Fuentes, S, Gonzalez Viejo, C, Tongson, E et al. (3 more authors) (2022) Animal biometric assessment using non-invasive computer vision and machine learning are good predictors of dairy cows age and welfare: The future of automated veterinary support systems. Journal of Agriculture and Food Research, 10. 100388. ISSN 2666-1543
Abstract
Digitally extracted biometrics from visible videos of farm animals could be used to automatically assess animal welfare, contributing to the future of automated veterinary support systems. This study proposed using non-invasive video acquisition and biometric analysis of dairy cows in a robotic dairy farm (RDF) located at the Dookie campus, The University of Melbourne, Australia. Data extracted from dairy cows were used to develop two machine learning models: a biometrics regression model (Model 1) targeting (i) somatic cell count, (ii) weight, (iii) rumination, and (iv) feed intake and a classification model (Model 2) mapping features from dairy cow's face to predict animal age. Results showed that Model 1 achieved a high correlation coefficient (R = 0.96), slope (b = 0.96), and performance, and Model 2 had high accuracy (98%), low error (2%), and high performance without signs of under or overfitting. Models developed in this study can be used in parallel with other models to assess milk productivity, quality traits, and welfare for RDF and conventional dairy farms.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Artificial intelligence; Cows physiology; Mastitis; Animal biometrics; Short range remote sensing |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Biological Sciences (Leeds) > School of Biology (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 19 Dec 2022 13:45 |
Last Modified: | 19 Dec 2022 13:45 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.jafr.2022.100388 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:194092 |