中国农业科学院机构知识库

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

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文献类型：外文期刊

作者： Zhang, Shuyuan ¹ ; Jing, Haitao ¹ ; Dong, Jihua ² ; Su, Yue ² ; Hu, Zhengdong ³ ; Bao, Longlong ³ ; Fan, Shiyu ³ ; Sarsen, Guldana ³ ; Lin, Tao ³ ; Jin, Xiuliang ² ;

作者机构： 1.Henan Polytech Univ, Sch Surveying & Land Informat Engn, Jiaozuo 454000, Peoples R China

2.Chinese Acad Agr Sci, Inst Crop Sci, State Key Lab Crop Gene Resources & Breeding, Beijing 100081, Peoples R China

3.Xinjiang Uyghur Autonomous Reg Acad Agr Sci, Cotton Res Inst, Xinjiang Key Lab Cotton Genet Improvement & Intell, Cotton Res Inst,Xinjiang Key Lab Cotton Genet Impr, Urumqi 830000, Peoples R China

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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