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      Automated monitoring of brush use in dairy cattle

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          Abstract

          Access to brushes allows for natural scratching behaviors in cattle, especially in confined indoor settings. Cattle are motivated to use brushes, but brush use varies with multiple factors including social hierarchy and health. Brush use might serve an indicator of cow health or welfare, but practical application of these measures requires accurate and automated monitoring tools. This study describes a machine learning approach to monitor brush use by dairy cattle. We aimed to capture the daily brush use by integrating data on the rotation of a mechanical brush with data on cow identify derived from either 1) low-frequency radio frequency identification or 2) a computer vision system using fiducial markers. We found that the computer vision system outperformed the RFID system in accuracy, and that the machine learning algorithms enhanced the precision of the brush use estimates. This study presents the first description of a fiducial marker-based computer vision system for monitoring individual cattle behavior in a group setting; this approach could be applied to develop automated measures of other behaviors with the potential to better assess welfare and improve the care for farm animals.

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          Support-vector networks

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            Applied Logistic Regression

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              THE DISTRIBUTION OF THE FLORA IN THE ALPINE ZONE.1

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: Writing – review & editing
                Role: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLOS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                25 June 2024
                2024
                : 19
                : 6
                : e0305671
                Affiliations
                [001] Animal Welfare Program, Faculty of Land and Food Systems, The University of British Columbia, Vancouver, BC, Canada
                Universidade do Porto Instituto de Biologia Molecular e Celular, PORTUGAL
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                [¤]

                Current address: Institute of Animal Welfare Science, University of Veterinary Medicine, Vienna, Austria

                Author information
                https://orcid.org/0000-0002-0901-3057
                https://orcid.org/0000-0002-1559-1216
                https://orcid.org/0000-0002-1427-3152
                https://orcid.org/0000-0002-0917-3982
                Article
                PONE-D-24-01454
                10.1371/journal.pone.0305671
                11198893
                38917231
                2b949cf5-df27-45a3-8c2e-daaf656ad282
                © 2024 Sadrzadeh et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 11 January 2024
                : 3 June 2024
                Page count
                Figures: 4, Tables: 2, Pages: 14
                Funding
                Funded by: investment agriculture
                Award ID: INV 174
                Award Recipient :
                DMW received funding for this project from the Investment Agriculture Foundation of BC (#INV174; https://iafbc.ca/). The funders played no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Physical Sciences
                Mathematics
                Applied Mathematics
                Algorithms
                Machine Learning Algorithms
                Research and Analysis Methods
                Simulation and Modeling
                Algorithms
                Machine Learning Algorithms
                Computer and Information Sciences
                Artificial Intelligence
                Machine Learning
                Machine Learning Algorithms
                Computer and Information Sciences
                Computer Vision
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Vertebrates
                Amniotes
                Mammals
                Bovines
                Cattle
                Biology and Life Sciences
                Zoology
                Animals
                Vertebrates
                Amniotes
                Mammals
                Bovines
                Cattle
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Vertebrates
                Amniotes
                Mammals
                Ruminants
                Cattle
                Biology and Life Sciences
                Zoology
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                Amniotes
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                Computer and Information Sciences
                Artificial Intelligence
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                Biology and Life Sciences
                Anatomy
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                Medicine and Health Sciences
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                Biology and Life Sciences
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                Collective Animal Behavior
                Social Sciences
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                Behavior
                Animal Behavior
                Collective Animal Behavior
                Biology and Life Sciences
                Zoology
                Animal Behavior
                Collective Animal Behavior
                Computer and Information Sciences
                Artificial Intelligence
                Machine Learning
                Support Vector Machines
                Physical Sciences
                Physics
                Electromagnetic Radiation
                Radio Waves
                Custom metadata
                The data supporting the findings of this study are openly available in the University of British Columbia's Dataverse repository at https://doi.org/10.5683/SP3/YJU5ML.

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