Yield Prediction of Winter Wheat at Different Growth Stages Based on Machine Learning

• Published in: Agronomy (Basel) 2024-08, Vol.14 (8), p.1834
• Main Authors: Lou, Zhengfang, Lu, Xiaoping, Li, Siyi
• Format: Article
• Language: English
• Subjects: Accuracy; Agricultural practices; Agricultural production; Algorithms; Analysis; Crop yield; Crop yields; Deep learning; Food security; Food supply; Growth; Growth models; growth stage; Learning algorithms; Machine learning; Performance prediction; Precipitation; Precision farming; Prediction models; Radiation; Regression analysis; Regression models; Remote sensing; Satellite communications; Security management; Statistical analysis; Statistical models; Support vector machines; Sustainable agriculture; Sustainable practices; Triticum aestivum; Vegetation; Weather; Wheat; Winter wheat; yield prediction
• ISSN: 2073-4395; 2073-4395
• DOI: 10.3390/agronomy14081834

Accurate and timely prediction of crop yields is crucial for ensuring food security and promoting sustainable agricultural practices. This study developed a winter wheat yield prediction model using machine learning techniques, incorporating remote sensing data and statistical yield records from Henan Province, China. The core of the model is an ensemble voting regressor, which integrates ridge regression, gradient boosting, and random forest algorithms. This study optimized the hyperparameters of the ensemble voting regressor and conducted an in-depth comparison of its yield prediction performance with that of other mainstream machine learning models, assessing the impact of key hyperparameters on model accuracy. This study also explored the potential of yield prediction at different growth stages and its application in yield spatialization. The results demonstrate that the ensemble voting regressor performed exceptionally well throughout the entire growth period, with an R[sup.2] of 0.90, an RMSE of 439.21 kg/ha, and an MAE of 351.28 kg/ha. Notably, during the heading stage, the model’s prediction performance was particularly impressive, with an R[sup.2] of 0.81, an RMSE of 590.04 kg/ha, and an MAE of 478.38 kg/ha, surpassing models developed for other growth stages. Additionally, by establishing a yield spatialization model, this study mapped county-level yield predictions to the pixel level, visually illustrating the spatial differences in land productivity. These findings provide reliable technical support for winter wheat yield prediction and valuable references for crop yield estimation in precision agriculture.