US20200045873A1 - Machine Control System Providing Actionable Management Information and Insight Using Agricultural Telematics - Google Patents

Abstract

A machine control system includes an agricultural work machine having an ECU coupled via a system bus to control engine functions, a GPS receiver, data collector, and specialized guidance system including a stored program. The data collector captures agricultural geospatial data including location data for the work machine and data from the ECU, and executes the stored program to: (a) capture geometries of the farm; (b) capture agricultural geospatial data; (c) automatically classify the agricultural geospatial data using the geometries of the farm, into activity/event categories including operational, travel, and ancillary events; (d) aggregate the classified data to create geospatial data events; (e) match the geospatial data events to a model to generate matched events; (f) use the matched events to generate actionable information for the working machine in real time or near real-time; and (g) send operational directives to the agricultural work machine based on the actionable information.

Description

RELATED APPLICATION
• This application is a Continuation to U.S. patent application Ser. No. 16/043,229 filed Jul. 24, 2108, which claims the benefit of U.S. Provisional Patent Application Ser. No. 62/536,026, entitled Actionable Management Information and Insight System for Agricultural Telematics (AMIISAT), filed on Jul. 24, 2017, and is a Continuation-In-Part of U.S. patent application Ser. No. 15/490,791, entitled Machine Guidance for Optimal Working Direction of Travel, filed on Apr. 18, 2017, the contents of which are incorporated herein by reference in their entirety for all purposes.
BACKGROUNDTechnical Field
• This invention relates to a machine control system for agricultural equipment, and more specifically, to a system and method that uses real-time, or near real-time, adaptive analysis to provide actionable management information and operational directives to agricultural machines using agricultural geospatial data.
Background Information
• Actionable information and insights to anyone associated with performance metrics on an operating farm, including equipment operators, farm managers, and farm owners, is vital in order for the best management decisions to be made. Not only do operating farms have decisions to make, but sectors such as the agricultural retail sector of the industry also have to manage decisions involving equipment performance metrics. A major source of difficulty in this management can be attributed to properly managing the associated operating equipment and the corresponding data that it can generate. Equipment logistics, usage, and cost, as well as operating metrics like machine data, agronomic data, and monitor/controller data are just a few examples of parameters that may need to be evaluated in order to help with those decisions. The collection and analysis of this geospatial data can also take many man hours and be computationally expensive in current farm management information systems.
• The current collection and analysis process of said geospatial data usually includes first collecting the data from a multitude of different sources, e.g., from a portable storage medium such as a USB flash drive or an external hard drive, cloud/web application data services, data storage from databases, as well as directly from collection devices. These data sources have historically collected and stored the data from the incoming data streams so that further processing may be done at a later time. The data sources are also usually located in many different places, both physically and in terms of connected networks, and therefore are not directly part of a centralized system. They are also, very often, unstandardized in terms of the type of data that is stored. Stored data types for agricultural data may include field boundary data, agronomic data, machine, agronomic, or monitor/controller telematics data, as well as farm equipment information which may include, equipment financial information, equipment configurations, and the various potential activities/uses of said equipment. These different data types are often also stored in different data formats corresponding to different file types. The case often exists where even different data formats and file types may occur for the same data type if the data originates from different collection sources. Due to this, the incoming data must be retrieved, standardized, and merged in order to provide relevance. In order to provide this sort of context, which also helps to make the data actionable, the data must first be organized to determine which of the data is performing operational work. The non-operational category is any data that is not considered to be performing work on a given geospatial geometry of the farm. After the data has been classified, it must then be analyzed in order to create enough significant context in order for actionable information and insights to be generated. Once this context has been generated, further farm models, capacity, and financial business logic may be applied to help extend the information and insights provided.
• The actual analysis process used to classify, assign, and aggregate the data is often rigorous and manual by nature, but must occur in order to provide the necessary context for accurate analysis. Processing the large amount of telematics data along with said equipment configurations, and the various potential activities/uses of said equipment for all geospatial events, over all geospatial geometries, for all potential scenarios becomes extremely expensive in terms of computational power, time, and efficiency. This process, then, only becomes more complicated and expensive when data must be processed for multiple farms or agricultural retail operations.
• Not only is the current process computationally expensive and inefficient, but the tools and skills required to the complete the analysis are often numerous. Tools such as geographic information systems (GIS) software, data processing software, and data visualization software are required to complete the analysis from start to finish. With the increasing complexity in farm management situations and as the amount of data and potential scenarios increase, it can make these tools very time consuming to use. The skillset needed to operate these different software packages along with the skillset to physically move the data from one to another is also one that not all equipment operators, farm managers, or farm owners possess. Due to this, it could potentially leave these operators, managers, and/or owners at a competitive disadvantage when it comes to making decisions with the data.
• Therefore, a need has been shown for a system and process that addresses and improves upon the aforementioned issues.
SUMMARY OF THE INVENTION
• The appended claims may serve as a summary of the invention. The features and advantages described herein are not all-inclusive and various embodiments may include some, none, or all of the enumerated advantages. Additionally, many additional features and advantages will be apparent to one of ordinary skill in the art in view of the drawings, specification, and claims. Moreover, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and not to limit the scope of the inventive subject matter.
BRIEF DESCRIPTION OF THE DRAWINGS
• The present invention is illustrated by way of example and not limitation in the figures of the accompanying drawings, in which like references indicate similar elements and in which:
• FIG. 1 illustrates a diagram of agricultural geospatial data collection and transfer through the use of a cloud enabled network to a guidance system, which contains an embedded remote server-client architecture, coming from agricultural equipment with data collection capabilities, in accordance with embodiments of the present invention.
• FIG. 2 illustrates a simplified diagram of an equipment system data transfer bus that resides on typical agricultural equipment to allow for data collection, transfer, and control.
• FIG. 3 illustrates a system of the collection and transfer of agricultural geospatial data from a group of agricultural equipment as well as the process and method of the generation of actionable management information and insights.
• FIG. 4 illustrates a diagram displaying a collection of geospatial geometries.
• FIG. 5 illustrates a diagram displaying agricultural equipment performing field operation events on multiple geospatial geometries while transmitting agricultural geospatial data to a cloud enabled network.
• FIG. 6 is a detailed block diagram of the server.
• FIG. 7 is a detailed block diagram of the adaptive data analysis algorithm which details the creation of the summarized agricultural geospatial data events.
• FIG. 8 is an example of the summarized agricultural geospatial data events for a field boundary.
• FIG. 9 is block diagram of the client.
• FIG. 10 is a block diagram of the agricultural operations model.
• FIG. 11 is an example of the planned chronological list of equipment operation events for a field boundary.
• FIG. 12 is a detailed block diagram of the dynamic classification/matching algorithm that details the generation of actionable management information and insights.
• FIG. 13 is an example of actionable management information generated by embodiments of the present invention in terms of the percentage of time spent at 0 mph for operation events.
• FIG. 14 is an example of actionable management information generated by embodiments of the present invention in terms of a $/acre cost breakdown of operation events.
• FIG. 15 shows an example of actionable management insights generated by embodiments of the present invention in terms of relating planting and harvesting speed to potential savings by increasing the average operation speed.
• FIG. 16 is a block diagram of an exemplary computer usable in aspects of embodiments of the present invention.
DETAILED DESCRIPTION OF THE INVENTION
• It should be understood at the outset that, although exemplary embodiments are illustrated in the figures and described below, the principles of the present disclosure may be implemented using any number of techniques, whether currently known or not. The present disclosure should in no way be limited to the exemplary implementations and techniques illustrated in the drawings and described below. Additionally, unless otherwise specifically noted, articles depicted in the drawings are not necessarily drawn to scale. In addition, well-known structures, circuits and techniques have not been shown in detail in order not to obscure the understanding of this description. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined by the appended claims and their equivalents.
General Overview
• An aspect of the present invention was the realization by the instant inventors that without the association of the data to the proper geospatial geometry (field boundary or significant location) as well as to the geospatial event (operation), the use of the data is limited as to the information and insights that it is able to provide. It was further recognized that in order to make these assignments, knowledge of the location of the geospatial geometries, the equipment and their configurations, and temporal aggregation techniques must be known. As many farms contain a plurality of geospatial geometries, as well as many geospatial events that occur at or on each of these geospatial geometries, the classification, assignment, and aggregation process tends to be very complicated. That combined with the potential for an extremely large amount of incoming data, from different sources, can also make efficient use of such data extremely difficult.
• The instant inventors recognized that in agriculture, a majority of the time equipment is driven from work location to work location, such as from field to field, to perform operational work. During this travel period, extra time, distance, and fuel, just to name a few, are being accrued which affects the logistics, the overall farming operation, and its associated costs. This is especially true in cases such as farms with fields that are spread out over a large area, or pieces of equipment like a self-propelled sprayer that may make many trips to fields throughout the growing season. Without analyzing travel data, and the associated time and money that it attributes to the equipment and the total farming operation, a complete picture of equipment usage and its effects cannot be seen. This incomplete picture of equipment usage could potentially lead to uninformed management decisions. The same holds true with any geospatial data resulting as ancillary to the operational or travel data. The time and money accrued when the equipment is active in the barnyard, for example, must also be accounted for in order to obtain a comprehensive look at all equipment activities and their costs.
• The prior art known to the present inventors does not focus on the classification of agricultural geospatial data in an adaptive manner. As used herein, the term “adaptive” refers to the concept that the geospatial data itself, using its own measured parameters, provides a basis for the classification outcome. The known prior art also fails to discuss dynamically classifying/matching summarized data events to an agricultural operations model. The instant inventors further recognized that classifying summarized geospatial data events to an operations model, e.g., matching actual operation events to planned operation events, or assigning operation information to operation events, starts turning the data into something actionable. As operation type, and individual operations, are categories within which farms make decisions, having information in these terms may prove to be a management advantage. This, coupled with dynamically re-classifying and re-evaluating the data, as any portion of the agricultural operation model changes, to provide substantially real-time or near real-time results, may provide an immediate advantage to the farming operation. In many applications, these aspects may improve the quality of the information and insights generated, the actionable decisions made, and ultimately the overall performance and bottom line of the farming operation as a whole.
• Therefore, the inventive embodiments discussed hereinbelow provide a system and method that generates actionable management information, insights, and operational directives from telematics and agricultural geospatial data through a passive, automated, adaptive, and dynamic process.
• More specifically, these embodiments provide for: the passive collection and transfer of agricultural geospatial data, via telemetry, from active agricultural equipment; the automatic and adaptive classification of collected geospatial data into aggregated operational, travel, and ancillary data events; the summarization of said aggregated operational, travel, and ancillary data events; the methods of dynamically classifying/matching the summarized data events to an agricultural operations model for the dynamic generation of actionable management information and insights in real-time, in-season, and/or historically; and the transfer of this information to an agricultural machine in the form of operational directives.
• As discussed in greater detail hereinbelow, agricultural geospatial data may be collected from a collection device that is communicably coupled to an equipment system bus for the gathering of data that is being communicated on the equipment's functional systems. This data may then be transmitted, via telemetry, to a cloud enabled network, and then to a server for analysis in the system. The agricultural geospatial data may also be collected from sources that are not directly within the centralized analysis system but have already stored raw data, such as geospatial data databases, or external storage media such as a USB flash drive or external hard drive. These data sources may also be accessed so that both incoming data collected from collection devices and external sources may be analyzed with the adaptive data analysis algorithm. The algorithm may adaptively classify the agricultural geospatial data into operational, travel, or ancillary categories as the data is arriving into the system for effective processing and storage of the incoming data, or after the data has been stored. The algorithm also assigns the classified data to a geospatial geometry, such as a field boundary or a significant location to the farming operation. The algorithm is then able to aggregate and summarize the classified and assigned data in order to create summarized agricultural geospatial data events for each of the three classifications and for all known geospatial geometries. This adaptive classification analysis may be completed with only the help of the geospatially located geometries and the agricultural geospatial data itself, which contain the necessary information for classification. Through a geospatial relation of the agricultural geospatial data and the geospatial geometries, as well as parameters from the agricultural geospatial data, classification to operational, travel, and ancillary activities may occur. In this way field boundaries and/or significant locations associated with the farm may be evaluated. A temporal analysis may then be completed within the algorithm to aggregate the classified agricultural geospatial data and summarize the results in order for the generation of summarized geospatial data events.
• The agricultural geospatial data events may also be transferred, via a communication network, to a client for further processing in a dynamic classification algorithm. This algorithm may use the help of an agricultural operations model, as well as capacity, financial, and/or business logic to generate further information and insights on the contextualized geospatial data. Management categories such as, business farm entities or clients, land ownership entities or farms, fields, operation event types, specific operation events, and/or specific equipment may be used with information and insights generated from the geospatial data to help make actionable management decisions on the farm.
• In particular embodiments, the agricultural operations model also provides a chronological list of operation events that have been pre-planned and contain similar summarization characteristics, including cost of operation parameter characteristics. These summarized operating characteristics may then be used to classify the agricultural geospatial data events into either an event that matches an event in the planned list of events, one that doesn't match any in the planned list of events, or one that is not found in the planned list of events. These insights and information may then also be used by farm managers to help them make actionable management decisions on the farm.
Terminology
• As used in the specification and in the appended claims, the singular forms “a”, “an”, and “the” include plural referents unless the context clearly indicates otherwise. For example, reference to “an analyzer” includes a plurality of such analyzers. In another example, reference to “an analysis” includes a plurality of such analyses.
• Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation. All terms, including technical and scientific terms, as used herein, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs unless a term has been otherwise defined. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning as commonly understood by a person having ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and the present disclosure. Such commonly used terms will not be interpreted in an idealized or overly formal sense unless the disclosure herein expressly so defines otherwise.
• Where used in this disclosure, the term “computer” is meant to encompass a workstation, personal computer, personal digital assistant (PDA), wireless telephone, or any other suitable computing device including a processor, a computer readable medium upon which computer readable program code (including instructions and/or data) may be disposed, and a user interface. Terms such as “server” and “client”, and the like are intended to refer to a computer-related entity, including hardware or a combination of hardware and, software. For example, an engine may be, but is not limited to being: a process running on a processor; a processor including an object, an executable, a thread of execution, and/or program; and a computer. Moreover, the various computer-related entities may be localized on one computer and/or distributed between two or more computers. The terms “real-time” and “on-demand” refer to sensing and responding to external events nearly simultaneously (e.g., within seconds, milliseconds, or microseconds) with their occurrence, or without intentional delay, given the processing limitations of the system and the time required to accurately respond to the inputs. The terms “near real-time” and “near on-demand” also refer to the sensing and responding to external events that may be close to simultaneous, or near simultaneous, (e.g., within hours, minutes) with their occurrence, or without intentional delay, given the processing limitations and data flow capacity of the system to accurately respond to the inputs. The terms “real-time” and “near real-time” depend on the system and the capacity provided to the system for processing and data movement. They should in no way limit the scope in which the invention is presented herein.
Programming Languages
• Embodiments of the present invention can be programmed in any suitable language and technology, such as, but not limited to: Assembly Languages, C, C++; C #; Python; Visual Basic; Java; VBScript; Jscript; Node.js; BCMAscript; DHTM1; XML and CGI. Alternative versions may be developed using other programming languages including, Hypertext Markup Language (HTML), Active ServerPages (ASP) and Javascript. Any suitable database technology can be employed, such as, but not limited to, Microsoft SQL Server or IBM AS400, as well as big data and NoSQL technologies, such as, but not limited to, Hadoop or Microsoft Azure. Referring now to the attached Figures, embodiments of the present invention will be more thoroughly described.
• FIG. 1 shows acentralized system100 that illustrates the passive agricultural geospatial data collection and transfer for the generation of actionable management information and insights, which in particular embodiments, includes awork machine101 communicably coupled via a cloud enablednetwork110, to aguidance system112, e.g., in aserver111/client113 architecture as shown. Thesystem100 containsagricultural equipment101 with anequipment system bus102, e.g., in the form of a conventional Controller Area Network (CAN) bus, that allows interaction with the different systems on-board that control, operate, and monitor the equipment. Also connected to theequipment system bus102 are aGPS receiver system104, adata collection device105, and a wireless datatransfer device system106. AGPS receiver system104 connected to theequipment bus system102 allows forGPS signals108 being transmitted viaGPS satellites107 to be received and sent to theequipment bus102 for GPS-based positioning control of the equipment. TheGPS receiver system104 also allows for theGPS signal data108 to be combined/matched with the equipmentsystem bus data103 which creates agriculturalgeospatial data109, that may contain but should not be limited to, GPS positioning data, temporal data, machine and equipment data, agronomic data, monitor/controller data, or any other equipment sensor data. The passive agricultural geospatialdata collection device105 may be connected wirelessly or through a physical connection to theequipment system bus102 as well as to the datatransfer device system106. The passive agricultural geospatialdata collection device105 may monitor theequipment system bus102 without interfering with the operation of theequipment system bus102 while also measuring the agriculturalgeospatial data109 that is transmitted to the different systems. The agriculturalgeospatial data109 may then be transferred, via thetransfer device system106, to a cloud enablednetwork110. Once the agriculturalgeospatial data109 has been transferred to the cloud enablednetwork110, thedata109 can then be transferred to aserver111 in which an adaptive data analysis algorithm can be completed. The result of the adaptive data analysis algorithm can then be sent, via acommunication network110, to aclient113 for the dynamic classification algorithm to be completed and actionable management information and insights to be generated.
• In alternate embodiments of thedata collection device105, thedevice105 may both monitor theequipment system bus102 and measure the equipmentsystem bus data103 without theGPS signal data108. Thedata collection device105 may contain aGPS receiver system104 within thedevice system105 so thatGPS signal data108 may be directly measured by thedata collection device105 and combined/matched with the equipmentsystem bus data103 at thedata collection device105 before transfer of the agriculturalgeospatial data109 by thetransfer device system106 occurs.Data collection device105 may also contain thetransfer device system106 within itself, so that agriculturalgeospatial data109 may be collected and transferred by thesame device105. Thedata collection device105 may also have the ability to interact with theequipment system bus102 to control specific systems residing on the bus for input from external controlling systems. It should also be recognized that a combination of this system may have not been discussed within this explanation, but known that an alternate embodiment of the description may be possible while still representing the concepts and figures presented herein.
• In another alternate embodiment, the data source that is connected to the cloud enablednetwork110 and to theserver111 may not come directly from thedata collection device105 on theagricultural equipment101. The agriculturalgeospatial data109 may be from a multitude of different sources that have historically collected agriculturalgeospatial data109 or are connected via cloud/web application data services. These sources may include, for example, portable storage media such as USB flash drives or external hard drives, web application data services, and data storage databases. In various embodiments of potential agriculturalgeospatial data109 storage, a connection to theserver111, through a cloud enablednetwork110 or physical connection, is made and becomes a part of thecentralized system100 shown.
• FIG. 1 also shows just an example of a simplified, and specific, embodiment of anagricultural telematics system100 that is based on a client-server architecture. This by no means, should limit the concepts herein, as other potential embodiments of thistelematics system100 may include configurations of, just a server for all processing, just a client for all processing, a combined server-client unit, or any type of architecture that allows the processing and analyzing of data to flow fromagricultural equipment101 to farm managers.
• FIG. 2, then, shows anequipment system bus102 with a moredetailed level200 of the on-board systems associated withagricultural equipment101. The system contains acentralized data bus102 that allows the transfer ofdata103109 to and from different systems contained on theagricultural equipment101. Theequipment system bus102 may be one, or a combination of data buses that should not be limited to, a controller area network (CAN) like CAN bus and ISO bus, Local Interconnect Network (LIN), Ethernet, Transmission Control Protocol (TCP)/Internet Protocol (IP), RS232, CCD, Universal Serial Bus (USB) or any other connection between the equipment operating systems that can transfer data back and forth to the controlling systems.
• Systems such as theengine201,transmission202, electrical203, hydraulic204,hitch205, power take-off (PTO)206, and the monitor/controller system207, as well as thevirtual terminal208, any implementECU systems209, implementsensor systems210, and implementcontroller systems211 are examples of what may be connected to thecentralized equipment bus102 but in no way should be limited to only these. The functionality of thisdata bus102 allows fordata103109 flow between systems so proper functionality of theagricultural equipment101 may occur. Thedata103109 passed through thisbus102 can originate or be received from the different systems through a standard data flow protocol foragricultural equipment101. It also provides the opportunity for adata collection device105 to be used to gather thegeospatial data109, or just the equipmentsystem bus data103, and then send it to a cloud enablednetwork110 using adata transfer device106 for further analysis. The data collected103109 may comprise of information from one or multiple systems provided in200 as well as systems that may not be visualized within this diagram. Thedata collection device105, once again, may be a passive non-intrusive device that connects to existing systems or directly to anequipment system bus102 to record information of the equipment operating parameters, as well as a device that can provide input to theequipment system bus102 for control of specific systems within or on the bus.
• FIG. 3 shows this collection in a simplified diagram of asystem300 that includes multiple pieces ofagricultural equipment101 ofmultiple types301, including substantially any type of work machine operating substantially any type of farm implement. A representative (non-exclusive) list of work machines usable with these embodiments includes: Tractors, Combines, Powered Applicators (sprayers, spreaders, floaters), Bulk Harvesters (self-propelled forage harvesters, cotton harvesters, sugar beet harvesters, etc.), Self-Propelled Windrowers and Swathers, Skid-Steer Loaders, Semi-Trucks, Utility Vehicles, and Passenger Vehicles (for farm use—such as pickup trucks). A representative (non-exclusive) list of farm implements (tools) usable with these embodiments includes: Tillage Tools, Planters, Air Seeders, Drills, Balers, Grain Carts and Bulk Storage Carts, Forage Preparation Tools (Conditioners, Rakes, Tedders, etc.), Pull-type Applicators (pull type fertilizer spreaders or sprayers), Trailers/Wagons/Seed Tenders, Pull-type Harvesters (forage harvesters that are pulled), and Manure Spreaders.
• FIG. 3 also shows the flow of agriculturalgeospatial data109 from collection to the generation of actionable management information and insights (also referred to herein as “management insight(s)”, “actionable insight”, or simply “actionable information”)308. It should be noted thatsystem100 may automatically determine the type of equipment or implement being used, based on the data captured bydata collection device105, and/or by use of an implementsensor115 communicably coupled todevice105.FIG. 3 further shows thetransfer309 of this actionable information andinsights308 to the farm managers/workingmachine operators310, which may take the form of information displays as well as operational directives sent bysystem100. As discussed in detail hereinbelow, examples of actionable information include the amount of time the working machine was idle during performance of various geospatial events, the costs per acre associated with various geospatial events, correlation of speed of working machine to costs associated with various geospatial events, cost savings per acre as a function of speed of the working machine, and combinations thereof. An exemplary operational directive includes instructions to increase or decrease the speed of the working machine to match an optimal speed generated by the model for a particular geospatial event. Other exemplary operational directives may include, but should not be limited to: instructions to shift gears of a machine and reduce engine speed through throttle adjustment of the engine in order to reduce fuel use rate, to reduce fuel cost for a particular geospatial event; instructions to optimize field efficiency of a particular geospatial event by providing instructions on obtaining an optimal work direction of travel within the field boundary, such as shown and described in the above-referenced U.S. patent application Ser. No. 15/490,791; or providing instructions to turn the machine off during non-productive times to reduce fuel cost and cost of operation when the machine is not being productive. As these operational directives have been explicitly stated, it should be known to those skilled in the art that this is only a small subset of the potential operational directives that could be generated for machine control for a particular geospatial event. For example, parameters such as engine speed, engine load, distance travelled, fuel usage, as well as other machine, agronomic, or monitor/controller data may be used to generate these operational directives.
• In this regard, embodiments ofsystem100 are configured to automatically determine the particular geospatial event being performed, and to provide the operational directive, in real-time, or near real-time. Thegeospatial data109 collected from the set ofagricultural equipment301 is connected to the cloud enablednetwork110 that is connected to aserver111. Thisserver111 contains adatabase302 of the agricultural geospatial data that is collected from thedata collection device105, e.g., including location data captured fromGPS104, and operational data captured from the CAN bus of thework machine101, as well as adatabase303 that includes geospatially located geometries, e.g., field geometries, for the operating farm. Theserver111 also contains an adaptivedata analysis algorithm304 that processes and analyzes the agriculturalgeospatial data109 from the telematicsgeospatial data database302 using the geospatially locatedgeometries database303, as a reference, to create the summarized agriculturalgeospatial data events305 for the operational, travel, and ancillary categories. These summarizeddata events305 may then be transferred, via acommunication network110, to aclient113 for further processing.
• In embodiments, thecommunication network110 may be, but should not be limited to, a wireless or wired network, that may include networks such as, Local Area Network (LAN), Wide Area Network (WAN), the Internet, an Ethernet connection, Universal Serial Bus (USB), Wi-Fi, or Bluetooth for example. Moreover, theclient113, in this embodiment, may include any device that contains a processor and or a means of viewing information, for example, but should not be limited to, devices such as, a laptop or desktop computer, a smartphone, or a tablet.
• In this specific embodiment, as depicted inFIG. 3, theclient113 receives the summarized agriculturalgeospatial data events305 which may then be dynamically classified, i.e., matched, using thedynamic classification algorithm307 in relation to anagricultural operations model306. As thedynamic classification algorithm307 classifies the actualgeospatial data events305 thealgorithm307 also generates actionable management information andinsights308 that may then be transferred, via atransfer method309, to thefarm managers310 for quick and easy access in order to help make timely and efficient management decisions.
• As an alternate embodiment ofFIG. 3, and specifically ofserver111, the agriculturalgeospatial data109 may also be transferred via the cloud enablednetwork110 directly to the adaptivedata analysis algorithm304 for analysis before storage in the agricultural telematicsgeospatial database302. In such an embodiment, agriculturalgeospatial data109 may once again come from a multitude of external sources and/oragricultural equipment101.
• A factor in evaluating agriculturalgeospatial data109 is not only being able to associate thegeospatial data109 with the properagricultural equipment101 but also to associate thatequipment101 with its geospatial location in regards to geospatial geometries such as, field boundaries and significant locations associated with a farm.FIG. 4 provides a visualization of multiplegeospatial field boundaries401, along with a significant location that has been marked by the farm and is designated as402.Significant locations402 may represent, but should not be limited to, staging areas, storage areas, or any location that has been marked as geospatially significant to the farm operation in one way or another. Collectively these geometries create a set ofgeospatial data geometries400 that can be referenced as a whole farm. This set ofgeometries400 play a role in being able to properly locate, attribute, and adaptively classify thegeospatial data109 collected fromequipment101 into operational data offield boundary401, travel to or fromfield boundary401 orlocation402, or into ancillary data offield boundary401 orlocation402, for example. Thesegeometries400 are captured bysystem100, such as by uploading a computer file, such as a map, containing thegeometries400 therein. Alternatively,system100 may use theGPS receiver104 to capture GPS coordinates of the boundaries and topographical features of a field as theagricultural work machine101 traverses the field, e.g., as the machine circumnavigates the field(s).
• FIG. 5 also shows the farm with the set ofgeospatial geometries400 but now with equipment operation on thosefield boundaries500. The work operations thatequipment101_{0 to n}are performing may all occur at the same time, individually, or in any combination of occurrence in regards to time and location.FIG. 5 also shows the data collection of theoperating equipment101 in all of thefield boundaries401_{0 to n}transferring each equipment's101 individual agriculturalgeospatial data109 to the cloud enablednetwork110 for further processing.
• This further processing may occur on theserver111 and can be shown in greater detail in the block diagram inFIG. 6. Theserver system111 contains the agricultural telematicsgeospatial data database302, thegeospatial geometry database303, aprocessor601 that may be able to perform algorithmic computer code/instructions, which contain the adaptivedata analysis algorithm304, and the summarized agriculturalgeospatial data events305 that are created from the adaptivedata analysis algorithm304. Within theprocessor601, the agriculturalgeospatial data109 from thedatabase302 along with the geospatially locatedgeometries303 flow together into the agricultural data self-classification algorithm602 so the agriculturalgeospatial data109 may be parsed into operational, travel, and ancillary classifications for everyfield401 and/orlocation402. The data can then be aggregated and summarized in the geospatial dataevent creation algorithm603 in order for thedata events305 of everyfield boundary401 and/orlocation402 to be created.
• In an alternate embodiment ofserver111, agriculturalgeospatial data109 may be directly fed into the adaptivedata analysis algorithm304 from thecommunication network110. In this way the agricultural data would flow just with thegeospatial geometries303 into the self-classification algorithm602 e.g., without first being stored indatabases302,303. Theincoming data109 may then again be parsed and classified into operational, travel, and ancillary categories for all geospatial geometries stored in303. At this point, the classified data may then be stored inagricultural database302. Other alternate embodiments ofserver111, with a restructured path of data flow and storage may also be recognized by those skilled in the art. While the location of data flow and storage may differ along the data pipeline than what is shown in the figures or otherwise presented herein, it should not limit the scope of the invention as set forth in the claims hereof.
• In particular embodiments, this process can yet further be defined inFIG. 7 which shows a more detailed block diagram of the adaptivedata analysis algorithm304. It contains the methods of the agricultural data self-classification algorithm602 to adaptively analyze the agriculturalgeospatial data109 from the agriculturalgeospatial database302. Instep701 the agricultural geospatial data is parsed by the geospatially located geometries400 (field boundaries401 or locations402) provided from thedatabase303 in order to develop the geospatial relationship between the location of theagricultural data109 and the location of thegeospatial geometries400. This relationship is developed by comparing the location of the agriculturalgeospatial data109 with the location of thegeospatial geometries400. The comparison of location determines if thedata109 lies within the geospatial boundary of one or multiple of the geometries in theset400, if thedata109 resides on the outside of all geospatial boundaries of theset400, or if thedata109 intersects the geospatial boundary of one or multiple geometries in theset400. As the relationship analysis between the agriculturalgeospatial data109 and thegeospatial geometries400 is completed, a quantitative analysis is also being performed to quantify the geospatial distance between said agriculturalgeospatial data109 and thegeospatial geometries400 for each said relationship that is developed.
• After these geospatial relationships have been developed, the data then self-classifies itself into operational, travel, or ancillary categories instep702. In this step the data, itself, again provides one of its collected parameters, which is the moving speed of the equipment, in order to relate the agriculturalgeospatial data109 to an operating speed range and/or a travel speed range. Using the geospatial relationship, as well as the quantified geospatial distance, developed instep701, along with the speed of movement relationship developed in702, the agricultural geospatial data can self-classify itself into said categories. The relationships that thedata109 provides also provide the information to assign the agriculturalgeospatial data109 to the propergeospatial geometry401402 in the set ofgeometries400 for the given geometries located indatabase303.
• Agricultural geospatial data relationships developed fromstep702 may include, for example, self-classification of the operational data category if the data resides in the boundary of ageospatial geometry401 and the speed of the equipment is within the operating speed range, or self-classification of the travel data category if the data is outside of thegeospatial geometry400 and the speed of the equipment is within the travel speed range. These two examples of relationships, are discussed here to represent what the relationships may contain and in no way should limit the scope by which these relationships are built. It is known that many other relationships are common and possible but are just not discussed herein.
• In specific embodiments of the agricultural data self-classification algorithm602, the processes insteps701 and702 can be simplified for a specific explanation, in terms of one agricultural geospatial data point that contains a latitude and longitude coordinate, and one geospatial field boundary which contain a series of latitude and longitude coordinates that make up a boundary when that series is connected. The data point also contains information such as timestamp (time the data point was collected), and collected parameters, such as speed of the machine, engine load, fuel usage, etc. The self-classification process relates the location of the data point to the location of the field boundary, i.e., does the point lie within the boundary, is it outside of the boundary, and how close is it to the boundary edge. It then takes this relation and performs a similar relation with the moving speed of the equipment that is associated with the data point in order to relate it to an operating speed range and/or a travel speed range. When the relationships have been developed the data point can properly classify itself based on where it was located and how fast the equipment was moving at the time of data collection.
• In terms of the specific embodiment displayed inFIG. 7, the data classification categories may be described as follows.Data109 that is classified as operational, may represent thegeospatial data109 that is performing an agricultural task for afield boundary401. Travel data may represent thedata109 that occurs whenagricultural equipment101 is moving from onegeospatial geometry401402 to anothergeospatial geometry401402 in order to perform operational or ancillary work. The ancillary data then, may represent thegeospatial data109 that supports either travel or operational data and may be related to either afield boundary401 and/or alocation402. An example of each of these classification types may be, but should not be limited to, operational data of an agricultural tractor andtillage equipment101 performing tillage on afield boundary401, a self-propelledsprayer101 travelling from onefield boundary401 to anotherfield boundary401 in order to spray the next field, and finally ancillary data representing anagricultural tractor101 connecting to an implement, like a tillage tool, in abarnyard402 for example. These examples are meant to show what each data classification may represent, but in no way should be taken as limiting, as many other examples of each data classification exist, which will be apparent to those skilled in the art in light of the disclosures herein.
• After thedata109 has been classified as operational, travel, or ancillary in702, the data may then be transferred to the geospatial dataevent creation algorithm603. Thisalgorithm603 contains three paths for the data to follow, which include one path for each data classification category; operational which starts at703, travel at704, and ancillary at705. The skilled artisan should recognize that although three paths are shown and described herein, greater or fewer numbers of paths may be provided without departing from the scope of the present invention. The path specified for operational data starts with the process ofaggregation703 for all of the previously classified operational data. Using the timestamps of the data, when the data was initially measured and recorded from theagricultural equipment101, along with a temporal analysis approach, the data can be divided, organized, and then aggregated for the creation of geospatial data events. This is an automated process, and allows the data to, once again, organize itself based on its own collected parameters. In this case, it is the time of data collection that allows the data to then group itself so that specific geospatial data events may be created.
• In more detail,step703 may start by analyzing the timestamp parameter of the classified operational data for one piece/group of agricultural equipment, which we'll call101₀for explanation purposes, which has been assigned to one geospatial geometry, which we'll call401₀also for explanation purposes. Analyzing data foragricultural equipment101₀on onegeospatial geometry401₀allows for a direct timestamp comparison which provides a temporal density measurement of the data. This temporal density measurement then allows for gaps in time to be identified so the data can be partitioned at the identified gaps and aggregated in-between for geospatial data event creation. This procedure allows the data to dictate the number of geospatial data events created for the givenagricultural equipment101₀on the givengeospatial geometry401₀. Thisprocess703 may then continue for each and every piece ofequipment101_{1 to n}on thatgeospatial geometry401₀, and then starts again for the nextgeospatial geometry401₁in the list until allgeospatial geometries400 contained in thedatabase302 have been analyzed.
• In an alternate embodiment of703, the data that is incoming and is directly classified in702 may then also directly be sent to703 for aggregation. In this embodiment, the temporal analysis using the timestamps of the classified data is still used, but instead of using a temporal density to identify separations in time so aggregation of the data may occur, the timestamps of the data are analyzed in comparison to the last recorded geospatial data event. In this way, the temporal comparison to the previous geospatial data event, and the corresponding timestamps of the classified data that make up the geospatial data event, can evaluate if a large enough gap in time has occurred to either create a new geospatial data event, or continue to aggregate the incoming data to the previous geospatial data event. This process, again, can then be applied for allagricultural equipment101_{1 to n}and allgeospatial geometries400 that are contained within thedatabase302.
• After the geospatial data is aggregated into operational data events in703, the events can then be summarized to provide the operating characteristics of the events in706. Thisstep706 of thealgorithm603 uses the aggregated data for each event from703 and summarizes all of the data for each measured parameter that was collected by thecollection device105. A few examples of these measured parameters include, but should not be limited to, speed of the agricultural equipment for the operation event, total time of the operation event, distance travelled during the operation event, and fuel used during the event. Particular embodiments may also include parameters such as, seeding rate during the event, application rate during the event, average yield during the event, harvest moisture data during the event, or any other machine, agronomic, or monitor/controller data parameter that may be collected by thedata collection device105.
• Thisaggregation method603 may also be completed for the agriculturalgeospatial data109 that has been classified as travel data. This process, again, begins with aggregating the travel data for each piece/group ofequipment101 for everyfield boundary401 orlocation402. Using the timestamps of the geospatial data and a temporal analysis method, travel data events can be created. These travel data events may contain all of the relevant measured parameters in the data that the operational data contained, such as, but should not be limited to, speed of the agricultural equipment during the event, distance travelled during the event, and total time of the event. This data however, once aggregated into events, may also contain the travel event origin, or where it departed, as well as the destination, where the travel event arrived. These origins, and destinations, may be, but should not be limited to,field boundaries401, staging areas, storage areas, or any location that has been marked as significant to thefarm402. After the travel events have been created,step707 summarizes each event with the same technique used in706 in order to provide the operating characteristics associated with each travel event.
• The third path the geospatial dataevent creation algorithm603 is for agriculturalgeospatial data109 that has been classified as ancillary. This data can once again be aggregated byequipment101 for eachlocation402 that is designated as a support site for the operation. These aforementioned locations may also include a geospatial boundary and/orlocation402 so that the ancillary data can be classified and aggregated instep705. The aggregation technique as well as the summarization technique are the same that are used for the operational data in703, and travel data in704. Operating characteristics for each ancillary event may then be created by the summarization technique in708 so thenext step709 of the adaptivedata analysis algorithm304 may be completed.
• The aggregation of the classified geospatial data for the creation of agricultural geospatial data events helps to provide another layer of context, and therefore, usefulness to the data that is being processed. The classification methods used in the self-classification algorithm602, provide the data with context to whichgeospatial geometry400 it belongs, as well as, which classification category it is. Aggregating this data then provides another layer of context which can be thought of as the geospatial data event layer. This layer of context allows for the summarization of the classified data in order to describe each individual geospatial data event. As farms make decisions on these geospatial data event types, the ability to form the context into a layer that is easilyrelatable farm managers310 is important to make the agriculturalgeospatial data109 useful, actionable, and also beneficial for further context to be built upon.
• The summarizations of the operational706,travel707, and ancillary708 data events then allow for the creation of a chronological list of these events for each and everyfield401 and/orlocation402 as they occurred in time. In terms of the operational data events, thisprocess709 starts by sorting the time of occurrence of each data event for a givengeospatial geometry401₀, which would include all operational events for allagricultural equipment101_{0 to n}. After the time series sorting, the list provides an order of operational data events for thegeospatial geometry401₀, that we can call field events or field operation events, which occurred in chronological order and contain all of the associatedagricultural equipment101_{0 to n}that performed the work. Thisprocess709, can then be repeated again for allfields401, andlocations402, until eachgeospatial geometry400 contains a list offield events305. These lists can then be used and transferred, via acommunication network110, to aclient113 for further processing as depicted inFIG. 6.
• Thesame process709, may then be repeated for travel data events and ancillary data events. Ancillary data events may uselocations402, which have been set up by the farm as significant, to order all of the equipment's ancillary operations as they occurred. Travel data events, on the other hand, are slightly different in that they may use thefield boundary401 and/orlocation402 as a place that travel either originated from or arrived to, and can be ordered and listed for everyfield boundary401 and/orlocation402 in that manner.
• FIG. 8 displays an example of a summarized agricultural geospatialdata event list305 that has been classified as operational and was generated through the above process for anindividual field401₀. Thisdata event list305 contains not only the equipment the event corresponds to, but also the summarized operating characteristics that are obtained through the algorithmic processing of the adaptivedata analysis algorithm304. It should be noted that the geospatialdata event list305 contains information such as, total pass time, total distance covered, average speed, fuel used, and average engine load, but should not be limited to these summarization parameters as many others may be collected.
• AsFIG. 8 displays a simplified example of a summarized agricultural geospatialdata event list305, it may be realized that an alternate embodiment of this list may be a database that contains all of these geospatial data event lists305. This database may contain all of the same information as displayed in the geospatialdata event list305 and may be used to query the data contained within for the further processing inclient113.
• With that alternate embodiment realized,FIG. 9 gives a more detailed view of theclient113, which inputs the summarized agriculturalgeospatial data events305 as shown inFIG. 8 that have been transferred via thecommunication network110. Theclient113 may contain anagricultural operations model306, a processor that may be able to perform algorithmic computer code/instructions901, along with thedynamic classification algorithm307, and the actionable management information andinsights308 generated by thealgorithm307. Thedynamic classification algorithm307 can also be divided into two pieces; the agricultural geospatial dataevent classification algorithm902, and the information andinsights generation algorithm903. The agriculturalgeospatial data events305 along with results from theagricultural operations model306 are used within the dataevent classification algorithm902 in order to classify each event that occurred within thefield boundary401 and/orlocation402. Once the classification of thegeospatial data events305 have been classified instep902 theevents305 are then provided to the information andinsights generation algorithm903 in order for actionable information andinsights308 to be generated.
• In an alternative embodiment, theagricultural operations model306 may instead be fully contained within theserver111 portion of the system, or theagricultural operations model306 may also be a hybrid model where part of themodel306 is contained within theserver111 and part of themodel306 is contained within theclient113. Furthermore, theagricultural operations model306 may also be structured as a database rather than a model. In this potential embodiment, information contained within theagricultural operations model306 may be stored in the database and then used to help classify the agriculturalgeospatial data events305 using thealgorithm307. In either of the potential configurations or structure of theagricultural operations model306, it performs the same task in the system and is used to help generate actionable information andinsights308.
• In order to do this, theagricultural operations model306, which can be seen in more detail inFIG. 10, may create a planned, e.g., idealized, and optionally chronological, list ofequipment operation events1004 for everygeospatial geometry400. Thesegeometries400, may be from thegeospatial geometry database303, may come from a separate source, or may be a combination of the two. Thismodel306 may then use information such as a datacollection device list1001 along with anequipment list1002 for the farm so thatequipment101 may be associated with the properdata collection device105 in order for knowndevices105 andequipment101 to be used to createequipment operations1003. Other planned information that may be associated with theequipment operations1003 include, but should not be limited to,equipment cost information1005,fuel cost information1006, andlabor cost information1007 in order for cost parameters to be measured and associated with the operation events. These planned factors may then help in devising a planned chronological list ofequipment operation events1004. An example of a planned chronological list ofequipment operation events1004 can be seen inFIG. 11. Similar to the geospatialdata events list305, shown inFIG. 8, it may contain information such as theoperating equipment101 and the operation event name, as well as the summarized characteristics created from themodel306.FIG. 11 shows a small subset of summarized characteristics for simplicity purposes, but it should be understood, that parameters such as, which should not be limited to, equipment cost, fuel cost, labor cost, total cost, downtime, and field efficiency may also be contained along with others.
• In an alternate embodiment, theagricultural operations model306 may not create a planned chronological list ofequipment operation events1004 for each field boundary. Instead theequipment operations1003 may provide the necessary information to thedynamic classification algorithm307 itself, so that actionable management information and insights may be created.
• Also, in a similar fashion to the potential embodiment of a database for the agriculturalgeospatial data events305, the chronological planned list ofequipment operation events1004 may also potentially reside in a database structure. This would, once again, contain all of the information and parameters that the plannedlist1004 would contain and would perform the same function in the system to allow thedynamic classification algorithm307 to classify/match the actualgeospatial data events305 withplanned events1004.
• FIG. 12 shows a more detailed flowchart of thedynamic classification algorithm307 as described above that contains the agricultural geospatial dataevent classification algorithm902, and the information andinsights generation algorithm903. The planned chronological list of equipment operation events for eachfield boundary1004, along with the summarized agriculturalgeospatial data events305, are used to compare against one another using their similar summarized characteristics instep1201 of the dataevent classification algorithm902. Based on how closely the summarized characteristics of the plannedlist events1004 and the summarizedevents305 are, the dataevent classification algorithm902, then classifies each event for each field in1202. The classifications of these events may be, but should not be limited to, an event found in the summarizedgeospatial data events305 was matched to an operation event listed in the planned list ofoperation events1004, an event from thegeospatial events305 was unable to be matched to an event in the plannedlist1004, and theevent305 was unable to be found within the plannedlist1004. This last classification may distinguish itself from the middle classification in terms of an example, in which anevent305 has been generated for an agricultural piece ofequipment101 with adata collection device105 but theequipment101 was not in the plannedchronological list1004, resulting in an unknown actual event, as opposed to, just unable to match anevent305 with an event in the planned list of1004.
• After the classification of thegeospatial data events305 has occurred, operation event names specified in the plannedoperation event list1004 may be assigned to the corresponding matched and classifiedgeospatial data events305 in1203. Extended information, resulting from theagricultural operations model306 such as, but should not be limited to, equipment cost, fuel cost, and labor cost to calculate extended capacity and financial information, may also then be assigned as well. Theinformation generation algorithm903 may then take the assigned andgeospatial data events305 and generate actionable management information andinsights308.
• In an alternate embodiment of the agricultural geo spatial dataevent classification algorithm902,methods1201,1202, and1203 that compare, classify and assign the summarized geospatial data events, may instead consist of just one step in which the plannedlist1004 are assigned to the summarizedgeospatial data events305. These plannedgeospatial events1004 may be assigned through the use of time of occurrence of both the planned list ofdata events1004 and the summarizedgeospatial data events305 and how they occur chronologically. In an embodiment aforementioned where a planned list ofevents1004 was not created, the single step method would assign theequipment operations1003 with thegeospatial data events305. This assignment process would relate the specificagricultural equipment101 with the equipment used in the summarizedgeospatial data event305, along with the time of occurrence of theevent305, to assign theoperation1003 name and extended information from theagricultural operations model306.
• In either embodiment of the agricultural geospatial dataevent classification algorithm902, the summarizedgeospatial data events305 are matched with corresponding information from theagricultural operations model306. The fitting of the summarized geospatial data events to the information from theagricultural operations model306, is another way to provide further context to the data, which allows for further information and insights to be generated. It not only provides context to the data in whichfarm managers310 can easily recognize, but it also provides some technical advantages. Creating context from the agriculturalgeospatial data109 from the beginning of theanalysis system100, to automatically classify into operational, travel, and ancillary, then to assign to the propergeospatial data geometry400, and finally aggregate and summarize the data into geospatial data events allows for easy fitting to theagricultural operations model306. An easy fit to theagriculture operations model306 allows for lower processing time and resources, and increases efficiency in the processes as not all potential outcomes, scenarios, and permutations need to be evaluated in order for a fit of themodel306 to occur. Data storage may also be reduced by this technique as not all of these various scenarios need to be stored for further comparisons and evaluations. Finally, any further aggregation of the summarizedgeospatial data events305 may also be performed in a very computationally inexpensive manner as they have already been contextualized and would just need simple aggregation techniques performed on the queried data.
• This is the case for the information andinsights generation algorithm903 as it begins with aggregating the classified/matched/assigned events by the different types ofagricultural management categories1204. These management categories may contain, but should not be limited to, the business farm entities or clients, land ownership entities or farms, fields, storage locations, staging areas, equipment, operations, laborers, or any other category that may be used to aggregate the summarized geospatial data events and theircharacteristics305. Once the classified agriculturalgeospatial events305 have been aggregated into management category, the aggregated information may be analyzed in order to identify the most important and influential management categories that may provide the most actionable information andinsights1205. This step may contain processes in which categories have been selected prior to analysis to identify the most important information and influential categories.
• With actionable management categories and their relationships obtained instep1205, thealgorithm903 can then generate reports, visualizations, actionable information, andinsights1206 in order to extract the significant relationships from the agriculturalgeospatial data events305. The information and insights generated may be displayed, for example, in tabular reports, graphed visualizations of the geospatial event data, and summary information on both the results generated as well as the correlated actionable insights that the agriculturalgeospatial event data305 may have provided. The actionable management information andinsights308, may also be generated to contain just the most actionable information andinsights using step1205, just all information and insights using the aggregated management category information from1204, or a combination of both in order to provide thefarm managers310 with the most actionable and desired management information andinsights308 as possible.
• In an alternate embodiment of thedynamic classification algorithm307, the algorithm may perform similar steps as described above but in the scenario where one, two, or all three of thegeospatial data events305, theagricultural operations model306, and the chronological plannedequipment operation events1004 are in a database structure. In this embodiment, the databases of the plannedoperation events1004 and thegeospatial data events305 would be compared and matched to each other using a similar technique that is described above, but the way the data from1004 and305 would be accessed may be different as well as the underlying data structure. The process would still be able to obtain the classified geospatial data events as well as generate actionable information and insights. The databases may contain, but should not be limited to, the same operating characteristics as mentioned above but also contain extended financial information regarding the associated costs of equipment, inputs such as seed, fertilizer, and chemicals, and labor and may be used to compare, contrast, and align the data within the databases to achieve the classification and information andinsights308 generation.
• It should also be noted here that, the dynamic nature of theclassification algorithm307 may be attributed to the inputs of the chronological list ofoperation events1004 from theagricultural operations model306, the summarized agriculturalgeospatial data events305, as well as the structure of the algorithm itself. Theinputs1004 and305 may dictate the results of thealgorithm308, by the way thealgorithm307 uses the information provided to generate theresults308. If new agriculturalgeospatial data109 is collected and processed into new summarizedagricultural data events305, the algorithm may adjust to account for thesenew events305. In a similar approach, if any parameter in theagricultural operations model306 is modified that changes a related parameter, in any way, the algorithm may re-adjust for the new parameters in real-time so new actionable management information andinsights308 may be generated to reflect the change. These two input changes may also occur at the same time in which the new generatedresults308 may also occur.
• With the actionable management information andinsights308 generated, the management information and insights transfermethod309 may then transfer the information andinsights308 to thefarm managers310 for viewing purposes as depicted inFIG. 9. The actionable management information and insights transfermethod309 may include, for example, a connection that may be wireless or wired to a visual screen or monitor within theclient113 or to a wireless or wired connection to any device that is able to display the information andinsights308. Thistransfer method309 may transfer the information and insights to thefarm managers310 and to any number of devices they may use, such as, for example, mobile phones, desktop or laptop computers, tablets, PDAs, printers, fax machines, or any device that is able to display and/or visualize the information andinsights308 sent.
• FIG. 13 andFIG. 14 display examples of thisactionable management information308 that may be created by thesystem100.FIG. 13 displays the percentage of total operation time that the equipment was not moving, or was at 0 mph, for different agricultural operation events. The information provided inFIG. 13 may then showfarm managers310, for example, which operation events had the largest percentage of downtime, or time at 0 mph, as well as the comparison of the time at 0 mph for similar operation events such as Plant Corn and Plant Soybeans. This information is actionable because it allows thefarm managers310 to make decisions based on equipment operators, equipment used, or on the equipment operation event itself in order to improve the performance.
• In a similar manner,FIG. 14 displays a total cost per acre breakdown for each operation event in this example. The breakdown includes average equipment cost per acre, the average fuel cost per acre, and the average labor cost per acre for the operation events on all fields for the entire farm. FromFIG. 14, information on the operation events that have been analyzed through thesystem100 provided herein, may include which operations cost the most on a per acre basis, which operations have the most equipment, labor, and/or fuel cost per acre, the comparison of like operation events such as planting or spraying, and how much some operation events cost relative to other operation events. All of this information generated is actionable because it helpsfarm managers310 make decisions on operators, equipment, logistics, or on the operation events itself, in an attempt to try and limit the cost of these operation events.
• Once again, the results displayed inFIG. 13 andFIG. 14 are examples ofactionable information308 that may be generated through thesystem100 presented herein. These in no way should be taken as limiting examples, but rather, are shown as simplified displays of the actionable information generated through the system.
• FIG. 15 displays an example of the actionable management information (also referred to herein as “management insight”, “actionable insight”, or simply “actionable information”)308 that may be generated from thesystem100. This example should be taken as a simplified and a non-limiting example of what the system presented herein may be capable of providing.
• FIG. 15 depicts theactionable insight308 that may be generated from thesystem100, in terms of total farm savings in planting and harvesting costs due to increasing the average operation speed. The figure is split into four main charts that visualize the correlation of planting and harvest speed to the total operation event cost peracre15A, the dollar per acre savings per mile per hour (mph)increase15B, the speed increase to reach planned speed and the associated dollar per acre savings for thatincrease15C, and finally the total potential savings in planting and harvest costs all summed up15D. These four charts take the information and generate the insights to allow thefarm managers310 to make the actionable decision, in this specific embodiment, of whether the planting and harvesting operation events should be performed at a higher speed. This decision can help be made by evaluating the insight provided from the four figures within the chart. It also allows forfarm managers310 to gain insight on the comparison of actual operation characteristics versus the planned characteristics, which can be thought of as a set standard. Setting a standard allowsfarm managers310 to realize operational differences in actual versus the planned and manage accordingly to achieve that standard. The following describes an example of this in relation to operation speed and its associated costs.
• In15A the correlation of planting and harvest speed to the total dollar per acre cost can be seen. From the figure, it can be seen that the planned speed, which can be seen as the thicker gray bar on the left (speed) side of the chart, shows a faster operation event speed than does the actual operation event speed. This difference in speed can be correlated to the total cost per acre on the right ($/acre) hand chart in15A. The slower speed of operation events show that the total cost of operation per acre is higher as opposed to the cost for the planned operation event speed. To help quantify this difference in cost due to the speed of operation,15B shows the potential savings in total dollars per acre by a 1 mph average increase in speed of operation. This dollar per acre savings per mph can be seen for the different planting and harvesting operations and shows which of the operations may make the most sense to speed up if possible. With planting corn showing the highest dollar per acre savings per mph increase, it may make sense as the farm manager to try and speed up the corn planting operation event to obtain those potential savings. Whereas, the harvesting corn operation event may still prove to save money by speeding up the operation event, the savings may just not be by quite as much as the other operation events shown.
• While15A and15B present the actionable insights to correlate speed of operation with the cost of operation, the lower twocharts15C and15D provide theactionable insights308 of total cost savings. In15C, the chart displays the increase in speed of operation to obtain the planned speed of the operation event, as well as the dollar per acre savings for each of the operation event's speed increase. This chart shows that by increasing the average speed by the given amount, large potential savings may be seen in the total cost of operation. This total savings may then be rolled up and shown in the donut chart in15D. This chart displays the total operation cost for both planting and harvesting as well as the potential savings from the total cost of operation. The chart visualizes that 15% of the total cost of operation may be saved if the planned speed of operation for planting and harvesting is achieved during the actual operation events. This potential 15% in savings would amount to $18,514 in savings for this specific embodiment. Driving the information provided to the insight of just achieving the planned average speed for planting and harvesting operation events could potentially save the farm $18,514. This may provide enough insight to drive the decision of thefarm managers310 to make sure that the operators of the equipment, for these operating events, achieve the speed of operation that was set. The insight of increasing speed of operation is an actionable decision forfarm managers310 to make and may allow them to optimize the farming operation events to help them save money, increase production and efficiency, and ultimately fine tune their overall performance of the farm operation.
• With the potential advantages presented of increasing speed of operation for planting and harvesting operations, embodiments of the invention use this insight to provide the aforementioned operational directive of controlling thework machine101 to achieve the set speed. This operational directive can be implemented through control of the various systems201-211 communicably coupled to theequipment system bus102, as shown inFIG. 2. For example, theengine201,transmission202, andelectrical system203, may be controlled, e.g., through the ECU of the work machine, to allow thework machine101 to achieve the desired speed. This control may be obtained through an automated feedback system that allows information generated by theguidance system112, to be sent in the form of an operational directive via cloud/communication networks110 to themachine101 in order to control the necessary systems on theequipment system bus102. The operational directive(s) may also be used in a more manual, or semi-automated, manner in which the insight generated byguidance system112 is displayed to an operator within themachine101, so that the operator may implement the directive using conventional operating controls ofwork machine101, e.g., by shifting gears and adjusting throttle speed. In any case, whether through the automated process of controlling the machine viaequipment system bus102, by operator control, or by some combination thereof, embodiments of the present invention allow the operational directive to be received, read, executed, and implemented.
• Again, it should be understood that the above example of sending an operational directive of altering the speed of themachine101 is just an example and should not be taken as limiting. As other operational directives are acted upon, any number of thesystems201 through211 (FIG. 2), as well as other systems that may be developed in the future, may need to be controlled and adjusted in order to achieve the desired machine operation.
• FIG. 16 shows a diagrammatic representation of a machine in the exemplary form of acomputer system1300 within which a set of instructions, for causing the machine to perform methodologies discussed above, may be executed.
• Thecomputer system1300 includes aprocessor1302, amain memory1304 and astatic memory1306, which communicate with each other via a bus1308. Thecomputer system1300 may further include a video display unit1310 (e.g., a liquid crystal display (LCD), plasma, cathode ray tube (CRT), etc.). Thecomputer system1300 may also include an alpha-numeric input device1312 (e.g., a keyboard or touchscreen), a cursor control device1314 (e.g., a mouse), a drive (e.g., disk, flash memory, etc.,)unit1316, a signal generation device1320 (e.g., a speaker) and anetwork interface device1322.
• Thedrive unit1316 includes a computer-readable medium1324 on which is stored a set of instructions (i.e., software)1326 embodying any one, or all, of the methodologies described above. Thesoftware1326 is also shown to reside, completely or at least partially, within themain memory1304 and/or within theprocessor1302. Thesoftware1326 may further be transmitted or received via thenetwork interface device1322. For the purposes of this specification, the term “computer-readable medium” shall be taken to include any medium that is capable of storing or encoding a sequence of instructions for execution by the computer and that cause the computer to perform any one of the methodologies of the present invention, and as further described hereinbelow.
• Certain aspects of the present invention include process steps and instructions described herein in the form of an algorithm. It should be noted that the process steps and instructions of the present invention could be embodied in software, firmware or hardware, and when embodied in software, could be downloaded to reside on and be operated from different platforms used by real time network operating systems. Moreover, the particular naming of the components, capitalization of terms, the attributes, data structures, or any other programming or structural aspect is not mandatory or significant, and the mechanisms that implement the invention or its features may have different names, formats, or protocols.
• Moreover, unless specifically stated otherwise as apparent from the above discussion, it is appreciated that throughout the description, discussions utilizing terms such as “processing” or “computing” or “calculating” or “determining” or “displaying” or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system memories or registers or other such information storage, transmission or display devices.
• Embodiments of the present invention also relate to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, or it may comprise a computer selectively activated or reconfigured by a computer program stored on a computer readable medium that can be accessed by the computer. Such a computer program may be stored in a tangible, non-transitory, computer readable storage medium, such as, but is not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, application specific integrated circuits (ASICs), any other appropriate static, dynamic, or volatile memory or data storage devices, or other type of media suitable for storing electronic instructions, and each coupled to a computer system bus. Furthermore, the computers referred to in the specification may include a single processor or may be architectures employing multiple processor designs for increased computing capability.
• In addition, the present invention is not described with reference to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any references to specific languages are provided for disclosure of enablement and best mode of the present invention.
• The present invention is well suited to a wide variety of computer network systems over numerous topologies. Within this field, the configuration and management of large networks comprise storage devices and computers that are communicatively coupled to dissimilar computers and storage devices over a network, such as the Internet.
• Modifications, additions, or omissions may be made to the systems, apparatuses, and methods described herein without departing from the scope of the disclosure. For example, the components of the systems and apparatuses may be integrated or separated. Moreover, the operations of the systems and apparatuses disclosed herein may be performed by more, fewer, or other components and the methods described may include more, fewer, or other steps. Additionally, steps may be performed in any suitable order. It should be further understood that any of the features described with respect to one of the embodiments described herein may be similarly applied to any of the other embodiments described herein without departing from the scope of the present invention. As used in this document, “each” refers to each member of a set or each member of a subset of a set.
• To aid the Patent Office and any readers of any patent issued on this application in interpreting the claims appended hereto, applicants wish to note that they do not intend any of the appended claims or claim elements to invoke 35 U.S.C. 112(f) unless the words “means for” or “step for” are explicitly used in the particular claim.

Claims (38)

What is claimed is:

1. A machine guidance apparatus for completing farming operations with operational efficiency on a farm having at least one field or significant location, the apparatus comprising:

an agricultural work machine configured for traversing the farm, the agricultural work machine having an ECU (Electronic Control Unit) communicably coupled via a system bus to a plurality of machine system elements including an engine and sensors to control and monitor engine functions;

a farm implement operationally engaged with, and operated by, the agricultural work machine to effect farming operations as the agricultural work machine traverses the farm;

a GPS receiver disposed on the agricultural work machine to generate location data for the work machine; and

a data collector communicably coupled to the GPS receiver and to the system bus, the data collector configured to capture agricultural geospatial data including the location data for the work machine from the GPS receiver and engine function data from the ECU; and

a guidance system communicably coupled to the data collector, the guidance system having a memory and a processor, wherein the memory includes a stored program executable by the processor, the stored program configured to:

(a) capture geospatially located geometries of the farm, including field boundaries and topographical features of the field;

(b) capture the agricultural geospatial data;

(c) automatically classify the agricultural geospatial data, using the geospatially located geometries of the farm, into one or more activity/event categories including operational events, travel events, and ancillary events, to produce classified data;

(d) aggregate the classified data to create a plurality of geospatial data events;

(e) match the plurality of geospatial data events to planned versions of said plurality of geospatial data events within an agricultural operations model to generate a plurality of matched events;

(f) use the plurality of matched events to generate actionable information for the working machine in real-time or near real-time; and

(g) generate and send operational directives to the agricultural work machine based on the actionable information.

2. The apparatus ofclaim 1, wherein the operational directives for the working machine include: (i) instructions for increasing or decreasing the speed of the working machine to match an optimal speed generated by the model for an identified geospatial data event; (ii) instructions for shifting gears and reducing engine speed to match optimal levels generated by the model for an identified geospatial data event; (iii) instructions for altering a direction of travel of the work machine to match an optimal direction of travel generated by the model for an identified geospatial data event; and/or (iv) instructions to turn off the working machine during non-productive times.

3. The apparatus ofclaim 2, wherein the guidance system is configured to generate and send operational directives to the agricultural work machine in real-time or near real-time.

4. The apparatus ofclaim 1, wherein said automatically classify (c) comprises mapping the agricultural geospatial data to the geospatially located geometries of the farm to determine whether the agricultural geospatial data references a location (i) within any of the field boundaries, (ii) outside any of the field boundaries, or (iii) at an intersection of any of the field boundaries.

5. The apparatus ofclaim 4, wherein said automatically classify (c) further comprises determining whether the agricultural geospatial data references speed of the equipment (iv) within an operating speed range, or (v) within a travel speed range.

6. The apparatus ofclaim 5, wherein said automatically classify (c) further comprises determining a type of work machine and/or implement used to generate the agricultural geospatial data.

7. The apparatus ofclaim 6, further comprising an implement sensor disposed on the work machine, configured to capture the type of work machine and/or implement.

8. The apparatus ofclaim 1, wherein the agricultural geospatial data further comprises temporal data; machine and equipment data including speed of the machine, engine load, fuel usage; agronomic data; monitor/controller data; equipment sensor data; and combinations thereof.

9. The apparatus ofclaim 8, wherein said location data provides a travel path of the work machine.

10. The apparatus ofclaim 1, wherein:

the operational events comprise the work machine performing agricultural tasks within field boundaries;

the travel events comprise the work machine moving from one field to another field; the work maching moving from one significant location to another significant location; and combinations thereof;

and

the ancillary events comprise the work machine engaging in operations supportive of the operational events and/or the travel events.

11. The apparatus ofclaim 10, wherein the operational events are selected from the group consisting of: planting; applications such as spraying or spreading; harvesting; tilling; grain or bulk crop carting; grain or bulk crop hauling; baling; seed tending; and preparation work such as swathing, windrowing, conditioning, raking, or tedding.

12. The apparatus ofclaim 11, wherein the ancillary events include idling and switching implements.

13. The apparatus ofclaim 1, wherein the actionable information is selected from the group consisting of: percentage of time the working machine spent idling during various ones of said geospatial data events; costs per acre associated with various ones of said geospatial data events; correlation of speed of working machine to costs associated with various ones of said geospatial data events; cost savings per acre as a function of speed of the working machine; and combinations thereof.

14. The apparatus ofclaim 1, wherein the operational data for the work machine from the ECU includes speed, fuel use and engine rpm/load data.

15. The apparatus ofclaim 1, wherein said capture (a) comprises using the GPS receiver to capture GPS coordinates of the boundaries and topographical features of a field as the agricultural work machine traverses the field.

16. The apparatus ofclaim 1, wherein said capture (a) comprises capturing coordinates of the boundaries and topographical features of a field from a map.

17. The apparatus ofclaim 1, wherein said capturing (b) further comprises capturing a speed at which the farm implement is traversing the farm.

18. The apparatus ofclaim 1, wherein said (d) aggregate further comprises placing the plurality of geospatial data events into management categories.

19. The apparatus ofclaim 18, wherein the management categories are selected from the group consisting of: business farm entities or clients, land ownership entities or farms, fields, storage locations, staging areas, equipment, operations, laborers, and combinations thereof.

20. A method for completing farming operations with operational efficiency on a farm having at least one field or significant location, the method comprising:

providing an agricultural work machine configured for traversing the farm, the agricultural work machine having an ECU (Electronic Control Unit) communicably coupled via a system bus to a plurality of machine system elements including an engine and sensors to control and monitor engine functions, the agricultural work machine including a GPS receiver, a data collector, and a specialized guidance system including a memory and a processor, the memory including a stored program executable by the processor;

operationally engaging and operating a farm implement with the agricultural work machine;

capturing, with the data collector, agricultural geospatial data including location data for the work machine from the GPS receiver and engine function data from the ECU; and

executing the stored program to:

(a) capture geospatially located geometries of the farm, including field boundaries and topographical features of the field;

(b) capture the agricultural geospatial data;

(c) automatically classify the agricultural geospatial data, using the geospatially located geometries of the farm, into one or more activity/event categories including operational events, travel events, and ancillary events, to produce classified data;

(d) aggregate the classified data to create a plurality of geospatial data events;

(e) match the plurality of geospatial data events to planned versions of said plurality of geospatial data events within an agricultural operations model to generate a plurality of matched events;

(f) use the plurality of matched events to generate actionable information for the working machine in real-time or near real-time; and

(g) generate and send operational directives to the agricultural work machine based on the actionable information.

21. The method ofclaim 20, wherein the operational directives for the working machine include: (i) instructions for increasing or decreasing the speed of the working machine to match an optimal speed generated by the model for an identified geospatial data event; (ii) instructions for shifting gears and reducing engine speed to match optimal levels generated by the model for an identified geospatial data event; (iii) instructions for altering a direction of travel of the work machine to match an optimal direction of travel generated by the model for an identified geospatial data event; and/or (iv) instructions to turn off the working machine during non-productive times.

22. The method ofclaim 21, comprising generating and sending operational directives to the agricultural work machine in real-time or near real-time.

23. The method ofclaim 20, wherein said automatically classify (c) comprises mapping the agricultural geospatial data to the geospatially located geometries of the farm to determine whether the agricultural geospatial data references a location (i) within any of the field boundaries, (ii) outside any of the field boundaries, or (iii) at an intersection of any of the field boundaries.

24. The method ofclaim 23, wherein said automatically classify (c) further comprises determining whether the agricultural geospatial data references speed of the equipment (iv) within an operating speed range, or (v) within a travel speed range.

25. The method ofclaim 24, wherein said automatically classify (c) further comprises determining a type of work machine and/or implement used to generate the agricultural geospatial data.

26. The method ofclaim 25, further comprising capturing, with an implement sensor disposed on the work machine, the type of work machine and/or implement.

27. The method ofclaim 20, wherein the agricultural geospatial data further comprises temporal data; machine and equipment data including speed of the machine, engine load, fuel usage; agronomic data; monitor/controller data; equipment sensor data; and combinations thereof.

28. The method ofclaim 27, comprising using the location data to provide a travel path of the work machine.

29. The method ofclaim 20, wherein:

the operational events comprise the work machine performing agricultural tasks within field boundaries;

the travel events comprise the work machine moving from one field to another field; the work machine moving from one significant location to another significant location; and combinations thereof;

and

the ancillary events comprise the work machine engaging in operations supportive of the operational events and/or the travel events.

30. The method ofclaim 29, wherein the operational events are selected from the group consisting of: planting; applications such as spraying or spreading; harvesting; tilling; grain or bulk crop carting; grain or bulk crop hauling; baling; seed tending; and preparation work such as swathing, windrowing, conditioning, raking, or tedding.

31. The method ofclaim 30, wherein the ancillary events include idling and switching implements.

32. The method ofclaim 20, wherein the actionable information is selected from the group consisting of: percentage of time the working machine spent idling during various ones of said geospatial data events; costs per acre associated with various ones of said geospatial data events; correlation of speed of working machine to costs associated with various ones of said geospatial data events; cost savings per acre as a function of speed of the working machine; and combinations thereof.

33. The method ofclaim 20, wherein the operational data for the work machine from the ECU includes speed, fuel use and engine rpm/load data.

34. The method ofclaim 20, wherein said capture (a) comprises using the GPS receiver to capture GPS coordinates of the boundaries and topographical features of a field as the agricultural work machine traverses the field.

35. The method ofclaim 20, wherein said capture (a) comprises capturing coordinates of the boundaries and topographical features of a field from a map.

36. The method ofclaim 20, wherein said capturing (b) further comprises capturing a speed at which the farm implement is traversing the farm.

37. The method ofclaim 20, wherein said (d) aggregate further comprises placing the plurality of geospatial data events into management categories.

38. The method ofclaim 37, further comprising selecting the management categories from the group consisting of: business farm entities or clients, land ownership entities or farms, fields, storage locations, staging areas, equipment, operations, laborers, and combinations thereof.

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