Severity Grading of Southern Leaf Blight on Corn Leaves

In agriculture, continuous efforts are made to efficiently identify indicators of abiotic and biotic stresses affecting plants. Plant stresses, such as diseases, that induce leaf symptoms can exhibit distinctive features that could aid in identifying and quantifying these symptoms. Machine learning and computer vision approaches have been applied to agricultural applications to provide objective methods for plant stress phenotyping by leveraging variations in the size, shape, and reflectance spectra of disease symptoms. This talk will cover a multi-stage deep learning pipeline developed for early-stress detection and quantification of Southern corn leaf blight damage on maize leaves in visible-light (RGB) images. The goal is to develop an analytical disease severity grading tool designed to inform on selecting resistant genotypes and to overcome the limitations of visual estimation by human raters by detecting subtle changes in plant health that might be missed by the human eye, enabling quicker responses to disease infections, and reducing the need for manual monitoring and field scouting, saving time and labor, especially in large agricultural operations.

Chanae Ottley

PhD Student, NC State University on April 24, 2026 at 10:15 AM in EB2 1231
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Chanae Ottley received her B.S. degree in Mathematics from Florida A&M University. Her interest in the applications of mathematics led her to pursue a Ph.D. in Electrical and Computer Engineering at North Carolina State University. She was a recipient of the Louis Stokes Alliance for Minority Participation (LSAMP) – Bridge to Doctorate (BD) Fellowship Program, the NIH/NCSU Molecular Biotechnology Training Program (MBTP), and the GEM Fellowship Program with an Oak Ridge National Laboratory sponsorship. She has completed coursework towards completing a Biotechnology minor and the Agriculture Data Science graduate certificate. Her research focuses on using computational methodologies to understand changes in crop phenotypes resulting from plant-pathogen interactions.

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