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Volume 9
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Article

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

by
Shuyuan Zhang
1,2,
Haitao Jing
1,
Jihua Dong
2,
Yue Su
2,
Zhengdong Hu
3,
Longlong Bao
3,
Shiyu Fan
3,
Guldana Sarsen
3,
Tao Lin
3,* and
Xiuliang Jin
2,*
1
School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
2
State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
3
Xinjiang 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, China
*
Authors to whom correspondence should be addressed.
Drones 2025, 9(3), 163; https://doi.org/10.3390/drones9030163
Submission received: 11 January 2025 / Revised: 14 February 2025 / Accepted: 20 February 2025 / Published: 22 February 2025
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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.
Keywords: UAV; smart agriculture; irrigation management; machine learning UAV; smart agriculture; irrigation management; machine learning

Share and Cite

MDPI and ACS Style

Zhang, S.; Jing, H.; Dong, J.; Su, Y.; Hu, Z.; Bao, L.; Fan, S.; Sarsen, G.; Lin, T.; Jin, X. Accurate Estimation of Plant Water Content in Cotton Using UAV Multi-Source and Multi-Stage Data. Drones 2025, 9, 163. https://doi.org/10.3390/drones9030163

AMA Style

Zhang S, Jing H, Dong J, Su Y, Hu Z, Bao L, Fan S, Sarsen G, Lin T, Jin X. Accurate Estimation of Plant Water Content in Cotton Using UAV Multi-Source and Multi-Stage Data. Drones. 2025; 9(3):163. https://doi.org/10.3390/drones9030163

Chicago/Turabian Style

Zhang, Shuyuan, Haitao Jing, Jihua Dong, Yue Su, Zhengdong Hu, Longlong Bao, Shiyu Fan, Guldana Sarsen, Tao Lin, and Xiuliang Jin. 2025. "Accurate Estimation of Plant Water Content in Cotton Using UAV Multi-Source and Multi-Stage Data" Drones 9, no. 3: 163. https://doi.org/10.3390/drones9030163

APA Style

Zhang, S., Jing, H., Dong, J., Su, Y., Hu, Z., Bao, L., Fan, S., Sarsen, G., Lin, T., & Jin, X. (2025). Accurate Estimation of Plant Water Content in Cotton Using UAV Multi-Source and Multi-Stage Data. Drones, 9(3), 163. https://doi.org/10.3390/drones9030163

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