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

Abstract

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.

Plant phenotyping relevance

UAVマルチソース画像・センサーデータと機械学習により、綿花の水分含量という植物生理形質を推定する手法が研究の中心であり、フェノタイピング手法の開発・適用に該当する。

Code and data availability

The paper's cotton water content (CWC) measurements and UAV-derived imagery/features are not publicly deposited; the authors state the data are available only on request due to privacy restrictions. The supplementary material (Figures S1–S5) is hosted on mdpi.com, which is not among the allowed URLs, and no author code

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