{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/130220"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/130220","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Next generation phenotyping for production and efficiency traits in beef cattle","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-08-01","abstract_html":"Submission published under a 24 month embargo labeled &#x27;Closed Access&#x27;, the embargo will last until 2027-08-01","abstract_has_math":false,"creators":["Hanson, Erin Marie"],"institution":"University of Illinois Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Animal Sciences","degree_department":null,"school":null,"contributors":["Bresolin, Tiago"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-07-23","date_published":"2025-07-23","updated_at":"2026-07-22T22:25:06Z","subjects":["Biometric Traits","Digital Phenotypes","Depth Images","Feed Efficiency","Novel Phenotypes"],"languages":["en","eng"],"rights":["Copyright 2025 Erin Hanson"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/130220","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Bresolin, Tiago"]},{"key":"dc:creator","label":"Author","values":["Hanson, Erin Marie"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-07-23","2025-08"]},{"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 Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Biometric Traits","Digital Phenotypes","Depth Images","Feed Efficiency","Novel Phenotypes"]}]},{"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 2025 Erin Hanson"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/130220"]}]},{"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 2027-08-01","The student, Erin Hanson, accepted the attached license on 2025-07-22 at 15:41.","The student, Erin Hanson, submitted this Thesis for approval on 2025-07-22 at 15:55.","This Thesis was approved for publication on 2025-07-23 at 15:36.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22694 on 2025-10-25 at 15:54:32","There has been a growing need to improve feed efficiency traits in beef cattle to counteract the high costs of feed, which are part of the reason for the decline in beef cattle operations within the US. Advancements in sensor technologies have enabled the development of automated phenotyping tools to support management and breeding selection decisions. This study evaluated the potential of biometric body traits derived from depth images to predict production and efficiency traits in beef cattle. Specifically, we investigated the predictive performance of image-derived biometric features for body weight (BW) and feed efficiency traits including dry matter intake (DMI), residual feed intake (RFI), and residual average daily gain (RADG), the comparative performance of different predictive models, and the influence of image quantity and selection strategies on model accuracy. A total of 196 commercial Angus steers were video recorded using an Intel RealSense D455 camera. Biometric body traits, including projected volume, surface areas, length, width, and height, were extracted and summarized per animal using the median of multiple frames. Prediction models were developed using linear regression, partial least squares, elastic net, random forest, support vector machine, gradient boosting machine, and neural networks. Linear regression emerged as the most effective and practical model to predict all traits, achieving high accuracy with fewer computational requirements. For BW prediction, the combination of projected volume, flat surface area, and body length produced the most accurate results (R2 = 0.96; MAE = 10.12 kg). For DMI, the model using projected volume and flat surface area was the most effective (R2 = 0.59). However, biometric traits were not effective in predicting RFI, RADG, or residual intake and body weight gain (RIG) (R² < 0.01). Prediction accuracy was highest when biometric traits were summarized using all available frames, though models using 15 or more randomly or centrally selected frames also performed well. These findings demonstrate that biometric traits derived from depth imaging can accurately predict key production traits and may serve as valuable inputs for automated, non-invasive monitoring systems."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Next generation phenotyping for production and efficiency traits in beef cattle"]}]}],"canonical_facts":{"dc:contributor":["Bresolin, Tiago"],"dc:creator":["Hanson, Erin Marie"],"dc:date":["2025-07-23","2025-08"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-08-01","The student, Erin Hanson, accepted the attached license on 2025-07-22 at 15:41.","The student, Erin Hanson, submitted this Thesis for approval on 2025-07-22 at 15:55.","This Thesis was approved for publication on 2025-07-23 at 15:36.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22694 on 2025-10-25 at 15:54:32","There has been a growing need to improve feed efficiency traits in beef cattle to counteract the high costs of feed, which are part of the reason for the decline in beef cattle operations within the US. Advancements in sensor technologies have enabled the development of automated phenotyping tools to support management and breeding selection decisions. This study evaluated the potential of biometric body traits derived from depth images to predict production and efficiency traits in beef cattle. Specifically, we investigated the predictive performance of image-derived biometric features for body weight (BW) and feed efficiency traits including dry matter intake (DMI), residual feed intake (RFI), and residual average daily gain (RADG), the comparative performance of different predictive models, and the influence of image quantity and selection strategies on model accuracy. A total of 196 commercial Angus steers were video recorded using an Intel RealSense D455 camera. Biometric body traits, including projected volume, surface areas, length, width, and height, were extracted and summarized per animal using the median of multiple frames. Prediction models were developed using linear regression, partial least squares, elastic net, random forest, support vector machine, gradient boosting machine, and neural networks. Linear regression emerged as the most effective and practical model to predict all traits, achieving high accuracy with fewer computational requirements. For BW prediction, the combination of projected volume, flat surface area, and body length produced the most accurate results (R2 = 0.96; MAE = 10.12 kg). For DMI, the model using projected volume and flat surface area was the most effective (R2 = 0.59). However, biometric traits were not effective in predicting RFI, RADG, or residual intake and body weight gain (RIG) (R² < 0.01). Prediction accuracy was highest when biometric traits were summarized using all available frames, though models using 15 or more randomly or centrally selected frames also performed well. These findings demonstrate that biometric traits derived from depth imaging can accurately predict key production traits and may serve as valuable inputs for automated, non-invasive monitoring systems."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/130220"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Erin Hanson"],"dc:subject":["Biometric Traits","Digital Phenotypes","Depth Images","Feed Efficiency","Novel Phenotypes"],"dc:title":["Next generation phenotyping for production and efficiency traits in beef cattle"],"dc:type":["text"],"thesis:degree_discipline":["Animal Sciences"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:06Z"}