Changes in reflectance of rice seedlings during planthopper feeding as detected by digital camera: Potential applications for high-throughput phenotyping.

Finbarr G Horgan,Eduardo Crisol Martínez,Artzai Jauregui,Ainara Peñalver Cruz,Carmencita C Bernal

doi:10.1371/journal.pone.0238173

Finbarr G Horgan, Eduardo Crisol Martínez + Show 3 more

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https://doi.org/10.1371/journal.pone.0238173

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Abstract

Damage to grasses and cereals by phloem-feeding herbivores is manifest as nutrient and chlorophyll loss, desiccation, and a gradual decline in host vigour. Chlorophyll loss in particular leads to a succession of colour changes before eventual host death. Depending on the attacking herbivore species, colour changes can be difficult to detect with the human eye. This study used digital images to examine colour changes of rice seedlings during feeding by the brown planthopper, Nilaparvata lugens (Stål) and whitebacked planthopper, Sogatella furcifera (Horváth). Values for red (580 nm), green (540 nm) and blue (550 nm) reflectance for 39 rice varieties during seedling seed-box tests were derived from images captured with a digital camera. Red and blue reflectance gradually increased as herbivore damage progressed until final plant death. Red reflectance was greater from plants attacked by the brown planthopper than plants attacked by the whitebacked planthopper, which had proportionately more green and blue reflectance, indicating distinct impacts by the two planthoppers on their hosts. Analysis of digital images was used to discriminate variety responses to the two planthoppers. Ordination methods based on red-green-blue reflectance and vegetation indices such as the Green Leaf Index (GLI) that included blue reflectance were more successful than two-colour indices or indices based on hue, saturation and brightness in discriminating between damage responses among varieties. We make recommendations to advance seed-box screening methods for cereal resistance to phloem feeders and demonstrate how images from digital cameras can be used to improve the quality of data captured during high-throughput phenotyping.

Highlights

Crop breeding relies on the identification of useful traits during phenotyping studies and the eventual elimination from breeding programs of plants with relatively undesirable traits [1,2]
Based on our results we propose a model for phenotyping using digital images together with other, available technologies, to improve the information gathered from bulk tests such as the Seedling Seed-box Test (SSST) or Modified Seedling Seed-box Test (MSST)
We investigated a range of indices (Table 1) that have been developed to describe the colour of vegetation based on mean red-green-blue reflectance or hue-saturation-brightness values

Summary

Introduction

Crop breeding relies on the identification of useful traits during phenotyping studies and the eventual elimination from breeding programs of plants with relatively undesirable traits [1,2]. Host plant resistance is a valuable trait to reduce the damage and yield losses from insects and diseases to crops [3,4,5]. Breeding for crop resistance to herbivores has relied heavily on laboratory-based phenotyping to eliminate susceptible plants, to identify donor varieties, and to test breeding lines at successive stages of varietal development [3]. Host resistance and tolerance to herbivore and disease damage are often complex traits governed by several genes. During exploratory screening for resistance to insect pests in rice, normally less than 1% of plant materials has some resistance, indicating that resistance genes are frequently rare [6,7,8]. Horgan et al [11] have indicated that attention to routine screening increased varietal resistance against the green leafhopper, Nephotettix virescens, in the Philippines, despite an apparent lack of deliberate introgression with leafhopper resistant donor varieties during varietal development

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