TriYield-XAI: An Explainable Ensemble model for Weather and Geospatial-Driven Aman, Aus, and Boro Yield Prediction in Bangladesh

We developed TriYield-XAI, a unified explainable framework for jointly predicting Aman, Aus, and Boro rice yields by integrating climatic, geographical, temporal, station-level, and historical yield information. We introduced a panel-aware hybrid ensemble architecture that combines Profile-KNN, matrix reconstruction, out-of-fold ensemble learning, meta-stacking, calibration, and residual correction to effectively capture complex spatio-temporal yield patterns and improve prediction performance. Furthermore, we applied SHAP-based explainable AI techniques to investigate the contribution of temporal, geographical, climatic, and station-specific factors to rice yield predictions, enhancing the interpretability and transparency of the proposed model.