首页|Henan Polytechnic University Researcher Yields New Study Findings on Machine Lea rning (Yield Prediction of Winter Wheat at Different Growth Stages Based on Mach ine Learning)
Henan Polytechnic University Researcher Yields New Study Findings on Machine Lea rning (Yield Prediction of Winter Wheat at Different Growth Stages Based on Mach ine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on artificial in telligence have been published. According to news reporting out of Jiaozuo, Peop le's Republic of China, by NewsRx editors, research stated, "Accurate and timely prediction of crop yields is crucial for ensuring food security and promoting s ustainable agricultural practices." Financial supporters for this research include National Key Research And Develop ment Plan of China. Our news correspondents obtained a quote from the research from Henan Polytechni c University: "This study developed a winter wheat yield prediction model using machine learning techniques, incorporating remote sensing data and statistical y ield records from Henan Province, China. The core of the model is an ensemble vo ting regressor, which integrates ridge regression, gradient boosting, and random forest algorithms. This study optimized the hyperparameters of the ensemble vot ing regressor and conducted an in-depth comparison of its yield prediction perfo rmance with that of other mainstream machine learning models, assessing the impa ct of key hyperparameters on model accuracy. This study also explored the potent ial of yield prediction at different growth stages and its application in yield spatialization. The results demonstrate that the ensemble voting regressor perfo rmed exceptionally well throughout the entire growth period, with an R2 of 0.90, an RMSE of 439.21 kg/ha, and an MAE of 351.28 kg/ha. Notably, during the headin g stage, the model's prediction performance was particularly impressive, with an R2 of 0.81, an RMSE of 590.04 kg/ha, and an MAE of 478.38 kg/ha, surpassing mod els developed for other growth stages."
Henan Polytechnic UniversityJiaozuoPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
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