Location: Peanut and Small Grains Research UnitTitle: Vegetation indices enable indirect phenotyping of peanut physiologic and agronomic characteristics
|BALOTA, MARIA - Virginia Tech|
|SARKAR, SAYANTAN - Virginia Tech|
|BUROW, M - Texas A&M Agrilife|
|WANG, NING - Oklahoma State University|
|WHITE, MELANIE - US Department Of Agriculture (USDA)|
|CHAGOYA, JENNIFER - Texas A&M Agrilife|
|SUNG, CHENG-JUNG - Texas Tech University|
Submitted to: American Peanut Research and Education Society Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 4/28/2021
Publication Date: N/A
Interpretive Summary: Germplasm with resistances to biotic and abiotic stressors need to be identified before commercial cultivars can be improved with these traits. However, the screening procedures for evaluating resistances to biotic and abiotic stressors are slow and labor intensive. Therefore, alternative technologies for evaluating germplasm were investigated using the U.S. peanut minicore collection in 2017 and 2018. Plants were evaluated at multiple points during the growing season using drone-collected images, as well as ground-based traditional measurements. Some of the drone-collected data were well-correlated with traditional measurements, indicating that the more efficient drone data collection may be used instead of the laborious traditional measurements. These results may help the peanut industry develop cultivars with disease and drought resistances more efficiently.
Technical Abstract: Identification of sources of resistance to biotic and abiotic stress is key for the development of improved cultivars, but direct phenotyping is slow. Vegetation Indices (VIs) derived from aerially-collected canopy reflectance in the red, green, and blue (RGB) and near infra-red (NIR) spectra enables indirect phenotyping. Accessions of the US mini-core peanut (Arachis hypogaea L.) germplasm collection were grown in RCBD replicated trials in 2017 and 2018 at the Tidewater Agricultural Research and Extension Center, in Suffolk, VA. Phenotyping included stand count, plant height, lateral branch growth, normalized difference vegetation index (NDVI), canopy temperature depression (CTD), wilting, thrips damage, tomato spotted wilt virus (TSWV, caused by tomato spotted wilt virus, genus Tospovirus, family Bunyaviridae); southern stem rot (SSR, caused by Athelia rolfsii = Sclerotium rolfsii); Sclerotinia blight (caused by Sclerotinia minor Jagger); Cylindrocladium black rot (CBR, caused by Calonectria ilicicola = Cylindrocladium parasiticum); post digging in-shell sprouting; and yield. These characteristics were evaluated at 4, 5, 6, 7, 9, 10, 11, 12, 14, and 16 weeks after planting (WAP). A total of 48 VIs including reflectance in red, blue, green, and NIR, RGB color space indices and combinations of them, taken by an octocopter drone at the same time as the ground measurements, were correlated with the physiologic and agronomic characteristics. Correlation coefficients up to 0.8 were identified for several VIs, indicating their suitability for indirect phenotyping. Broad-sense heritability (H2) was further calculated to assess the suitability of particular VIs to enable genetic gains. For example, the normalized difference CIELab (NDLab) and CIELuv (NDLuv) indices were significantly correlated with yield within all botanical types in the mini-core collection in 2017, i.e. N=104; r=0.43 to 0.57 (p=0.001). But while H2 for yield was 0.14, H2 for the NDLab and NDLuv evaluated during pod development ranged from 0.43 to 0.54, showing that the vegetation indices could be used successfully as surrogates for the physiological and agronomic trait section in peanut.