Optimizing Crop Yield Predictions through Satellite Data Fusion and Machine Learning
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Abstract
Accurate crop yield estimation is crucial for sustainable agriculture and food security, especially in Maharashtra, where climate variability significantly impacts crop growth. This study utilizes satellite data from MODIS, Landsat, Sentinel-1, and Sentinel-2 to predict the yields of 22 crops across 36 districts. Machine learning models, including Random Forest, Gradient Boosting, and SVM, were evaluated using RMSE, MAE, and R2 metrics. Random Forest outperformed the others, achieving R2 values above 0.70 for all crops, with a peak R2 of 0.93. Incorporating seasonal and permuted feature data further enhanced predictions, demonstrating the efficacy of integrating satellite data and machine learning for agriculture. Keywords: Machine learning, MODIS, Landsat-8, Sentinel-2, Sentinel-1, crop yield, features, vegetation indices.
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