Applying RGB- and Thermal-Based Vegetation Indices from UAVs for High-Throughput Field Phenotyping of Drought Tolerance in Forage Grasses

The persistence and productivity of forage grasses, important sources for feed production, are threatened by climate change-induced drought. Breeding programs in search new drought tolerant grass varieties, but those still rely on time-consuming less consistent visual scoring breeders. In this study, we evaluate whether Unmanned Aerial Vehicle (UAV) based remote sensing can complement or replace breeder score. A field experiment was set up to test the tolerance genotypes from three common types two different species: Festuca arundinacea, diploid Lolium perenne tetraploid perenne. Drought stress imposed using mobile rainout shelters. UAV flights with RGB thermal sensors were conducted at five time points during experiment. Visual-based indices colour spaces selected that closely correlated Furthermore, several indices, particular H NDLab, HSV (Hue Saturation Value) CIELab (Commission Internationale de l’éclairage) space, respectively, displayed a broad-sense heritability as high higher than score, making these highly suited high-throughput phenotyping applications even thermal-based Crop Water Stress Index CWSI provided complementary information visual-based enabling analysis differences ecophysiological mechanisms coping reduced water availability between species ploidy levels. All species/types variation tolerance, which confirms there is sufficient selection within groups grasses. Our results confirmed better potential also showed more tolerant.