Binary Classification of Agricultural Crops Using Sentinel Satellite Data and Machine Learning Techniques

• Autorzy: Bertellini Paolo , D'Addese Gianluca , Franchini Giorgia , Parisi Simone , Scribano Carmelo , Zanirato Daniele , Bertogna Marko

Identyfikatory

DOI

10.15439/2023F1703

• Języki publikacji: EN

Abstrakty

EN

The advent of high-resolution satellite imagery, such as Sentinel 1 and Sentinel 2, has provided valuable data for various applications, including crop classification. This paper presents a study on the classification of agricultural fields using indices derived from Sentinel satellite imagery. Specifically, we focus on creating binary classifiers capable of distinguishing between different crops, namely Tomatoes, Soy, Sugar Beets, Rice, and Wheat. The paper investigates various preprocessing techniques to create a dataset suitable for machine learning methods, such as Random Forests, which require a fixed number of features. Additionally, we demonstrate that linear interpolation and out-of-scale values have equivalent performance in terms of classification accuracy. Furthermore, we address the issue of imbalanced datasets commonly encountered in agricultural field classification. We explore different balancing techniques that can significantly improve the performance of machine learning methodologies. The motivation for this work stems from the growing interest in Agriculture 4.0, and it serves as a valuable tool to verify farmers' claims, especially in relation to state subsidies for specific crops of interest. Understanding the crop type present in the field represents highly valuable information that can serve as a foundation for subsequent analyses or as input for calibrating models, such as Decision Support Systems. Overall, this study contributes to the field of agricultural research and provides insights into the application of Machine Learning techniques for crop classification using satellite imagery. The findings offer practical implications for monitoring and optimizing agricultural practices in the context of precision farming and sustainable agriculture.

Słowa kluczowe

EN

crop classification; AgriAI; machine learning; agriculture; decision support systems; Sentinel; Copernicus

PL

klasyfikacja upraw; AgriAI; uczenie maszynowe; rolnictwo; systemy wspomagania decyzji; Sentinel; Copernicus

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2023
• Tom: Vol. 35
• Strony: 859--864
• Opis fizyczny: Bibliogr. 13 poz., il., wykr., tab.

Twórcy

autor

Bertellini Paolo

• R&D - Data science Abaco Group Mantua, Italy

autor

D'Addese Gianluca

• Department of FIM UNIMORE Modena, Italy

• https://orcid.org/0000-0001-7755-0893

autor

Franchini Giorgia

• Department of FIM UNIMORE Modena, Italy

• https://orcid.org/0000-0001-9082-8087

autor

Parisi Simone

• R&D - Data science Abaco Group Mantua, Italy

autor

Scribano Carmelo

• Department of FIM UNIMORE Modena, Italy

• https://orcid.org/0000-0003-1006-7826

autor

Zanirato Daniele

• Department of Statistical Sciences University of Bologna Bologna, Italy

autor

Bertogna Marko

• Department of FIM UNIMORE Modena, Italy

• https://orcid.org/0000-0003-2115-4853

Bibliografia

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• 7. Immitzer, M., Vuolo, F., Atzberger, C.: First experience with sentinel-2 data for crop and tree species classifications in central europe. Remote Sensing 8(3) (2016). https://doi.org/10.3390/rs8030166
• 8. Lepot, M., Aubin, J.B., Clemens, F.H.: Interpolation in time series: An introductive overview of existing methods, their performance criteria and uncertainty assessment. Water 9(10), 796 (2017)
• 9. Nasrallah, A., Baghdadi, N., Mhawej, M., Faour, G., Darwish, T., Belhouchette, H., Darwich, S.: A novel approach for mapping wheat areas using high resolution sentinel-2 images. Sensors 18(7) (2018). https://doi.org/10.3390/s18072089
• 10. Piedelobo, L., Hernández-López, D., Ballesteros, R., Chakhar, A., Del Pozo, S., González-Aguilera, D., Moreno, M.A.: Scalable pixel-based crop classification combining sentinel-2 and landsat-8 data time series: Case study of the duero river basin. Agricultural Systems 171, 36–50 (2019). https://doi.org/https://doi.org/10.1016/j.agsy.2019.01.005
• 11. Wardlow, B., Egbert, S., Kastens, J.: Analysis of time-series modis 250 m vegetation index data for crop classification in the u.s. central great plains. Remote Sensing of Environment 108, 290–310 (06 2007). https://doi.org/10.1016/j.rse.2006.11.021
• 12. Yi, Z., Jia, L., Chen, Q.: Crop classification using multi-temporal sentinel-2 data in the shiyang river basin of china. Remote Sensing 12(24) (2020), https://www.mdpi.com/2072-4292/12/24/4052
• 13. You, N., Dong, J.: Examining earliest identifiable timing of crops using all available sentinel 1/2 imagery and google earth engine. ISPRS Journal of Photogrammetry and Remote Sensing 161, 109–123 (2020). https://doi.org/https://doi.org/10.1016/j.isprsjprs.2020.01.001

Uwagi

1. This work has been partially supported by the INdAM research group GNCS. The publication was created with the co-financing of the European Union-FSE-REACT-EU, PON Research and Innovation 2014-2020 DM1062/2021. This work has been partially supported by the industrial collaboration between ABACO Group SpA and UNIMORE.

2. Thematic Tracks Short Papers

3. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).