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

Ensuring accurate predictions of wheat yield and nutritional content is vital for enhancing agricultural pro
ductivity 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
f
indings 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.