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

  • Journal: Frontiers in Plant Science 13:912332
  • Authors: Ivan Chapu, David Kalule Okello, Robert C. Ongom Okello, Thomas Lapaka Odong, Sayantan Sarkar and Maria Balota
  • Publication Type: Article
  • Date:
  • Keywords: roundnut rosette disease, late leaf spot (LLS), phenotyping, NDVI, RGB indices, logistic models


Late 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.

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