{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129666"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129666","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"The sorting hat: An automated activity index based on finishing pig behavior","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","abstract_has_math":false,"creators":["Felton, Mekali"],"institution":"University of Illinois Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Technical Systems Management","degree_department":null,"school":null,"contributors":["Green-Miller, Angela","Condotta, Isabella","Malvandi, Amir"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-04-03","date_published":"2025-04-03","updated_at":"2026-07-22T22:25:05Z","subjects":["machine learning","behavior analysis","finishing pigs","yolov8","mobilenet"],"languages":["en","eng"],"rights":["Chapter 4 contents are proprietary and are being submitted to the office of technology management"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129666","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Green-Miller, Angela","Condotta, Isabella","Malvandi, Amir"]},{"key":"dc:creator","label":"Author","values":["Felton, Mekali"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-04-03","2025-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Technical Systems Management"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["machine learning","behavior analysis","finishing pigs","yolov8","mobilenet"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Chapter 4 contents are proprietary and are being submitted to the office of technology management"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129666"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","The student, Mekali Felton, accepted the attached license on 2025-03-03 at 16:03.","The student, Mekali Felton, submitted this Thesis for approval on 2025-03-03 at 16:13.","This Thesis was approved for publication on 2025-04-03 at 14:14.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21657 on 2025-10-19 at 19:52:27","A new tool is needed to support animal behavior observation on pig production farms. New guidelines for pig production include behavior observations within daily protocols, which is an improvement in utilizing the pig as an indicator of its status. Human observation is limited to sporadic viewing of many animals, and relevant trends are likely to be missed. An automated tool to evaluate behavior would alleviate some of the human workload for behavior observations and provide a more complete dataset from which to derive trends and ultimately valuable decisions. Existing tools and technologies have numerous limitations for practical deployment on commercial farms, including computing resources and processing time for real-time analysis. There is an opportunity to explore simplified computer vision techniques for observing pigs that yield sufficient information to provide valuable insights. Five computer vision techniques were implemented using three categories to evaluate the application in a simple behavior analysis. Each category represents a collection of behaviors representing a similar animal status for a group of pigs together in a pen. The automated determination of category was accomplished using machine learning and mathematical techniques with frames extracted from a set of labeled images from a commercial finishing pig farm. The image labels divide the image set into three activity categories based on behavior (categories 1-3), based on written definitions, assigned by a trained reviewer. Frames were manually separated and labeled, according to the input format for each model technique. Computer vision models with YOLOv8 and TensorFlow had activity level predictions of 86% and 79% overall accuracy, respectively, with the models having more mislabels for Category 2 and 3. The mathematical technique using Mean Square Error (MSE) correctly distinguished between activity categories 1 and 3 (P=0.000297), and both Root Mean Square Error (RMSE) and Structural Similarity Index Measure (SSIM) did not result in distinctly different categories."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["The sorting hat: An automated activity index based on finishing pig behavior"]}]}],"canonical_facts":{"dc:contributor":["Green-Miller, Angela","Condotta, Isabella","Malvandi, Amir"],"dc:creator":["Felton, Mekali"],"dc:date":["2025-04-03","2025-05"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","The student, Mekali Felton, accepted the attached license on 2025-03-03 at 16:03.","The student, Mekali Felton, submitted this Thesis for approval on 2025-03-03 at 16:13.","This Thesis was approved for publication on 2025-04-03 at 14:14.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21657 on 2025-10-19 at 19:52:27","A new tool is needed to support animal behavior observation on pig production farms. New guidelines for pig production include behavior observations within daily protocols, which is an improvement in utilizing the pig as an indicator of its status. Human observation is limited to sporadic viewing of many animals, and relevant trends are likely to be missed. An automated tool to evaluate behavior would alleviate some of the human workload for behavior observations and provide a more complete dataset from which to derive trends and ultimately valuable decisions. Existing tools and technologies have numerous limitations for practical deployment on commercial farms, including computing resources and processing time for real-time analysis. There is an opportunity to explore simplified computer vision techniques for observing pigs that yield sufficient information to provide valuable insights. Five computer vision techniques were implemented using three categories to evaluate the application in a simple behavior analysis. Each category represents a collection of behaviors representing a similar animal status for a group of pigs together in a pen. The automated determination of category was accomplished using machine learning and mathematical techniques with frames extracted from a set of labeled images from a commercial finishing pig farm. The image labels divide the image set into three activity categories based on behavior (categories 1-3), based on written definitions, assigned by a trained reviewer. Frames were manually separated and labeled, according to the input format for each model technique. Computer vision models with YOLOv8 and TensorFlow had activity level predictions of 86% and 79% overall accuracy, respectively, with the models having more mislabels for Category 2 and 3. The mathematical technique using Mean Square Error (MSE) correctly distinguished between activity categories 1 and 3 (P=0.000297), and both Root Mean Square Error (RMSE) and Structural Similarity Index Measure (SSIM) did not result in distinctly different categories."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129666"],"dc:language":["en","eng"],"dc:rights":["Chapter 4 contents are proprietary and are being submitted to the office of technology management"],"dc:subject":["machine learning","behavior analysis","finishing pigs","yolov8","mobilenet"],"dc:title":["The sorting hat: An automated activity index based on finishing pig behavior"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Technical Systems Management"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:05Z"}