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
","abstract_html":"<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>","abstract_has_math":false,"creators":["Murry, Jamison T."],"institution":null,"degree_name":"Master of Science in Crop, Soil & Environmental Sciences (MS)","degree_level":"Thesis","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Mozzoni, Leandro A.","Purcell, Larry C."],"advisors":["Mason, Richard E."],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-05-01T07:00:00Z","date_published":"2020-05-01T07:00:00Z","updated_at":"2026-07-24T00:58:14Z","subjects":["Breeding Nurseries","Multispectral Sensors","Plot Size","Remote Sensing","Unmanned Aircrafts","Vegetative Indices","Agronomy and Crop Sciences","Plant Breeding and Genetics","Plant Pathology"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarworks.uark.edu/etd/3564","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Mozzoni, Leandro A.","Purcell, Larry C."]},{"key":"dc:contributor.advisor","label":"Advisor","values":["Mason, Richard E."]},{"key":"dc:creator","label":"Author","values":["Murry, Jamison T."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2021-02-24T08:00:00Z"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Crop, Soil & Environmental Sciences (MS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Breeding Nurseries","Multispectral Sensors","Plot Size","Remote Sensing","Unmanned Aircrafts","Vegetative Indices","Agronomy and Crop Sciences","Plant Breeding and Genetics","Plant Pathology"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarworks.uark.edu/etd/3564"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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
"]},{"key":"dc:title","label":"Title","values":["An Evaluation of Unmanned Aircraft Systems' Ability to Assess Stripe Rust in Large Wheat Breeding Nursies"]}]}],"canonical_facts":{"dc:contributor":["Mozzoni, Leandro A.","Purcell, Larry C."],"dc:contributor.advisor":["Mason, Richard E."],"dc:creator":["Murry, Jamison T."],"dc:date":["2020"],"dc:date.available":["2021-02-24T08:00:00Z"],"dc:description.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
"],"dc:identifier":["https://scholarworks.uark.edu/etd/3564"],"dc:subject":["Breeding Nurseries","Multispectral Sensors","Plot Size","Remote Sensing","Unmanned Aircrafts","Vegetative Indices","Agronomy and Crop Sciences","Plant Breeding and Genetics","Plant Pathology"],"dc:title":["An Evaluation of Unmanned Aircraft Systems' Ability to Assess Stripe Rust in Large Wheat Breeding Nursies"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science in Crop, Soil & Environmental Sciences (MS)"]},"updated_at":"2026-07-24T00:58:14Z"}