Water Stress Assessment of Cotton Cultivars Using Unmanned Aerial System Images

Abstract

Efficiently monitoring and quantifying the response of genotypes to water stress is critical in developing resilient crop cultivars in water-limited environments. The objective of this study was to assess water stress in cotton (Gossypium hirsutum L.) using high-resolution unmanned aerial system (UAS) images and identify water-stress-resistant cultivars in plant breeding. Various vegetation indices (VIs) and the crop water stress index (CWSI) derived from UAS images were applied to assess water stress in eight cotton cultivars under four irrigation treatments (90%, 60%, 30%, and 0% ET). The enhanced vegetation index (EVI), green normalized difference vegetation index (GNDVI), normalized difference red-edge index (NDRE), normalized difference vegetation index (NDVI), and crop water stress index (CWSI) were effective in detecting the effects of the irrigation treatments during the growing season. These VIs effectively differentiated cultivars in the middle and late seasons, while the CWSI detected cultivar differences in the mid–late growing season. The NDVI, GNDVI, NDRE, and EVI had a strong positive relationship with cotton yield starting from the mid-growing season in two years (R2 ranged from 0.90 to 0.95). Cultivars under each irrigation treatment were clustered into high-, medium-, and low-yielding groups based on the VIs at the mid–late growing seasons using hierarchical cluster analysis (HCA). The EVI derived from UAS images with high temporal and spatial resolutions can effectively screen drought-resistant cotton varieties under 30% and 60% irrigation treatments. The successful classification of cultivars based on UAS images provides critical information for selecting suitable varieties in plant breeding to optimize irrigation management based on water availability scenarios. This technology enables the targeted selection of water-stress-resistant cotton cultivars and facilitates site-specific crop management and yield prediction, ultimately contributing to precision irrigation and sustainable agriculture in water-limited environments.