CROPINTEL DATASET: A COMPREHENSIVE AGRO-ENVIRONMENTAL RESOURCE FOR CROP CLASSIFICATION, IRRIGATION PLANNING, AND YIELD ANALYSIS

The growing demand for sustainable agricultural intensification necessitates advanced tools for optimizing crop selection and resource management. This study presents an integrated analytical framework using machine learning to derive agro-environmental intelligence from the comprehensive CropIntel dataset. We evaluated a suite of over fifteen machine learning algorithms for three critical precision agriculture tasks: crop classification, seasonal irrigation requirement prediction, and yield potential forecasting. The results demonstrate the exceptional capability of tree-based models in this domain. Notably, Decision Tree, Bagging, and Gaussian Naïve Bayes classifiers achieved near-perfect accuracy (Acc≈99.7%) in identifying suitable crops based on climatic and edaphic conditions. For regression tasks, LightGBM and Histogram Gradient Boosting models proved most effective, explaining approximately 84% of the variance in irrigation needs (R²≈0.84) and about 70% of the variance in yield potential (R²≈0.696). These findings underscore the potential of machine learning to create powerful decision support systems that can guide crop selection, optimize water allocation, and provide reliable yield forecasts. This research contributes to a holistic, data-driven methodology that can enhance the efficiency and sustainability of agricultural practices.