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Integrating meteorological and breeding data to predict maize yields using machine learning algorithms
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DOI：10.3389/fpls.2025.1722068.png)
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Abstract
En 中文
IntroductionAccurate crop yield prediction is vital for ensuring global food security; particularly amid growing environmental challenges such as climate change. Although deep learning (DL) methods have shown potential in yield prediction; they often demand large datasets and considerable computational resources.MethodsTo address these limitations; this study developed a machine learning (ML) model to predict maize hybrid yields by integrating meteorological data with breeder-level genetic information; specifically breeding values estimated using the best linear unbiased prediction (BLUP) method. Four commonly used ML algorithms—Random Forest (RF); XGBoost; Support Vector Regression (SVR); and Gaussian Process Regression (GPR)—were evaluated and optimized through hyperparameter tuning.ResultsAmong these models; the RF algorithm achieved the best performance; with a coefficient of determination (R²) of 0.64; a root mean square error (RMSE) of 1010.59 kg/ha; a mean absolute error (MAE) of 743.89 kg/ha; a relative RMSE (RRMSE) of 10.32%; and a mean absolute percentage error (MAPE) of 8.3%.DiscussionThese results demonstrate that the proposed RF-based model can provide accurate yield predictions for specific maize cultivars under diverse planting conditions. This predictive framework offers practical support for farmers in selecting well-adapted hybrids and serves as a cost-effective; efficient tool for breeders to identify high-yielding maize hybrids optimized for particular environments. Consequently; the model promotes smarter breeding strategies and more precise cultivation recommendations.
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