EVALUATING UAV CAPTURED RGB AND MULTISPECTRAL IMAGERY AS A PROXY FOR VISUAL RATING OF LEAF SPOT IN CULTIVATED PEANUT

Abstract

Leaf spot is a devastating disease in cultivated peanut that can lead to significant yield losses without chemical controls. Multiple disease symptoms, two causal organisms, inconsistent testing environments, and genotype by environment interactions are all components which make breeding for leaf spot resistant peanuts challenging. To better understand this disease, and make gains in breeding for disease resistance, an accurate and effective phenotyping strategy must be implemented. In this work, data derived from leaf scans and UAV-captured RGB and multispectral imagery were evaluated as a replacement for the subjective visual rating scale used at present. Standard operating procedures are detailed for all digital methods evaluated in this paper, and all digital phenotypes are fully characterized with descriptive statistics. Feature importance and post hoc proof of concept studies are conducted to further evaluate the new digital methods. Ultimately, ‘Visible Atmospherically Resistant Index’ is selected as the most appropriate proxy for immediate use by researchers and plant breeders in the peanut community.

Code and data availability

The supplied blocks contain only the title page and abstract. The only hosted file is the manuscript document itself (LSP_Journal Template 091324.docx), which is the article, not a paper-specific dataset, image collection, code deposit, or model. No public phenotype data, UAV imagery, analysis code, or trained model is

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