Tools and Insights for Sustainable Management of Plant Pests and Pathogens
Type of DegreeMaster's Thesis
Entomology and Plant Pathology
MetadataShow full item record
Minimizing crop yield losses caused by plant pathogens is one way of increasing agricultural productivity. To that end, I developed an automated classifier of digital images of soybean diseases to assist with early and accurate detection of pathogens. This application, based on a convolutional neural network, distinguishes between eight soybean disease/deficiency classes with an overall accuracy of 96.75%, which may help minimize pesticide usage and improve overall productivity. I also performed a quantitative integration of the existing research characterizing the relationship between virulence and within-host pathogen accumulation. By doing so, I aimed to help increase our ability to foresee and manage the evolution of highly-virulent pathogen genotypes. In these two ways, I pursued my overarching goal of developing tools and gaining insights for the sustainable management of plant pathogens.