An Explainable AI-Driven Probabilistic Rainfall Forecasting and Harvest Optimization Framework for Precision Agriculture
This paper proposes XAI-PRFHO, an explainable AI framework that integrates a TCN–BiLSTM model with conformal prediction and multi-layer interpretability to generate calibrated probabilistic rainfall forecasts, which are then optimized via NSGA-III to significantly reduce weather-induced crop losses and increase yields in precision agriculture.