Graduate Studies

 

First Advisor

Jinliang Yang

Degree Name

Doctor of Philosophy (Ph.D.)

Committee Members

James Schnable, Jeffrey Mower, Yufeng Ge

Department

Agronomy and Horticulture

Date of this Version

7-2024

Document Type

Dissertation

Citation

A dissertation presented to the faculty of the Graduate College of the University of Nebraska in partial fulfillment of requirements for the degree of Doctor of Philosophy

Major: Agronomy and Horticulture

Under the supervision of Professor Jinliang Yang

Lincoln, Nebraska, July 2024

Comments

Copyright 2024, Eric T. Rodene. Used by permission

Abstract

Modern breeding programs rely heavily on efficiently screening large numbers of genotypes for agronomic traits, such as disease resistance, drought tolerance, and yield. Identifying the genetic loci or genes associated with these traits using GWAS or functional analyses will benefit future plant breeding efforts seeking to incorporate these traits into new crop varieties, whether through conventional breeding or gene editing techniques. Unmanned aerial vehicle (UAV)-based image data has been increasingly used for this task, as it allows entire test plots to be quickly and cost-effectively phenotyped. In my research, I have developed methods to improve the accuracy of machine learning techniques for automated tassel counting by removing non-tassel pixels, as well as identifying vegetation indices correlated with traits like leaf area, nitrogen (N) content, and kernel weight and then use GWAS to identify genes associated with these UAV-extracted traits. Recently, by leveraging a maize breeding population grown under high N and low N field conditions, I used multispectral and visible color (RGB) image data to develop N-responsive vegetation indices (VI) that correlate with plant growth throughout the season. By incorporating these N-responsive VIs into the genomic selection pipeline, we demonstrated improved prediction accuracies for end-of-season yield-related traits. This research highlights the potential of UAV-enabled plant breeding to facilitate the development of N-resilient maize.

Advisor: Jinliang Yang

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