Ultrastructural Study of Systemic Lesions induced by Peanut Rosette Virus in Peanut Leaves

Favali, M. A.

doi:10.1111/j.1439-0434.1977.tb02841.x

Cited by 1 publication

(1 citation statement)

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“…Canopy temperature increased with an increase in GRD severity although the correlation was weak ( r = 0.42) at beginning of maturity. GRD is associated with damage of the leaf xylem ( Favali, 1977 ) and this could limit the water transport to the leaves hence causing reduced transpirational cooling. The findings in this study support our hypothesis that there are HTP-derived indices that are correlated with visual scores of LLS and GRD symptoms.…”

Section: Discussionmentioning

confidence: 99%

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

Chapu

Okello²,

Okello³

et al. 2022

Front. Plant Sci.

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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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Section: Discussionmentioning

confidence: 99%