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

Richard E. Mason

Committee Member

Leandro Mozzoni

Second Committee Member

Larry Purcell

Third Committee Member

Lisa Wood

Fourth Committee Member

John Rupe

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

Available for download on Wednesday, February 24, 2021

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