Coefficient of variation method combined with XGboost ensemble model for wheat growth monitoring by Xinyan Li, Changchun Li, Fuchen Guo, Xiaopeng Meng, Yanghua Liu, Fang Ren

“…The three models of Random Forest, Ridge Regression and XGBoost were used to construct the wheat growth inversion model with the best effect at the flowering stage, and the XGBoost model had the highest inversion accuracy when comparing in the same period, with the training and test sets reaching 0.904 and 0.870, and the RMSEs were 0.050 and 0.079, so that the XGBoost model can be used as an effective method of monitoring the growth of wheat. …”
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