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The quantification of southern corn leaf blight disease using deep UV fluorescence spectroscopy and autoencoder anomaly detection techniques.
Banah, Hashem; Balint-Kurti, Peter J; Houdinet, Gabriella; Hawkes, Christine V; Kudenov, Michael.
Afiliación
  • Banah H; Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC, United States of America.
  • Balint-Kurti PJ; NC Plant Science Initiative, North Carolina State University, Raleigh, NC, United States of America.
  • Houdinet G; USDA-ARS, Plant Science Research Unit and Entomology and Plant Pathology Department, North Carolina State University, Raleigh, NC, United States of America.
  • Hawkes CV; NC Plant Science Initiative, North Carolina State University, Raleigh, NC, United States of America.
  • Kudenov M; Department of Plant and Microbial Biology, North Carolina State University, Raleigh, NC, United States of America.
PLoS One ; 19(5): e0301779, 2024.
Article en En | MEDLINE | ID: mdl-38748689
ABSTRACT
Southern leaf blight (SLB) is a foliar disease caused by the fungus Cochliobolus heterostrophus infecting maize plants in humid, warm weather conditions. SLB causes production losses to corn producers in different regions of the world such as Latin America, Europe, India, and Africa. In this paper, we demonstrate a non-destructive method to quantify the signs of fungal infection in SLB-infected corn plants using a deep UV (DUV) fluorescence spectrometer, with a 248.6 nm excitation wavelength, to acquire the emission spectra of healthy and SLB-infected corn leaves. Fluorescence emission spectra of healthy and diseased leaves were used to train an Autoencoder (AE) anomaly detection algorithm-an unsupervised machine learning model-to quantify the phenotype associated with SLB-infected leaves. For all samples, the signature of corn leaves consisted of two prominent peaks around 450 nm and 325 nm. However, SLB-infected leaves showed a higher response at 325 nm compared to healthy leaves, which was correlated to the presence of C. heterostrophus based on disease severity ratings from Visual Scores (VS). Specifically, we observed a linear inverse relationship between the AE error and the VS (R2 = 0.94 and RMSE = 0.935). With improved hardware, this method may enable improved quantification of SLB infection versus visual scoring based on e.g., fungal spore concentration per unit area and spatial localization.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Enfermedades de las Plantas / Ascomicetos / Hojas de la Planta / Zea mays Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Enfermedades de las Plantas / Ascomicetos / Hojas de la Planta / Zea mays Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos