Date of Graduation


Document Type


Degree Name

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

Degree Level



Crop, Soil & Environmental Sciences


Richard E. Mason

Committee Member

Leandro Mozzoni

Second Committee Member

Larry Purcell

Third Committee Member

Lisa Wood

Fourth Committee Member

John Rupe


Breeding Nurseries, Multispectral Sensors, Plot Size, Remote Sensing, Unmanned Aircrafts, Vegetative Indices


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