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

• Main Authors: Finbarr G Horgan, Artzai Jauregui, Ainara Peñalver Cruz, Eduardo Crisol Martínez, Carmencita C Bernal
• Format: Article
• Language: English
• Published: Public Library of Science (PLoS) 2020-01-01
• Series: PLoS ONE
• Online Access: https://doi.org/10.1371/journal.pone.0238173

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.