Comparison of machine learning algorithms for estimation of pig body weights from on-animal and digital image measurements

dc.contributor.advisorTulpan, Dan
dc.contributor.authorWang, Zhuoyi of Animal Biosciencesen_US of Guelphen of Scienceen_US Biosciencesen_US
dc.description.abstractLive body weight (LBW) is an important parameter for both animal health and market requirements for livestock. My thesis focused on exploring the feasibility of a semi-automatic system that estimated the LBW of pigs by applying 4 machine learning (ML) methods (Linear Regression, K-Nearest Neighbor, Decision Tree, and Random Forest) using 3 biometric and 6 morphometric measurements extracted from digital images acquired from 26 pigs in Arkell Swine Research Facility with a consumer-level camera and a reference object. Age, age group, and 6 morphometric measurements significantly influenced LBW estimation, while gender did not. The correlation between manual and image-based measurements was positive and moderately high. Prediction errors for ML models trained on the two acquisition methods were within a  3 kg range, with image-based measurements performing up to 16% lower than manual measurements. This cost- and time-efficient system shows potential for intelligent solutions in animal research and production.en_US
dc.description.sponsorshipFood From Thought Funding (499063)
dc.publisherUniversity of Guelphen
dc.rights.licenseAll items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated.
dc.subjectMachine learning algorithmsen_US
dc.subjectMachine learning algorithmsen_US
dc.subjectLive body weighten_US
dc.subjectMachine learning modelsen_US
dc.subjectsemi-automatic/automatic estimationen_US
dc.titleComparison of machine learning algorithms for estimation of pig body weights from on-animal and digital image measurementsen_US
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