Accurate Estimation of Plant Water Content in Cotton Using UAV Multi-Source and Multi-Stage Data

文献类型： 外文期刊

第一作者： Zhang, Shuyuan

作者： Zhang, Shuyuan;Jing, Haitao;Zhang, Shuyuan;Dong, Jihua;Su, Yue;Jin, Xiuliang;Hu, Zhengdong;Bao, Longlong;Fan, Shiyu;Sarsen, Guldana;Lin, Tao

作者机构：

关键词： UAV; smart agriculture; irrigation management; machine learning

期刊名称：DRONES （ 影响因子：4.8； 五年影响因子：5.0 ）

ISSN：

年卷期： 2025 年 9 卷 3 期

页码：

收录情况： SCI

摘要： Cotton (Gossypium hirsutum L.), as a significant economic crop, has undergone significant modernization in planting methods, and its smart irrigation management relies heavily on accurate cotton water content (CWC) estimation. Existing ground-based methods for measuring CWC are constrained by their limited scope and high monitoring costs. Although the development of unmanned aerial vehicle (UAV) technology has provided a new opportunity for large-scale CWC measurements, there remains a gap in the study of CWC estimation in cotton using multi-source and multi-stage data. In this study, we used UAV-based data, including texture features, vegetation indices, and a heat index, and applied four machine learning algorithms, i.e., partial least-squares regression (PLSR), support vector regression (SVR), random forest regression (RFR), and extreme gradient boosting (XGB), to estimate CWC. The findings demonstrate that in a single growth stage, the boll setting stage performs the best, and multi-source and multi-stage inputs can improve the accuracy of CWC estimation, with the best performance of XGB (R2 = 0.860). Overall, this study highlights that the synergistic use of multi-source and multi-stage data can effectively improve CWC estimation in cotton, suggesting UAV-based data will lead to a brighter future for precision agriculture.

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