Tools and Insights for Sustainable Management of Plant Pests and Pathogens
Metadata Field | Value | Language |
---|---|---|
dc.contributor.advisor | Hardy, Nate B. | |
dc.contributor.author | Bevers, Noah | |
dc.date.accessioned | 2022-04-25T12:50:23Z | |
dc.date.available | 2022-04-25T12:50:23Z | |
dc.date.issued | 2022-04-25 | |
dc.identifier.uri | https://etd.auburn.edu//handle/10415/8142 | |
dc.description.abstract | 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. | en_US |
dc.rights | EMBARGO_NOT_AUBURN | en_US |
dc.subject | Entomology and Plant Pathology | en_US |
dc.title | Tools and Insights for Sustainable Management of Plant Pests and Pathogens | en_US |
dc.type | Master's Thesis | en_US |
dc.embargo.length | MONTHS_WITHHELD:12 | en_US |
dc.embargo.status | EMBARGOED | en_US |
dc.embargo.enddate | 2023-04-25 | en_US |
dc.contributor.committee | Sikora, Edward J. | |
dc.contributor.committee | Jacobson, Alana L. | |
dc.creator.orcid | 0000-0002-0683-3658 | en_US |