Date of Graduation
5-2020
Document Type
Thesis
Degree Name
Master of Science in Crop, Soil & Environmental Sciences (MS)
Degree Level
Graduate
Department
Crop, Soil & Environmental Sciences
Advisor/Mentor
Mason, Richard E.
Committee Member
Mozzoni, Leandro A.
Second Committee Member
Purcell, Larry C.
Third Committee Member
Wood, Lisa S.
Fourth Committee Member
Rupe, John C.
Keywords
Breeding Nurseries; Multispectral Sensors; Plot Size; Remote Sensing; Unmanned Aircrafts; Vegetative Indices
Abstract
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
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
Citation
Murry, J. T. (2020). An Evaluation of Unmanned Aircraft Systems' Ability to Assess Stripe Rust in Large Wheat Breeding Nursies. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/3564
Included in
Agronomy and Crop Sciences Commons, Plant Breeding and Genetics Commons, Plant Pathology Commons, Remote Sensing Commons
