- Gaetano Saurio10,
- Marco Muscas10,
- Indro Spinelli10,
- Valerio Rughetti12,
- Irma Della Giovampaola11 &
- …
- Simone Scardapane10
Part of the book series:Lecture Notes in Computer Science ((LNCS,volume 14365))
Included in the following conference series:
406Accesses
Abstract
This paper summarizes the development of a weed monitoring system in the Parco archeologico del Colosseo (hereinafter, Parco) using Deep Learning (DL) techniques to recognize forty-one species of plants now present in the area. The project is part of SyPEAH (System for the Protection and Education of Archaeological Heritage), a platform designed to safeguard the Parco by its Authority. This study emanates from an extended phase of the photographic collection spanning ten months. This endeavour facilitated the compilation of a dataset comprising nearly 5,000 photographs depicting the flora of pertinent significance. In the paper, we detail the first version of the system, consisting of a neural network trained to predict the species of plants and the materials on which they grow. We also describe transfer learning techniques aimed at improving performance. The present system attains recognition accuracy exceeding 90% for common species, enabling near real-time monitoring of the entire Park’s flora through image analysis using supplied fixed and mobile devices. It will support proactive interventions for maintenance. The paper details data analysis and neural network design and envisions future developments.
This is a preview of subscription content,log in via an institution to check access.
Access this chapter
Subscribe and save
- Get 10 units per month
- Download Article/Chapter or eBook
- 1 Unit = 1 Article or 1 Chapter
- Cancel anytime
Buy Now
- Chapter
- JPY 3498
- Price includes VAT (Japan)
- eBook
- JPY 13727
- Price includes VAT (Japan)
- Softcover Book
- JPY 17159
- Price includes VAT (Japan)
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Arnal Barbedo, J.G.: Digital image processing techniques for detecting, quantifying and classifying plant diseases. Springerplus2(1), 1–12 (2013)
Benos, L., Tagarakis, A.C., Dolias, G., Berruto, R., Kateris, D., Bochtis, D.: Machine learning in agriculture: a comprehensive updated review. Sensors21(11), 3758 (2021)
Cravero, A., Pardo, S., Sepúlveda, S., Muñoz, L.: Challenges to use machine learning in agricultural big data: a systematic literature review. Agronomy12(3), 748 (2022)
Della Giovampaola, I.: Piano sostenibile di tutela e valorizzazione del patrimonio archeologico e di educazione continua al patrimonio culturale: SyPEAH (A platform Systemfor the Protection and Education of Archaeological Heritage). Bullettino della Commissione Archeologica Comunale CXII, pp. 61–76 (2021)
Della Giovampaola, I.: SyPEAH: the WebAPP system for protection and education to archaeological heritage in the parco archeologico del colosseo. Geosciences11(6), 246 (2021)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)
Dosovitskiy, A., et al.: An image is worth 16\(\times \)16 words: transformers for image recognition at scale. In: International Conference on Learning Representations (2020)
Fracchiolla, M., Lasorella, C., Cazzato, E., Vurro, M.: Weeds in non-agricultural areas: how to evaluate the impact? A preliminary case study in archaeological sites. Agronomy12(5), 1079 (2022)
Garcin, C., et al.: Pl@ntNet-300K: a plant image dataset with high label ambiguity and a long-tailed distribution. In: NeurIPS 2021–35th Conference on Neural Information Processing Systems (2021)
Angiosperm Phylogeny Group: An update of the angiosperm phylogeny group classification for the orders and families of flowering plants: APG iv. Bot. J. Linn. Soc.181(1), 1–20 (2016)
Hasan, A.M., Sohel, F., Diepeveen, D., Laga, H., Jones, M.G.: A survey of deep learning techniques for weed detection from images. Comput. Electron. Agric.184, 106067 (2021)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature521(7553), 436–444 (2015)
Nilsback, M.E., Zisserman, A.: Automated flower classification over a large number of classes. In: 2008 Sixth Indian Conference on Computer Vision, Graphics and Image Processing, pp. 722–729. IEEE (2008)
Olsen, A., et al.: DeepWeeds: a multiclass weed species image dataset for deep learning. Sci. Rep.9(1), 2058 (2019)
Panda, B., Mishra, M.K., Mishra, B.S.P., Tiwari, A.K.: An extensive review on crop/weed classification models. In: Web Intelligence, pp. 1–16. No. Preprint, IOS Press (2023)
Ricotta, C., Grapow, L.C., Avena, G., Blasi, C.: Topological analysis of the spatial distribution of plant species richness across the city of Rome (Italy) with the echelon approach. Landsc. Urban Plan.57(2), 69–76 (2001)
Russo, A., Della Giovampaola, I.: Il monitoraggio e la manutenzione delle aree archeologiche. Il piano per il futuro del Parco archeologico del Colosseo, pp. 13–31. “L’Erma” di Bretschneider (2020)
Russo, A., Giovampaola, I.D., Spizzichino, D., Leoni, G., Coletta, A., Virelli, M.: The project of parco archeologico del colosseo and the Italian network of archaeological parks: from satellite monitoring to conservation and preventive maintenance policies. In: El-Qady, G.M., Margottini, C. (eds.) Sustainable Conservation of UNESCO and Other Heritage Sites Through Proactive Geosciences, pp. 659–678. Springer, Cham(2023).https://doi.org/10.1007/978-3-031-13810-2_34
Sudars, K., Jasko, J., Namatevs, I., Ozola, L., Badaukis, N.: Dataset of annotated food crops and weed images for robotic computer vision control. Data Brief31, 105833 (2020)
Sun, Y., Liu, Y., Wang, G., Zhang, H., et al.: Deep learning for plant identification in natural environment. Comput. Intell. Neurosci.2017, 7361042 (2017)
Therrien, R., Doyle, S.: Role of training data variability on classifier performance and generalizability. In: Medical Imaging 2018: Digital Pathology, vol. 10581, pp. 58–70. SPIE (2018)
Wäldchen, J., Mäder, P.: Plant species identification using computer vision techniques: a systematic literature review. Arch. Comput. Methods Eng.25, 507–543 (2018)
Wu, Z., Chen, Y., Zhao, B., Kang, X., Ding, Y.: Review of weed detection methods based on computer vision. Sensors21(11), 3647 (2021)
Author information
Authors and Affiliations
Sapienza University of Rome, Rome, Italy
Gaetano Saurio, Marco Muscas, Indro Spinelli & Simone Scardapane
Ministry of Culture, Rome, Italy
Irma Della Giovampaola
UniNettuno University, Rome, Italy
Valerio Rughetti
- Gaetano Saurio
You can also search for this author inPubMed Google Scholar
- Marco Muscas
You can also search for this author inPubMed Google Scholar
- Indro Spinelli
You can also search for this author inPubMed Google Scholar
- Valerio Rughetti
You can also search for this author inPubMed Google Scholar
- Irma Della Giovampaola
You can also search for this author inPubMed Google Scholar
- Simone Scardapane
You can also search for this author inPubMed Google Scholar
Corresponding author
Correspondence toGaetano Saurio.
Editor information
Editors and Affiliations
University of Udine, Udine, Italy
Gian Luca Foresti
University of Udine, Udine, Italy
Andrea Fusiello
University of York, York, UK
Edwin Hancock
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Saurio, G., Muscas, M., Spinelli, I., Rughetti, V., Della Giovampaola, I., Scardapane, S. (2024). ArcheoWeedNet: Weed Classification in the Parco archeologico del Colosseo. In: Foresti, G.L., Fusiello, A., Hancock, E. (eds) Image Analysis and Processing - ICIAP 2023 Workshops. ICIAP 2023. Lecture Notes in Computer Science, vol 14365. Springer, Cham. https://doi.org/10.1007/978-3-031-51023-6_36
Download citation
Published:
Publisher Name:Springer, Cham
Print ISBN:978-3-031-51022-9
Online ISBN:978-3-031-51023-6
eBook Packages:Computer ScienceComputer Science (R0)
Share this paper
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative