US Department of Agriculture is an equal opportunity provider and employer. C O N F L I C T O F I N T E R E S T S TAT E M E N T The authors declare no conflicts of interest. DATA AVA I L A B I L I T Y S TAT E M E N T The datasets generated during and/or analyzed during the cur- rent study are available in the Dryad repository: https://doi.org/10.5061/dryad.rn8pk0pnm.O RC I D RyanAndres https://orcid.org/0000-0001-8635-4077 JeffreyDunne https://orcid.org/0000-0003-0544-9889 R E F E R E N C E S Anco, D. J., Thomas, J. S., Jordan, D. L., Shew, B. B., Monfort, W. S., Mehl, H. L., Small, I. M., Wright, D. L., Tillman, B. L., Dufault, N. S., Hagan, A. K., & Campbell, H. L. (2020). Peanut yield los
Open resource ↗Dryad · 10.5061/dryad.rn8pk0pnm.O · pdf-raw-page:15 lines:1-82Paper record
Evaluating UAV captured RGB and multispectral imagery as a proxy for visual rating of leaf spot in cultivated peanut
The Plant Phenome Journal · 21 May 2025 · 10.1002/ppj2.70019
Abstract
Abstract Leaf spot is a devastating disease in cultivated peanut ( Arachis hypogaea L.) 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 that make breeding for leaf spot‐resistant peanuts challenging. To better understand this disease, and make gains in breeding for disease resistance, an accurate and objective phenotyping strategy must be implemented. In this work, data derived from leaf scans, unoccupied aerial vehicle‐captured red, green, blue 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” was selected as the most appropriate proxy for visual ratings and should be deployed by researchers and plant breeders in the peanut community for the objective evaluation of leaf spot resistance.
Code and data availability
The paper deposits its phenotyping datasets (visual ratings, leaf scans, UAV RGB/multispectral imagery) in Dryad and hosts analysis scripts and supporting information in a public GitHub repository, both explicitly linked by the authors.
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