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

dc.contributor.authorIvan Chapu
dc.contributor.authorDavid Kalule Okello
dc.contributor.authorRobert C. Ongom Okello
dc.contributor.authorThomas Lapaka Odong
dc.contributor.authorSayantan Sarka
dc.contributor.authorMaria Balota
dc.date.accessioned2025-02-24T05:33:58Z
dc.date.available2025-02-24T05:33:58Z
dc.date.issued2022-06-14
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 (H2 > 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.
dc.description.sponsorshipThis 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).
dc.identifier.citationChapu I, Okello DK, Okello RCO, Odong TL, Sarkar S and Balota M (2022) Exploration of Alternative Approaches to Phenotyping of Late Leaf Spot and Groundnut Rosette Virus Disease for Groundnut Breeding. Front. Plant Sci. 13:912332. doi: 10.3389/fpls.2022.912332
dc.identifier.issn10.3389/fpls.2022.912332
dc.identifier.urihttp://104.225.218.216/handle/123456789/162
dc.language.isoen
dc.publisherFrontiers in Plant Science
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United Statesen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/
dc.subjectgroundnut rosette disease
dc.subjectlate leaf spot (LLS)
dc.subjectphenotyping
dc.subjectNDVI
dc.subjectRGB indices
dc.subjectlogistic models
dc.titleExploration of Alternative Approaches to Phenotyping of Late Leaf Spot and Groundnut Rosette Virus Disease for Groundnut Breeding
dc.typeArticle

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