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Machine Learning Applied to Crop Mapping in Rice Varieties Using Spectral Images

Rubén Simeón, Kenza El Masslouhi, Alba Agenjos-Moreno, Beatriz Ricarte, Antonio Uris (), Belen Franch, Constanza Rubio and Alberto San Bautista
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Rubén Simeón: Centro Valenciano de Estudios Sobre el Riego (CVER), Universitat Politècnica de València, 46022 Valencia, Spain
Kenza El Masslouhi: Department of Plant Production, Protection and Biotechnology (DPPBV), Institut Agronomique et Vétérinaire Hassan II (IAV Hassan II), Rabat 16000, Morocco
Alba Agenjos-Moreno: Centro Valenciano de Estudios Sobre el Riego (CVER), Universitat Politècnica de València, 46022 Valencia, Spain
Beatriz Ricarte: Institut de Matemàtica Multidisciplinar, Universitat Politècnica de València, 46022 Valencia, Spain
Antonio Uris: Physics Technologies Research Centre, Universitat Politècnica de València, 46022 Valencia, Spain
Belen Franch: Global Change Unit, Image Processing Laboratory, Universitat de València, 46980 Valencia, Spain
Constanza Rubio: Physics Technologies Research Centre, Universitat Politècnica de València, 46022 Valencia, Spain
Alberto San Bautista: Centro Valenciano de Estudios Sobre el Riego (CVER), Universitat Politècnica de València, 46022 Valencia, Spain

Agriculture, 2025, vol. 15, issue 17, 1-20

Abstract: Global food security is increasingly challenged by climate change and the availability of arable land. This situation calls for improved crop monitoring and management strategies. Rice is a staple food for nearly half of the world’s population and a significant source of calories. Accurately identifying rice varieties is crucial for maintaining varietal purity, planning agricultural activities, and enhancing genetic improvement strategies. This study evaluates the effectiveness of machine learning algorithms to identify the most effective approach to predicting rice varieties, using multitemporal Sentinel-2 images in the Marismas del Guadalquivir of Sevilla, Spain. Spectral reflectance data were collected from ten Sentinel-2 bands, which include visible, red-edge, near-infrared, and shortwave infrared regions, at two key phenological stages: tillering and reproduction. The models were trained on pixel-level data from the growing seasons of 2021 and 2024, and they were evaluated using a test set from 2022. Four classifiers were compared: random forest, XGBoost, K-nearest neighbors, and logistic regression. Performance was assessed based on accuracy, precision, recall, specificity and F1 score. Non-linear models outperformed linear ones. The highest performance was achieved with the Random Forest classifier during the reproduction phase, reaching an exceptional accuracy of 0.94 using all bands or only the most informative subset (red edge, NIR, and SWIR). This classifier also maintained excellent accuracy (0.93 and 0.92) during the initial tillering phase. This fact demonstrates that it is possible to perform reliable varietal mapping in the early stages of the growing season.

Keywords: Sentinel-2; rice; machine learning; pixel classification; variety mapping; precision agriculture; crop mapping; remote sensing (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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