Simulation analysis and yield prediction of wheat growth based on ensemble learning algorithm and WOFOST model
In response to the limitations of traditional single crop growth models and machine learning models in prediction,the WO-FOST 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 experimen-tal 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 activi-ties.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.