Identification of Cotton Defoliation Sensitive Materials Based on UAV Multispectral Imaging

Yuantao Guo, Hu Zhang, Wenju Gao, Quanjia Chen, Qiyu Chang, Jinsheng Wang, Qingtao Zeng, Haijiang Xu and Qin Chen ()
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Yuantao Guo: Key Laboratory of Xinjiang Crop Biological Breeding, College of Agronomy, Xinjiang Agricultural University, Wulumuqi 830052, China
Hu Zhang: Key Laboratory of Xinjiang Crop Biological Breeding, College of Agronomy, Xinjiang Agricultural University, Wulumuqi 830052, China
Wenju Gao: Key Laboratory of Xinjiang Crop Biological Breeding, College of Agronomy, Xinjiang Agricultural University, Wulumuqi 830052, China
Quanjia Chen: Key Laboratory of Xinjiang Crop Biological Breeding, College of Agronomy, Xinjiang Agricultural University, Wulumuqi 830052, China
Qiyu Chang: Key Laboratory of Xinjiang Crop Biological Breeding, College of Agronomy, Xinjiang Agricultural University, Wulumuqi 830052, China
Jinsheng Wang: Key Laboratory of Xinjiang Crop Biological Breeding, College of Agronomy, Xinjiang Agricultural University, Wulumuqi 830052, China
Qingtao Zeng: The 7th Division of Agricultural Sciences Institute, Xinjiang Production and Construction Corps, Kuitun 833200, China
Haijiang Xu: Cotton Research Institute of Xinjiang Uyghur Autonomous Region Academy of Agricultural Sciences, Wulumuqi 830091, China
Qin Chen: Key Laboratory of Xinjiang Crop Biological Breeding, College of Agronomy, Xinjiang Agricultural University, Wulumuqi 830052, China

Agriculture, 2025, vol. 15, issue 9, 1-19

Abstract: (1) Background: This study aims to analyze the defoliation and boll opening performance of 123 upland cotton germplasm resources after spraying defoliant, using multispectral data to select relevant vegetation indices and identify germplasm resources sensitive to defoliants, providing methods for cotton variety improvement and high-quality parental resources. (2) Methods: 123 historical upland cotton germplasm resources from Xinjiang were selected, and the defoliation and boll opening of cotton leaves were investigated at 0, 4, 8, 12, 16, and 20 days after defoliant application. Simultaneously, multispectral digital images were collected using drones to obtain 12 vegetation indices. Based on defoliation rate, the optimal vegetation index was selected, and defoliant-sensitive germplasm resources were identified. (3) Results: The most significant difference in defoliation rate of cotton germplasm resources occurred 16 days after application. Cluster analysis grouped the 123 breeding materials into three categories, with Class I showing the best defoliation effect. Among the 12 vegetation indices, the Plant Senescence Reflectance Index (PSRI) has the highest correlation coefficient with the defoliation rate; and when the PSRI value is higher, the defoliation effect of the material is better. By comparing the traditional investigation method with the unmanned aerial vehicle multispectral technology, 15 cotton materials sensitive to defoliants were determined, with a defoliation rate of over 85%, a lint percentage ranging from 76.67% to 98.04%, and a PSRI value ranging from 0.1607 to 0.1984. (4) Conclusions: The study found that the vegetation index with sensitive response can be used as an effective indicator to evaluate the sensitivity of cotton breeding materials to defoliants. Using an unmanned aerial vehicle (UAV) equipped with vegetation indices for screening shows a high consistency with the manual investigation and screening method in screening excellent defoliation materials; it proves that it is feasible to screen cotton breeding materials with excellent defoliation effects using UAV multispectral technology.

Keywords: cotton; UAV; multispectral; defoliation rate; vegetation index (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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