Explainable AI for Assessing Climate Change Impacts on Rice Yield in India: A Hybrid LSTM-SHAP Approach

Climate change threatens rice cultivation in India, a cornerstone of food security for over 1.4 billion people. This study proposes an Explainable Artificial Intelligence (XAI) framework integrating Long Short-Term Memory (LSTM) networks with SHAP (SHapley Additive exPlanations) to predict rice yield and interpret the impact of climate variables (temperature, precipitation, humidity, soil moisture). Using historical data (2000–2020) from the India Meteorological Department (IMD) and Ministry of Agriculture, the model achieves an R² of 0.88, Mean Absolute Error (MAE) of 0.11 tons/ha, and Root Mean Squared Error (RMSE) of 0.15 tons/ha. SHAP analysis identifies temperature (42%) and precipitation (33%) as primary drivers of yield variability. This framework provides transparent, data-driven insights, supporting farmers and policymakers in developing climate-resilient agricultural strategies aligned with India’s sustainability goals.