High-resolution unmanned aircraft systems imagery for stay-green characterization in grain sorghum (Sorghum bicolor L.)

Abstract

Grain sorghum (Sorghum bicolor [L]. Moench) is a crucial crop to the world’s semiarid regions, as it can produce grain and biomass yields in precipitation-limited environments. Many genotypes have a characterized form of drought resistance known as the stay-green (SG) trait, enabling sorghum plants to resist postflowering drought stress that can severely reduce yields. Breeding for SG sorghum lines is considered vital for sorghum breeders around the world, but selecting for SG traits currently relies on methods that are labor-intensive and time-consuming. Using unmanned aerial systems capable of capturing high-resolution imagery offers a solution for reducing the time and energy required to select for these traits. A field study was conducted in Manhattan, Kansas, where 20 Pioneer® sorghum hybrids were planted in a randomized complete block design with three replications per hybrid. Imagery was collected with a DJI® Matrice 200™ equipped with a MicaSense® RedEdge-MX™ multispectral camera. Flight altitude was 30 m, and flights were collected under clear, sunny skies within ±2.5  h of solar noon. Ground-measured data included visual senescence ratings, fresh and dry plant biomass, leaf area index, and final grain yield. After correlation and regression analysis, results indicated significant relationships with the near-infrared spectral band with fresh and dry plant biomass samples, the green normalized difference vegetation index scores at flowering were the most related to final grain yield, and the visible atmospherically resistant index was the most related to visual senescence scores. Significant spectral band/vegetative indices were clustered into groups, and significant differences were found between various traits. We have developed a methodology for SG sorghum growers to collect, process, and extract data for more efficient identification of traits of interest.