数字农科院2.0

CatBoost Improves Inversion Accuracy of Plant Water Status in Winter Wheat Using Ratio Vegetation Index

文献类型： 外文期刊

作者： Dong, Bingyan;Ma, Shouchen;Gao, Zhenhao;Qin, Anzhen

作者机构：

关键词： precision agriculture;random forest;decision trees;light gradient boosting machine;categorical boosting algorithm;remote sensing;North China Plain

期刊名称： APPLIED SCIENCES-BASEL

ISSN：

年卷期： 2025 年 15 卷 21 期

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收录情况： 无来源刊（2025版）

摘要： The accurate monitoring of crop water status is critical for optimizing irrigation strategies in winter wheat. Compared with satellite remote sensing, unmanned aerial vehicle (UAV) technology offers superior spatial resolution, temporal flexibility, and controllable data acquisition, making it an ideal choice for the small-scale monitoring of crop water status. During 2023-2025, field experiments were conducted to predict crop water status using UAV images in the North China Plain (NCP). Thirteen vegetation indices were calculated and their correlations with observed crop water content (CWC) and equivalent water thickness (EWT) were analyzed. Four machine learning (ML) models, namely, random forest (RF), decision tree (DT), LightGBM, and CatBoost, were evaluated for their inversion accuracy with regard to CWC and EWT in the 2024-2025 growing season of winter wheat. The results show that the ratio vegetation index (RVI, NIR/R) exhibited the strongest correlation with CWC (R = 0.97) during critical growth stages. Among the ML models, CatBoost demonstrated superior performance, achieving R2 values of 0.992 (CWC) and 0.962 (EWT) in training datasets, with corresponding RMSE values of 0.012% and 0.1907 g cm-2, respectively. The model maintained robust performance in testing (R2 = 0.893 for CWC, and R2 = 0.961 for EWT), outperforming conventional approaches like RF and DT. High-resolution (5 cm) inversion maps successfully identified spatial variability in crop water status across experimental plots. The CatBoost-RVI framework proved particularly effective during the booting and flowering stages, providing reliable references for precision irrigation management in the NCP.

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