Exploration of Alternative Approaches to Phenotyping of Late Leaf Spot and Groundnut Rosette Virus Disease for Groundnut Breeding

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dc.contributor.authorChapu, Ivanen
dc.contributor.authorOkello, David Kaluleen
dc.contributor.authorOkello, Robert C. Ongomen
dc.contributor.authorOdong, Thomas Lapakaen
dc.contributor.authorSarkar, Sayantanen
dc.contributor.authorBalota, Mariaen
dc.date.accessioned2022-11-30T14:45:39Zen
dc.date.available2022-11-30T14:45:39Zen
dc.date.issued2022-06-14en
dc.description.abstractLate leaf spot (LLS), caused by Nothopassalora personata (Berk. & M.A Curt.), and groundnut rosette disease (GRD), [caused by groundnut rosette virus (GRV)], represent the most important biotic constraints to groundnut production in Uganda. Application of visual scores in selection for disease resistance presents a challenge especially when breeding experiments are large because it is resource-intensive, subjective, and error-prone. High-throughput phenotyping (HTP) can alleviate these constraints. The objective of this study is to determine if HTP derived indices can replace visual scores in a groundnut breeding program in Uganda. Fifty genotypes were planted under rain-fed conditions at two locations, Nakabango (GRD hotspot) and NaSARRI (LLS hotspot). Three handheld sensors (RGB camera, GreenSeeker, and Thermal camera) were used to collect HTP data on the dates visual scores were taken. Pearson correlation was made between the indices and visual scores, and logistic models for predicting visual scores were developed. Normalized difference vegetation index (NDVI) (r = -0.89) and red-green-blue (RGB) color space indices CSI (r = 0.76), v* (r = -0.80), and b* (r = -0.75) were highly correlated with LLS visual scores. NDVI (r = -0.72), v* (r = -0.71), b* (r = -0.64), and GA (r = -0.67) were best related to the GRD visual symptoms. Heritability estimates indicated NDVI, green area (GA), greener area (GGA), a*, and hue angle having the highest heritability (H-2 > 0.75). Logistic models developed using these indices were 68% accurate for LLS and 45% accurate for GRD. The accuracy of the models improved to 91 and 84% when the nearest score method was used for LLS and GRD, respectively. Results presented in this study indicated that use of handheld remote sensing tools can improve screening for GRD and LLS resistance, and the best associated indices can be used for indirect selection for resistance and improve genetic gain in groundnut breeding.en
dc.description.notesThis study was made possible by the generous support of the American people through the United States Agency for International Development (USAID) through Cooperative Agreement No. 7200AA 18CA00003 to the University of Georgia as management entity for U.S. Feed the Future Innovation Lab for Peanut (2018-2023).en
dc.description.sponsorshipUnited States Agency for International Development (USAID) [7200AA 18CA00003]en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.3389/fpls.2022.912332en
dc.identifier.issn1664-462Xen
dc.identifier.other912332en
dc.identifier.pmid35774822en
dc.identifier.urihttp://hdl.handle.net/10919/112735en
dc.identifier.volume13en
dc.language.isoenen
dc.publisherFrontiersen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectgroundnut rosette diseaseen
dc.subjectlate leaf spot (LLS)en
dc.subjectphenotypingen
dc.subjectNDVIen
dc.subjectRGB indicesen
dc.subjectlogistic modelsen
dc.titleExploration of Alternative Approaches to Phenotyping of Late Leaf Spot and Groundnut Rosette Virus Disease for Groundnut Breedingen
dc.title.serialFrontiers in Plant Scienceen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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