{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/122042"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/122042","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Towards an electronic system for estrus detection in swine","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms","abstract_has_math":false,"creators":["Bushman, Jeni L."],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Animal Sciences","degree_department":null,"school":null,"contributors":["Condotta, Isabella","Green-Miller, Angela","Knox, Robert"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-12","date_published":"2023-12","updated_at":"2026-07-22T22:25:00Z","subjects":["Machine Learning","Computer Vision","Estrus Detection","Back-pressure Test"],"languages":["en","eng"],"rights":["Copyright 2023 Jeni Bushman"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/122042","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Condotta, Isabella","Green-Miller, Angela","Knox, Robert"]},{"key":"dc:creator","label":"Author","values":["Bushman, Jeni L."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-12","2023-12-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Animal Sciences"]},{"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 at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Machine Learning","Computer Vision","Estrus Detection","Back-pressure Test"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2023 Jeni Bushman"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/122042"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms","The student, Jeni Bushman, accepted the attached license on 2023-11-30 at 10:25.","The student, Jeni Bushman, submitted this Thesis for approval on 2023-11-30 at 10:34.","This Thesis was approved for publication on 2023-12-05 at 16:26.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20084 on 2024-03-01 at 13:15:18","Because most pigs worldwide are now bred by artificial insemination (AI), accurate estrus detection is essential. There is consensus that display of the lordosis response (“standing”) following physical boar contact or application of the back-pressure test (BPT) is the gold standard for estrus detection in pigs. However, little research has explored its true accuracy and evaluated whether automated measures could improve upon such a manually driven process. On commercial pig breeding farms, estrus detection relies on multiple trained technicians and daily efforts and occurs year-round. This places an undue burden on an already volatile and limited farm labor supply. And while research into automated technology specific to swine estrus detection is evolving, it remains underdeveloped. Although electronic estrus detection (EED) systems have been successfully integrated into the management of dairy and goat herds, use in swine breeding herds has faced challenges. Therefore, the aim of this thesis is to summarize current knowledge of estrus detection procedures in swine and extrapolate how these processes can be improved using computer vision technology. A YOLOv8 object detection model for distinguishing erect ear posture from neutral ear posture had a mean average precision (mAP) of 98.3% at 0.5 intersection over union (IoU) for all classes. This is acceptable for automatic detection of ear posture in gilts. A YOLOv8 instance segmentation model detected the outline of gilts in single-stall housing with an overall mAP of 99.5% at 0.5 IoU; this model's performance was also satisfactory. A YOLOv8 movement classification model assigning three classes of behavioral responses to gilts receiving the back-pressure test (BPT) showed high false positive rates for one class and high false negative rates for two classes. This model is not yet satisfactory in automatically classifying types of behavioral responses to the BPT. Additionally, a YOLOv8 object detection model was trained to identify four postures (sitting, standing, kneeling, and lying) in prepubertal gilts, achieving an overall average of 0.976 mAP at 0.5 IoU. This model was then applied to a 24-hour dataset of gilts transitioning from proestrus into estrus and metestrus. Time budgets were created for each gilt according to the four postures detected. No statistically significant differences existed among the four postures between pre-puberty and the conclusion of estrus."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Towards an electronic system for estrus detection in swine"]}]}],"canonical_facts":{"dc:contributor":["Condotta, Isabella","Green-Miller, Angela","Knox, Robert"],"dc:creator":["Bushman, Jeni L."],"dc:date":["2023-12","2023-12-05"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms","The student, Jeni Bushman, accepted the attached license on 2023-11-30 at 10:25.","The student, Jeni Bushman, submitted this Thesis for approval on 2023-11-30 at 10:34.","This Thesis was approved for publication on 2023-12-05 at 16:26.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20084 on 2024-03-01 at 13:15:18","Because most pigs worldwide are now bred by artificial insemination (AI), accurate estrus detection is essential. There is consensus that display of the lordosis response (“standing”) following physical boar contact or application of the back-pressure test (BPT) is the gold standard for estrus detection in pigs. However, little research has explored its true accuracy and evaluated whether automated measures could improve upon such a manually driven process. On commercial pig breeding farms, estrus detection relies on multiple trained technicians and daily efforts and occurs year-round. This places an undue burden on an already volatile and limited farm labor supply. And while research into automated technology specific to swine estrus detection is evolving, it remains underdeveloped. Although electronic estrus detection (EED) systems have been successfully integrated into the management of dairy and goat herds, use in swine breeding herds has faced challenges. Therefore, the aim of this thesis is to summarize current knowledge of estrus detection procedures in swine and extrapolate how these processes can be improved using computer vision technology. A YOLOv8 object detection model for distinguishing erect ear posture from neutral ear posture had a mean average precision (mAP) of 98.3% at 0.5 intersection over union (IoU) for all classes. This is acceptable for automatic detection of ear posture in gilts. A YOLOv8 instance segmentation model detected the outline of gilts in single-stall housing with an overall mAP of 99.5% at 0.5 IoU; this model's performance was also satisfactory. A YOLOv8 movement classification model assigning three classes of behavioral responses to gilts receiving the back-pressure test (BPT) showed high false positive rates for one class and high false negative rates for two classes. This model is not yet satisfactory in automatically classifying types of behavioral responses to the BPT. Additionally, a YOLOv8 object detection model was trained to identify four postures (sitting, standing, kneeling, and lying) in prepubertal gilts, achieving an overall average of 0.976 mAP at 0.5 IoU. This model was then applied to a 24-hour dataset of gilts transitioning from proestrus into estrus and metestrus. Time budgets were created for each gilt according to the four postures detected. No statistically significant differences existed among the four postures between pre-puberty and the conclusion of estrus."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/122042"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Jeni Bushman"],"dc:subject":["Machine Learning","Computer Vision","Estrus Detection","Back-pressure Test"],"dc:title":["Towards an electronic system for estrus detection in swine"],"dc:type":["text"],"thesis:degree_discipline":["Animal Sciences"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:00Z"}