关键词: 集成学习算法, WOFOST模型, 小麦生长, 模拟, 产量预测, 耦合

Abstract: In response to the limitations of traditional single crop growth models and machine learning models in prediction, the WOFOST model was combined with irrigation models, and an ensemble learning algorithm was used to establish a multi model coupling system （WOFOST coupling model）,simulated experiments were conducted using data from NASA from 1990 to 2020, and experimental results were presented in 2006 and 2018. The results showed that the leaf area index and total biomass of wheat in the WOFOST coupled model were higher than those in the WOFOST model, and the WOFOST coupled model was closer to actual production activities.The MAE and MSE of the coupled algorithm were lower than those of the Bagging, Boosting, and Stacking algorithms, with values of 2.836 and 7.581, respectively. The R² was higher than that of the Bagging, Boosting, and Stacking algorithms, with a value as high as 0.942. The WOFOST coupled model provided a more comprehensive and accurate simulation of crop growth status, improving the accuracy and credibility of yield prediction.

Key words: ensemble learning algorithm, WOFOST model, wheat growth, simulation, yield prediction, coupling