{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/83121"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/83121","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Evaluation of Sensing and Machine Vision Techniques in Stress Detection and Quality Evaluation of Turfgrass Species","abstract":"The utility of different image sensing and non-image sensing techniques was also studied in objectively evaluating turfgrass quality parameters like, color, density and texture from National Turfgrass Evaluation Program trials. Image sensing took the greatest amount of time, while non-image sensing techniques were the fastest among the evaluated methods. All methods showed significant differences in cultivars for color in different trials. The quantified hue values from the multispectral camera were the least correlated with other evaluation techniques. Both chlorophyll meter and turf color meter showed potential in quantifying turfgrass color with greater consistency. However, the narrow separation obtained using turf color meter may not allow cultivar differentiation from species with less genetic color variation. Texture evaluation of turfgrasses was done after developing and implementing the run length encoding algorithm (RLE) on simulated turf built using twist ties in both planar and turf-type arrangements. Significant relationship was observed between manual measurements of twist ties and RLE-derived values. The algorithm implementation on true turfgrass images collected under greenhouse and field conditions from Kentucky bluegrass showed significantly positive relationship between RLE values and visual evaluation ratings. The possibility of collecting and analyzing images from multiple plots for color quantification was also evaluated successfully using an elevated platform from both Kentucky bluegrass and fairway bentgrass trials.","abstract_html":"The utility of different image sensing and non-image sensing techniques was also studied in objectively evaluating turfgrass quality parameters like, color, density and texture from National Turfgrass Evaluation Program trials. Image sensing took the greatest amount of time, while non-image sensing techniques were the fastest among the evaluated methods. All methods showed significant differences in cultivars for color in different trials. The quantified hue values from the multispectral camera were the least correlated with other evaluation techniques. Both chlorophyll meter and turf color meter showed potential in quantifying turfgrass color with greater consistency. However, the narrow separation obtained using turf color meter may not allow cultivar differentiation from species with less genetic color variation. Texture evaluation of turfgrasses was done after developing and implementing the run length encoding algorithm (RLE) on simulated turf built using twist ties in both planar and turf-type arrangements. Significant relationship was observed between manual measurements of twist ties and RLE-derived values. The algorithm implementation on true turfgrass images collected under greenhouse and field conditions from Kentucky bluegrass showed significantly positive relationship between RLE values and visual evaluation ratings. The possibility of collecting and analyzing images from multiple plots for color quantification was also evaluated successfully using an elevated platform from both Kentucky bluegrass and fairway bentgrass trials.","abstract_has_math":false,"creators":["Narra, Siddhartha"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Natural Resrouces and Environmental Sciences","degree_department":null,"school":null,"contributors":["Fermanian, Thomas W."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-25T21:00:35Z","date_published":"2015-09-25T21:00:35Z","updated_at":"2026-07-22T22:26:20Z","subjects":["Environmental Sciences"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(MiAaPQ)AAI3290329"],"render_values":[{"text":"(MiAaPQ)AAI3290329","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/83121","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Fermanian, Thomas W."]},{"key":"dc:creator","label":"Author","values":["Narra, Siddhartha"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-25T21:00:35Z","10000-01-01","2007"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Natural Resrouces and Environmental Sciences"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Environmental Sciences"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/83121","(MiAaPQ)AAI3290329"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The utility of different image sensing and non-image sensing techniques was also studied in objectively evaluating turfgrass quality parameters like, color, density and texture from National Turfgrass Evaluation Program trials. Image sensing took the greatest amount of time, while non-image sensing techniques were the fastest among the evaluated methods. All methods showed significant differences in cultivars for color in different trials. The quantified hue values from the multispectral camera were the least correlated with other evaluation techniques. Both chlorophyll meter and turf color meter showed potential in quantifying turfgrass color with greater consistency. However, the narrow separation obtained using turf color meter may not allow cultivar differentiation from species with less genetic color variation. Texture evaluation of turfgrasses was done after developing and implementing the run length encoding algorithm (RLE) on simulated turf built using twist ties in both planar and turf-type arrangements. Significant relationship was observed between manual measurements of twist ties and RLE-derived values. The algorithm implementation on true turfgrass images collected under greenhouse and field conditions from Kentucky bluegrass showed significantly positive relationship between RLE values and visual evaluation ratings. The possibility of collecting and analyzing images from multiple plots for color quantification was also evaluated successfully using an elevated platform from both Kentucky bluegrass and fairway bentgrass trials.","Made available in DSpace on 2015-09-25T21:00:35Z (GMT). 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Image sensing took the greatest amount of time, while non-image sensing techniques were the fastest among the evaluated methods. All methods showed significant differences in cultivars for color in different trials. The quantified hue values from the multispectral camera were the least correlated with other evaluation techniques. Both chlorophyll meter and turf color meter showed potential in quantifying turfgrass color with greater consistency. However, the narrow separation obtained using turf color meter may not allow cultivar differentiation from species with less genetic color variation. Texture evaluation of turfgrasses was done after developing and implementing the run length encoding algorithm (RLE) on simulated turf built using twist ties in both planar and turf-type arrangements. Significant relationship was observed between manual measurements of twist ties and RLE-derived values. The algorithm implementation on true turfgrass images collected under greenhouse and field conditions from Kentucky bluegrass showed significantly positive relationship between RLE values and visual evaluation ratings. The possibility of collecting and analyzing images from multiple plots for color quantification was also evaluated successfully using an elevated platform from both Kentucky bluegrass and fairway bentgrass trials.","Made available in DSpace on 2015-09-25T21:00:35Z (GMT). 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