Sarkar, Sayantan2022-06-302022-06-302021-01-05vt_gsexam:28631http://hdl.handle.net/10919/111060Peanut (Arachis hypogaea L.) is an important food crop in the USA and worldwide with high net returns but yield in excess of 4500 kg ha-1 is needed to offset the production costs. Because yield is limited by biotic and abiotic stresses, cultivars with stress tolerance are needed to optimize yield. The U.S. peanut mini-core germplasm collection is a valuable resource that breeders can use to improve stress tolerance in peanut. Phenotyping for plant height, leaf area, and leaf wilting have been used as proxies for the desired tolerance traits. However, proximal data collection, i.e. measurements are taken on individual plants or in the proximity, is slow. Remote data collection and machine learning techniques for analysis offer a high-throughput phenotyping (HTP) alternative to manual measurements that could help breeding for stress tolerance. The objectives of this study were to 1) develop HTP methods using aerial remote sensing; 2) evaluate the mini-core collection in SE Virginia; and 3) perform a detailed physiological analysis on a sub-set of 28 accessions from the mini-core collection under drought stress, i.e. the sub-set was selected based on contrasting differences under drought in three states, Virginia, Texas, and Oklahoma. To address these objectives, replicated experiments were performed in the field at the Tidewater Agricultural Research and Extension Center in Suffolk, VA, in 2017, 2018, and 2019, under rainfed, irrigated, and controlled conditions using rainout shelters to induce drought. Proximal data collection involved physiological, morphological, and yield measurements. Remote data collection was performed aerially and included collection of red-green-blue (RGB) images and canopy reflectance in the visible, near infra-red, and infra-red spectra. This information was used to estimate plant characteristics related to growth and drought tolerance. Under objective 1), we developed HTP for plant height with 85-95% accuracy, LAI with 85-88% accuracy, and wilting with 91-99% accuracy; this was done with significant reduction of time as compared to proximal data collection. Under objectives 2) and 3), we determined that shorter genotypes were more drought tolerant than taller genotypes; and identified CC650 less wilted and with increased carbon assimilation, electron transport, quantum efficiency, and yield than other accessions.ETDCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 InternationalPeanutplant physiologyremote sensinghigh-throughput phenotyping (HTP)precision agricultureDissertation