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

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

Cotton (<i>Gossypium hirsutum</i> 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 co...

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Bibliographic Details
Main Authors: Shuyuan Zhang, Haitao Jing, Jihua Dong, Yue Su, Zhengdong Hu, Longlong Bao, Shiyu Fan, Guldana Sarsen, Tao Lin, Xiuliang Jin
Format: Article
Language:English
Published: MDPI AG 2025-02-01
Series:Drones
Subjects:
UAV
smart agriculture
irrigation management
machine learning
Online Access:https://www.mdpi.com/2504-446X/9/3/163
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Summary:Cotton (<i>Gossypium hirsutum</i> 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 (R<sup>2</sup> = 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.
ISSN:2504-446X

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