settings controller that generates an operator command control signal indicative of an operator command based on the detected geographic location and the functional predictive agricultural map and controls the controllable subsystem based on the operator command control signal to execute the operator command.
• Although the subject matter has been described in language specific to structural features or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of the claims.

Claims (20)

1. An agricultural system comprising:

a communication system that receives a map that includes values of a biomass characteristic corresponding to different geographic locations in a field;

a geographic position sensor that detects a geographic location of an agricultural work machine;

an in-situ sensor that detects a value of an agricultural characteristic corresponding to the geographic location;

a predictive map generator that generates a functional predictive agricultural map of the field that maps predictive control values to the different geographic locations in the field based on the values of the biomass characteristic in the map and based on the value of the agricultural characteristic;

a control system that generates aa, control signal to control a controllable subsystem of the agricultural work machine based on the geographic location of the agricultural work machine and based on the control values in the functional predictive agricultural map.

2. The agricultural system ofclaim 1, wherein the map is a predictive biomass map generated based on values from a map and, values of a biomass characteristic detected in-situ.

3. The agricultural system ofclaim 1, in the predictive map generator comprises:

a predictive agricultural characteristic map generator that generates, as the functional predictive agricultural map, a functional predictive agricultural characteristic map that maps, as the predictive control values, predictive values of the agricultural characteristic to the different geographic locations in the field.

4. The agricultural system ofclaim 1, wherein the in-situ sensor detects, as the value of the agricultural characteristic, a value of an operator command indicative of a commanded action of the agricultural work machine.

5. The agricultural system ofclaim 4, wherein the predictive map generator comprises:

a predictive operator command map that generates, as the functional predictive agricultural map, a functional predictive operator command map that maps, as the predictive control values, predictive operator command values to the different geographic locations in the field.

6. The agricultural system ofclaim 5, wherein the control system comprises:

a settings controller that generates an operator command control signal indicative of an operator command based on the detected geographic location and the functional predictive operator command map and controls the controllable subsystem based on the operator command control signal to execute the operator command.

7. The agricultural system ofclaim 1, wherein the control system generates the control signal to control the controllable subsystem to adjust a feed rate of material through the agricultural work machine.

8. The agricultural system ofclaim 1 and further comprising:

a predictive model generator that generates a predictive agricultural model that models a relationship between the biomass characteristic and the agricultural characteristic based on a value of the biomass characteristic in the map at the geographic location and the value of the agricultural characteristic detected by the in-situ sensor corresponding to the geographic location, wherein the predictive map generator generates the functional predictive agricultural map based on the values of the biomass characteristic in the map and based on the predictive agricultural model.

9. The agricultural system ofclaim 1, wherein the control system further comprises:

an operator interface controller that generates a user interface map representation of the functional predictive agricultural map, the user interface map representation comprising a field portion with one or more markers indicating the predictive control values at one or more geographic locations on the field portion.

10. The agricultural system ofclaim 9, wherein the operator interface controller generates the user interface map representation to include an interactive display portion that displays a value display portion-indicative of a selected value, an interactive threshold display portion indicative of an action threshold, and an interactive action display portion indicative of a control action to be taken when one of the predictive control values satisfies the action threshold in relation to the selected value, the control system generating the control signal to control the controllable subsystem based on the control action.

11. A computer implemented method of controlling an agricultural work machine comprising:

obtaining a map that includes values of a biomass characteristic corresponding to different geographic locations in a field;

detecting a geographic location of the agricultural work machine;

detecting, with an in-situ sensor, a value of an agricultural characteristic corresponding to the geographic location;

generating a functional predictive agricultural map of the field that maps predictive control values to the different geographic locations in the field based on the values of the biomass characteristic in the map and based on the value of the agricultural characteristic; and

controlling a controllable subsystem based on the geographic location of the agricultural work machine and based on the control values in the functional predictive agricultural map.

12. The computer implemented method ofclaim 11, wherein obtaining the map comprises:

obtaining a predictive biomass map that includes, as values of a biomass characteristic, predictive values of the biomass characteristic corresponding to different geographic locations in the field.

13. The computer implemented method ofclaim 11, wherein generating the functional predictive agricultural map comprises:

generating a functional predictive agricultural characteristic map that maps, as the predictive control values, predictive agricultural characteristic values to the different geographic locations in the field.

14. The computer implemented method ofclaim 11, wherein detecting, with an in-situ sensor, the value of an agricultural characteristic comprises:

detecting, with the in-situ sensor, as the value of the agricultural characteristic, an operator command indicative of a commanded action of the agricultural work machine.

15. The computer implemented method ofclaim 14, wherein generating the functional predictive agricultural map comprises:

generating a functional predictive operator command map that maps, as the predictive control values, predictive operator command values to the different geographic locations in the field.

16. The computer implemented method ofclaim 15, wherein controlling the controllable subsystem comprises:

generating an operator command control signal indicative of an operator command based on the detected geographic location and the functional predictive operator command map; and

controlling the controllable subsystem based on the operator command control signal to execute the operator command.

17. The computer implemented method ofclaim 11, wherein controlling the controllable subsystem comprises:

controlling the controllable subsystem to adjust a feed rate of material through the agricultural work machine.

18. The computer implemented method ofclaim 11 and further comprising:

generating a predictive agricultural model that models a relationship between the biomass characteristic and the agricultural characteristic based on a value of the biomass characteristic in the map at the geographic location and the value of the agricultural characteristic detected by the in-situ sensor corresponding to the geographic location, wherein generating the functional predictive agricultural map comprises generating the functional predictive agricultural map based on the values of the biomass characteristic in the map and based on the predictive agricultural model.

19. An agricultural system comprising:

a communication system that receives a map that includes values of a biomass characteristic corresponding to different geographic locations in a field;

a geographic position sensor that detects a geographic location of an agricultural work machine;

an in-situ sensor that detects a value of an agricultural characteristic corresponding to the geographic location;

a predictive model generator that generates a predictive agricultural model that models a relationship between the biomass characteristic and the agricultural characteristic based on a value of the biomass characteristic in the map at the geographic location and the value of the agricultural characteristic detected by the in-situ sensor corresponding to the geographic location;

a predictive map generator that generates a functional predictive agricultural map of the field that maps predictive control values to the different geographic locations in the field based on the values of the biomass characteristic in the map and based on the predictive agricultural model;

a control system that generates a control signal to control a controllable subsystem of the agricultural work machine based on the geographic position of the agricultural work machine and based on the control values in the functional predictive agricultural map.

20. The agricultural system ofclaim 19, wherein the control system comprises at least one of:

a feed rate controller that generates a feed rate control signal based on the detected geographic location and the functional predictive agricultural map and controls the controllable subsystem based on the feed rate control signal to control a feed rate of material through the agricultural work machine;

a settings controller that generates a speed control signal based on the detected geographic location and the functional predictive agricultural map and controls the controllable subsystem based on the speed control signal to control a speed of the agricultural work machine;

a header controller that generates a header control signal based on the detected geographic location and the functional predictive agricultural map and controls the controllable subsystem based on the header control signal to control a distance of at least a portion of a header on the agricultural work machine from a surface of the field; and

a settings controller that generates an operator command control signal indicative of an operator command based on the detected geographic location and the functional predictive agricultural map and controls the controllable subsystem based on the operator command control signal to execute the operator command.

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