Статья

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

Shuyuan ZhangSchool of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, ChinaHaitao JingSchool of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, ChinaJihua DongState Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaYue SuState Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaZhicheng HuXinjiang Cotton Technology Innovation Center, Xinjiang Key Laboratory of Cotton Genetic Improvement and Intelligent Production, Cotton Research Institute of Xinjiang Uyghur Autonomous Region Academy of Agricultural Sciences, National Cotton Engineering Technology Research Center, Urumqi 830091, ChinaLonglong BaoXinjiang Cotton Technology Innovation Center, Xinjiang Key Laboratory of Cotton Genetic Improvement and Intelligent Production, Cotton Research Institute of Xinjiang Uyghur Autonomous Region Academy of Agricultural Sciences, National Cotton Engineering Technology Research Center, Urumqi 830091, ChinaShiyu FanXinjiang Cotton Technology Innovation Center, Xinjiang Key Laboratory of Cotton Genetic Improvement and Intelligent Production, Cotton Research Institute of Xinjiang Uyghur Autonomous Region Academy of Agricultural Sciences, National Cotton Engineering Technology Research Center, Urumqi 830091, ChinaGuldana SarsenXinjiang Cotton Technology Innovation Center, Xinjiang Key Laboratory of Cotton Genetic Improvement and Intelligent Production, Cotton Research Institute of Xinjiang Uyghur Autonomous Region Academy of Agricultural Sciences, National Cotton Engineering Technology Research Center, Urumqi 830091, ChinaTao LinXinjiang Cotton Technology Innovation Center, Xinjiang Key Laboratory of Cotton Genetic Improvement and Intelligent Production, Cotton Research Institute of Xinjiang Uyghur Autonomous Region Academy of Agricultural Sciences, National Cotton Engineering Technology Research Center, Urumqi 830091, ChinaXiuliang JinState Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China

2025en

ABI

Аннотация

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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Идентификаторы

DOI: 10.3390/drones9030163

Цитирования и источники

Цитирований: 2Использованных источников: 0

Показатели — AkademScholar