{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/20995"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/20995","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Detection of stress cracks in corn kernels using machine vision","abstract":"Maintaining high quality of corn is very important to both corn producers and buyers. The detection of stress cracks remains one of the most important tasks in corn quality inspection. Such an index of quality would be helpful in assessing not only the end-use values of the corn but also the drying method used and the appropriateness of subsequent handling procedures.","abstract_html":"Maintaining high quality of corn is very important to both corn producers and buyers. The detection of stress cracks remains one of the most important tasks in corn quality inspection. Such an index of quality would be helpful in assessing not only the end-use values of the corn but also the drying method used and the appropriateness of subsequent handling procedures.","abstract_has_math":false,"creators":["Kim, Chul-Soo"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Agricultural Engineering","degree_department":null,"school":null,"contributors":["Reid, John F."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-05-07T12:55:15Z","date_published":"2011-05-07T12:55:15Z","updated_at":"2026-07-22T22:25:17Z","subjects":["Engineering, Agricultural","Engineering, Electronics and Electrical"],"languages":["eng"],"rights":["Copyright 1991 Kim, Chulsoo"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["AAI9124440","(UMI)AAI9124440"],"render_values":[{"text":"AAI9124440","href":null,"code":true},{"text":"(UMI)AAI9124440","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/20995","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Reid, John F."]},{"key":"dc:creator","label":"Author","values":["Kim, Chul-Soo"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2011-05-07T12:55:15Z","10000-01-01","1991"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Agricultural Engineering"]},{"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":["Engineering, Agricultural","Engineering, Electronics and Electrical"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 1991 Kim, Chulsoo"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["AAI9124440","(UMI)AAI9124440","http://hdl.handle.net/2142/20995"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Maintaining high quality of corn is very important to both corn producers and buyers. The detection of stress cracks remains one of the most important tasks in corn quality inspection. Such an index of quality would be helpful in assessing not only the end-use values of the corn but also the drying method used and the appropriateness of subsequent handling procedures.","For automatic detection of corn stress cracks, a machine vision system was developed, which simulates the processes that the human visual system uses to perceive the stress cracks from the corn kernel in the conventional candling method.","The automatic stress crack detection system consisted of four consecutive stages and was configured in various ways by selecting different image processing algorithms in each stage. Several edge detection algorithms suitable for inclusion in the automatic stress crack detection system were developed and analytically evaluated. From analytical evaluation, it was found that edge detection algorithms had different characteristics in their responses and that proper threshold values should be assigned to each algorithm. The proper threshold values were selected by a statistical design procedure.","A set of performance criteria also was developed to evaluate the automatic stress crack detection system and used to compare the performance of different configuration on several varieties of corn samples against human inspectors. Evaluation results showed that the system configured with the circular band operator, the Duda road operator, and the Hough transform, performed best; with success rates of 78.2% and failure rates of 8.2%. The performance measures of the system with this configuration were superior to that of human inspectors. When the system was used to distinguish cracked-kernels from sound kernels, its accuracy was higher than 90%.","Made available in DSpace on 2011-05-07T12:55:15Z (GMT). No. of bitstreams: 2 license.txt: 4922 bytes, checksum: 910b249b4beec47e7ab768910c8f966f (MD5) 9124440.pdf: 4001050 bytes, checksum: 21a55bcd27cf9782cc293c158bbe0e17 (MD5) Previous issue date: 1991","Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding (hding2@illinois.edu) on 2011-05-07T14:47:46Z Item is restricted indefinitely.","Restriction data tranferred 2014-07-01T11:21:35-05:00 Original Data Group with Access UIUC Users [automated] Release Date: none Reason: ETDs are only available to UIUC Users without author permission","ETDs are only available to UIUC Users without author permission","U of I Only"]},{"key":"dc:title","label":"Title","values":["Detection of stress cracks in corn kernels using machine vision"]}]}],"canonical_facts":{"dc:contributor":["Reid, John F."],"dc:creator":["Kim, Chul-Soo"],"dc:date":["2011-05-07T12:55:15Z","10000-01-01","1991"],"dc:description":["Maintaining high quality of corn is very important to both corn producers and buyers. The detection of stress cracks remains one of the most important tasks in corn quality inspection. Such an index of quality would be helpful in assessing not only the end-use values of the corn but also the drying method used and the appropriateness of subsequent handling procedures.","For automatic detection of corn stress cracks, a machine vision system was developed, which simulates the processes that the human visual system uses to perceive the stress cracks from the corn kernel in the conventional candling method.","The automatic stress crack detection system consisted of four consecutive stages and was configured in various ways by selecting different image processing algorithms in each stage. Several edge detection algorithms suitable for inclusion in the automatic stress crack detection system were developed and analytically evaluated. From analytical evaluation, it was found that edge detection algorithms had different characteristics in their responses and that proper threshold values should be assigned to each algorithm. The proper threshold values were selected by a statistical design procedure.","A set of performance criteria also was developed to evaluate the automatic stress crack detection system and used to compare the performance of different configuration on several varieties of corn samples against human inspectors. Evaluation results showed that the system configured with the circular band operator, the Duda road operator, and the Hough transform, performed best; with success rates of 78.2% and failure rates of 8.2%. The performance measures of the system with this configuration were superior to that of human inspectors. When the system was used to distinguish cracked-kernels from sound kernels, its accuracy was higher than 90%.","Made available in DSpace on 2011-05-07T12:55:15Z (GMT). No. of bitstreams: 2 license.txt: 4922 bytes, checksum: 910b249b4beec47e7ab768910c8f966f (MD5) 9124440.pdf: 4001050 bytes, checksum: 21a55bcd27cf9782cc293c158bbe0e17 (MD5) Previous issue date: 1991","Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding (hding2@illinois.edu) on 2011-05-07T14:47:46Z Item is restricted indefinitely.","Restriction data tranferred 2014-07-01T11:21:35-05:00 Original Data Group with Access UIUC Users [automated] Release Date: none Reason: ETDs are only available to UIUC Users without author permission","ETDs are only available to UIUC Users without author permission","U of I Only"],"dc:identifier":["AAI9124440","(UMI)AAI9124440","http://hdl.handle.net/2142/20995"],"dc:language":["eng"],"dc:rights":["Copyright 1991 Kim, Chulsoo"],"dc:subject":["Engineering, Agricultural","Engineering, Electronics and Electrical"],"dc:title":["Detection of stress cracks in corn kernels using machine vision"],"dc:type":["text"],"thesis:degree_discipline":["Agricultural Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:17Z"}