The quantification of Southern Corn Leaf Blight Disease using deep UV fluorescence spectroscopy and autoencoder anomaly detection techniques

Authors: BANAH, HASHEM - North Carolina State University; Balint-Kurti, Peter; HOUDINET, GABRIELLA - North Carolina State University; HAWKES, CHRISTINE - North Carolina State University; KUDENOV, MICHAEL - North Carolina State University

Submitted to: PLOS ONE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/15/2024
Publication Date: 5/15/2024
Citation: Banah, H., Balint Kurti, P.J., Houdinet, G., Hawkes, C., Kudenov, M. 2024. The quantification of Southern Corn Leaf Blight Disease using deep UV fluorescence spectroscopy and autoencoder anomaly detection techniques. PLOS ONE. 19(5):e0301779. https://doi.org/10.1371/journal.pone.0301779.
DOI: https://doi.org/10.1371/journal.pone.0301779

Interpretive Summary: Plant disease is an important impediment to food production. Effective prevention often relies upon the early and accurate detection of disease. Here we demonstrate the use of a handheld spectrophotometer to identify emission wavelengths associated with the presence of a specific pathogen, the fungus Cochliobolus heterostrophus, causal agent of southern corn leaf blight disease, in corn leaves in the field. This type of technology might in the future be used by remote sensors to identify incipient disease in the field.

Technical 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 SLBinfected 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.