Evaluation of the U.S. Peanut Germplasm Mini-Core Collection in the Virginia-Carolina Region Using Traditional and New High-Throughput Methods

Abstract

Peanut (Arachis hypogaea L.) is an important food crop for the U.S. and the world. The Virginia-Carolina (VC) region (Virginia, North Carolina, and South Carolina) is an important peanut-growing region of the U.S and is affected by numerous biotic and abiotic stresses. Identification of stress-resistant germplasm, along with improved phenotyping methods, are important steps toward developing improved cultivars. Our objective in 2017 and 2018 was to assess the U.S. mini-core collection for desirable traits, a valuable source for resistant germplasm under limited water conditions. Accessions were evaluated using traditional and high-throughput phenotyping (HTP) techniques, and the suitability of HTP methods as indirect selection tools was assessed. Traditional phenotyping methods included stand count, plant height, lateral branch growth, normalized difference vegetation index (NDVI), canopy temperature depression (CTD), leaf wilting, fungal and viral disease, thrips rating, post-digging in-shell sprouting, and pod yield. The HTP method included 48 aerial vegetation indices (VIs), which were derived using red, blue, green, and near-infrared reflectance; color space indices were collected using an octocopter drone at the same time, with traditional phenotyping. Both phenotypings were done 10 times between 4 and 16 weeks after planting. Accessions had yields comparable to high yielding checks. Correlation coefficients up to 0.8 were identified for several Vis, with yield indicating their suitability for indirect phenotyping. Broad-sense heritability (H2) was further calculated to assess the suitability of particular VIs to enable genetic gains. VIs could be used successfully as surrogates for the physiological and agronomic trait selection in peanuts. Further, this study indicates that UAV-based sensors have potential for measuring physiologic and agronomic characteristics measured for peanut breeding, variable rate input application, real time decision making, and precision agriculture applications.

Phenotyping Peanut Drought Stress with Aerial Remote-Sensing and Crop Index Data

“…The rainfall, air temperature, and relative humidity (RH) were recorded daily from a weather station adjacent to the plots from 1 May until 30 September. Daily growing-degree days (GDD 13 s) were calculated from the minimum and maximum daily temperatures, using a base temperature of 13 • C [42]. Only positive values were used; negative values were taken as 0, and temperatures above 35 • C were recorded as 35 • C.…”

“…Calibration was performed using a reflectance panel with eight different shades from white to black [42]. The DNs of the eight shades were recorded for red, green, blue, and NIR rasters from each orthomosaic.…”

Application of Image Processing in Agriculture

“…Näsi et al [4] used 27 multispectral and RGB images acquired with UAVs to assess their association with soil quality indicators, concluding that soil indicators explain the variability in UAV images in the majority of cases. Sarkar et al [5] assessed several vegetation indices derived from multispectral aerial images collected a UAV, for indirect phenotyping of peanut crops to identify resistant germplasm under water stress. Soares et al [6] developed a method for the early detection of coffee leaf rust using multispectral images acquired with UAV using a support vector machine (SVM) algorithm.…”

Remote sensing continuity: a comparison of HTP platforms and potential challenges with field applications

“…These factors influence reflectance values and relative index effectiveness ( Wientjes et al., 2017 ; Lozada et al., 2020 ; Herr et al., 2023 ). Vegetation indices have many applications in capturing routine trait estimates like plot quality, biotic, and abiotic stress ( Sankaran et al., 2015a ; Guo et al., 2021 ; Sarkar et al., 2022 ; Sapkota et al., 2023 ), as well as previously infeasible traits like chlorophyll content and nitrogen content ( Xie and Yang, 2020 ; Yin et al., 2022 ). Unlike vegetation indices, water indices such as Normalized Water Index (NWI) use infrared range reflectance to evaluate stomatal conductance and overall photosynthetic efficiency ( Babar et al., 2006 ).…”