Prediction of Chlorophyll Content in Multi-Temporal Winter Wheat Based on Multispectral and Machine Learning - Wang, Wei; Cheng, Yukun; Ren, Yi; Zhang, Zhihui; Geng, Hongwei (Frontiers in Plant Science, 2022)
In order to obtain the canopy chlorophyll content of winter wheat in a rapid and non-destructive high-throughput manner, the study was conducted on winter wheat in Xinjiang Manas Experimental Base in 2021, and the multispectral images of two water treatments normal irrigation (NI) and drought stress(DS) in three key fertility stages (heading, flowering and filling) of winter wheat were obtained by DJI P4M unmanned arial vehicle (UAV) and flag leaf chlorophyll content(CC) data of different genotypes in the field were obtained by SPAD-502 Plus chlorophyll meter. Firstly, the CC distribution of different genotypes were studied, then 13 vegetation indices combined with the RandomForest algorithm and correlation evaluation of CC and 14 vegetation indices were used for vegetation index preference. Finally, preferential vegetation indices and nine machine learning algorithms, Ridge regression with cross-validation (RidgeCV), Ridge, AdaboostRegression, Bagging_Regressor, K_Neighbor, Gradient_Boosting_Regressor, RandomForest, Support Vector Machine (SVM), and Least absolute shrinkage and selection operator (Lasso), were preferentially selected to construct the CC estimation models under two water treatments at three different fertility stages, which were evaluated by correlation coefficient (r), root mean square error (RMSE) and the normalized root mean square error (NRMSE) to select the optimal estimation model. The results showed that the CC values under normal irrigation were higher than those under water limitation treatment at different fertility stages; several vegetation indices and CC values showed highly significant correlation, with the highest correlation reaching 0.51; in the prediction model construction of CC values, different models under normal irrigation and water limitation treatment had high estimation accuracy, among which the model with the highest prediction accuracy under normal irrigation was at the heading stage. The highest precision of the model prediction under normal irrigation was in the RidgeCV model (r=0.63, RMSE=3.28, NRMSE=16.2%) and the highest precision of the model prediction under water limitation treatment was in the SVM model (r=0.63, RMSE=3.47, NRMSE=19.2%).