An Evaluation of Unmanned Aircraft Systems' Ability to Assess Stripe Rust in Large Wheat Breeding Nursies

<p>Stripe Rust (Puccinia striiformis f. sp. tritici) is a foliar disease that significantly impacts global wheat production, and resistant cultivars provide the most efficient method of control. High-throughput phenotyping using unmanned aircraft systems (UAS) offers a potentially more efficient method for field-based phenotyping compared to visual assessment. Here we tested the ability of remote sensing to predict stripe rust severity in a diverse population of 594 soft red winter wheat lines, planted in single-rows, and evaluated them by visually rating stripe rust intensity and remotely using the dark green color index (DGCI), normalized difference vegetation index (NDVI) and blue NDVI. Significant relationships (p</p> <p>In a second study, the effect of plot size (single-row, two-row and four-row) on relationship between visual and remote sensing data (DGCI and NDVI) was explored. We evaluated a panel of 13 genotypes preselected to range from 0 to 100% severity, planted in three plot sizes across two measurement days. Significant (p</p>

Name thesis:degree_name

Master of Science in Crop, Soil & Environmental Sciences (MS)

Level thesis:degree_level

Thesis

Year dc:date.available

2020

Author dc:creator

• Murry, Jamison T.

Advisor dc:contributor.advisor

• Mason, Richard E.

Contributors dc:contributor

• Mozzoni, Leandro A.
• Purcell, Larry C.

• Breeding Nurseries
• Multispectral Sensors
• Plot Size
• Remote Sensing
• Unmanned Aircrafts
• Vegetative Indices
• Agronomy and Crop Sciences
• Plant Breeding and Genetics
• Plant Pathology

Repository record dc:identifier

https://scholarworks.uark.edu/etd/3564

OAI identifier oai:identifier

oai:scholarworks.uark.edu:etd-5114