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.
Manual evaluation of crop injury to herbicides is time-consuming. Unmanned aerial systems (UAS) and high-resolution multispectral sensors and machine learning classification techniques have the potential to save time and improve precision in the evaluation of herbicide injury in crops, including grain sorghum (Sorghum bicolor L. Moench). The objectives of our research are to (1) evaluate three supervised classification algorithms [support vector machine (SVM), maximum likelihood, and random forest] for categorizing high-resolution UAS imagery to aid in data extraction and (2) evaluate the use of vegetative indices (VIs) collected from UAS imagery as an alternative to traditional methods of visible herbicide injury assessment in mesotrione-tolerant grain sorghum breeding trials. An experiment was conducted in a randomized complete block design using a factorial treatment arrangement of three genotypes by four mesotrione doses. Herbicide injury was rated visually on a scale of 0 (no injury) to 100 (complete plant mortality). The UAS flights were flown at 9, 15, 21, 27, and 35 days after treatment. Results show the SVM algorithm to be the most consistently accurate, and high correlations (r = − 0.83 to −0.94; p < 0.0001) were observed between the normalized difference VI and ground-measured herbicide injury. Therefore, we conclude that VIs collected with UAS coupled with machine learning image classification has the potential to be an effective method of evaluating mesotrione injury in grain sorghum.
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