Summary
Indian agriculture currently faces a dual challenge: adapting to unpredictable climate patterns while addressing widespread nutritional insecurity. We propose a machine learning framework which optimizes crop rotation by balancing yield maximization with specific nutritional targets. Unlike traditional models that focus solely on productivity, our system integrates Random Forest (RF) regression for rainfall forecasting with XGBoostbased crop yield prediction to optimize the output of five critical nutrients: protein, iron, calcium, zinc, and folate. We first model district-specific rainfall patterns using temporal and geographic data, achieving a Coefficient of Determination ($R^{2}$) of 0.816. These meteorological forecasts then feed into an XGBoost yield predictor ($R^{2}$ of 0.9388), wh
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