Hybridization of process-based models, remote sensing, and machine learning for enhanced spatial predictions of wheat yield and quality

• Integrated PBM-ML-RS approach improves crop yield and nutritional quality predictions. • Hybridization of PBM-ML-RS, avoided PBM limitations in nutrient predictions. • Hybrid model reduces RMSE for yield, N, Fe, and Zn content compared to DSSAT models. • Spatial simulations support site-specific recommendations for enhanced productivity. • Higher variability in nutrient concentrations across different sites and cultivars. Ensuring accurate predictions of wheat yield and nutritional content is vital for enhancing agricultural productivity and food security. This study aims to improve wheat yield prediction by integrating process-based models (PBM), machine learning (ML), and remote sensing (RS) techniques. Three Decision Support System for Agrotechnology Transfer (DSSAT) wheat models were calibrated and evaluated using field data from three wheat cultivars grown over three seasons in diverse environments. We developed a hybrid PBM-ML-RS approach using polynomial regression to generate iron (Fe) and zinc (Zn) content from nitrogen predictions. The DSSAT wheat models slightly overestimated wheat yield but accurately predicted nitrogen content. The hybrid PBM-ML-RS approach closely estimated Fe and Zn content with a root mean square error (RMSE) of 0.42 t/ha for yield and 0.89 % for nitrogen content. The integration of ML and RS improved the prediction accuracy for Fe and Zn, achieving RMSE values of 0.35 % and 0.28 % respectively. Spatial simulations provided detailed geographic estimations of wheat yield and nutrient content, supporting site-specific management practices. This study demonstrates the potential of combining PBM, ML, and RS for comprehensive yield and nutrition prediction. The findings indicate a modest decrease in protein, Fe, and Zn concentrations with increasing grain yield, exhibiting high variability across different sites and cultivars. Future research should integrate additional data sources to enhance model robustness and applicability to other crops and regions, contributing to sustainable agriculture and food security.