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

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dc.creatorKheir, Ahmed M.S.
dc.creatorGovind, Ajit
dc.creatorNangia, Vinay
dc.creatorEl-Maghraby, Maher A.
dc.creatorElnashar, Abdelrazek
dc.creatorAhmed, Mukhtar
dc.creatorAboelsoud, Hesham
dc.creatorMostafa, Rania
dc.creatorFeike, Til
dc.date2025-03-23
dc.date2025-06-18T16:27:31Z
dc.date2025-06-18T16:27:31Z
dc.date.accessioned2026-06-27T14:24:00Z
dc.descriptionEnsuring 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.
dc.formatapplication/pdf
dc.identifierhttps://hdl.handle.net/10568/175162
dc.identifier.urihttp://hdl.handle.net/123456789/78564
dc.languageen
dc.publisherElsevier
dc.rightsOpen Access
dc.sourceAhmed M. S. Kheir, Ajit Govind, Vinay Nangia, Maher A. El-Maghraby, Abdelrazek Elnashar, Mukhtar Ahmed, Hesham Aboelsoud, Rania Mostafa, Til Feike. (23/3/2025). Hybridization of process-based models, remote sensing, and machine learning for enhanced spatial predictions of wheat yield and quality. Computers and Electronics in Agriculture, 234.
dc.subjectuncertainty
dc.subjectzinc
dc.subjectiron
dc.subjectwheat
dc.subjectdssat
dc.subjectprotein
dc.subjectrandom forest regressor
dc.subjectnutrient concentration
dc.titleHybridization of process-based models, remote sensing, and machine learning for enhanced spatial predictions of wheat yield and quality
dc.typeJournal Article

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Centro Internacional para la Investigación Agrícola en las Áreas Secas (ICARDA)