An explainable spatio-temporal deep learning framework for crop yield prediction and recommendation

Agriculture plays a critical role in ensuring global food security, yet crop yield variability driven by climate change, soil heterogeneity, and environmental fluctuations poses persistent challenges. Accurate yield forecasting and intelligent crop recommendation are essential for sustainable and efficient farm management. This study proposes a novel AI-driven framework for precise crop yield prediction and data-driven crop selection. The framework integrates systematic data preprocessing, hybrid feature selection, deep learning, and attention-based modeling to capture complex nonlinear relationships within agricultural datasets. Raw data, including soil properties, topography, climatic variables, and historical yield records, are processed using median-based imputation, normalization, and Z-score outlier detection to enhance reliability. A multi-stage hybrid feature selection approach combining Minimum Redundancy Maximum Weight, Sequential Forward Subset Selection, and Recursive Fisher Score identifies the most informative features while reducing redundancy. Yield prediction is performed using an attention-enhanced hybrid kernel Extreme Learning Machine (ELM). Crop recommendation is achieved through a Spatio-Temporal Explainable Group-Enhanced Transformer Network (STX-GTNET) optimized with the PantheraCobra metaheuristic. Model interpretability is ensured using Grad-CAM and Integrated Gradients. Experimental results demonstrate strong performance, achieving an RMSE of 281.6 and R² of 0.94 for yield prediction, and 98.4% accuracy with a 0.991 ROC-AUC for crop recommendation.