| In order to explore the feasibility of predicting winter wheat yield based on multi-source remote sensing data and machine learning algorithms,field experiments were carried out with 70 F7 families derived from Zhongmai 175/Lunxuan 987 were used as materials. The spectral data of winter wheat during grain filling period were obtained through UAV remote sensing platform,ground phenotypic vehicle platform and handheld canopy identification platform. Four machine learning methods and integrated methods were used to establish yield prediction models. The results showed that among the 61 spectral indices,except for MCARI,DSI,and PVI,the other indices were significantly correlated with yield,and the combination of 700 nm and 800 nm hyperspectral indices predicted yield more accurately. Compared with hyperspectral and multispectral,the RGB sensor had the highest yield prediction accuracy,with an average coefficient of determination (r2) of 0.74 and an average root mean square error (RMSE) of 517.78 kg·hm-2. Compared with the three traditional machine learning algorithms of decision tree (DT),random forest (RF) and support vector machine (SVM),the ridge regression (RR) algorithm had the highest accuracy in predicting yield,with an average r2 of 0.73 and an average RMSE of 516.1 kg·hm-2. Compared with the single traditional machine learning algorithm model,the prediction accuracy of DT,RF,SVM,and RR combined with the integrated algorithm was high and stable,with an r2 of up to 0.77 and a low RMSE. The prediction accuracy of sensor integration algorithm,which was composed of four machine learning algorithms of SVM,RF,DT and RR and four sensors of RGB,ASD,UAV and UGV,was improved,with r2 of 0.79,where the RMSE was reduced to 469.98 kg·hm-2. Therefore,using the stacking integration method to combine different algorithms and sensors can effectively improve the yield prediction accuracy. |
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