ArticleScientific reports2026
A novel hybrid NSGA-III and machine learning framework for modeling wheat yield variability using climatic, edaphic, and nutritional drivers.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Accurate prediction of crop yield remains a critical research priority due to the increasing vulnerability of agricultural systems to climate change and the growing need for food security. In this study, we developed a hybrid modeling framework to predict irrigated wheat yield in Razavi Khorasan Province, Iran, using long-term (2004-2023) climatic, edaphic, and nutritional datasets comprising 47 variables collected across 17 counties. After preprocessing, feature selection was performed using an integrated approach combining Mutual Information (MI), Recursive Feature Elimination (RFE), and the advanced NSGA-III multi-objective optimization algorithm. Final yield prediction was conducted with a Stacking Regressor meta-learner incorporating LightGBM (LGBM) and a Deep Neural Network (DNN). The optimal subset of 10 features-Tmin, TS, K, Silt, EC, HCO₃, Mg, Prec_OC, AI_Clay, and a regional indicator variable (county_te) -achieved a test-set R
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