(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 re-sources 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. Sim-ultaneously, 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 coef-ficient with the defoliation rate; and when the PSRI value is higher, the defoliation ef-fect of the material is better. Using drone multispectral technology, 15 defoli-ant-sensitive cotton materials were identified, with defoliation rates exceeding 85%, boll opening rates ranging from 76.67% to 98.04%, and PSRI values between 0.1607 and 0.1984.(4) Conclusions: The study found that vegetation indices with sensitive re-sponses can serve as effective indicators for evaluating the sensitivity of cotton breed-ing materials to defoliants. The combined analysis of traditional survey methods and PSRI classification demonstrates that using drone multispectral technology as a sub-stitute for manual methods in large-scale, rapid monitoring and selection of cotton breeding materials with excellent defoliation effects is feasible.