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Recording behaviour of indoor-housed farm animals automatically using machine vision technology: A systematic review

Large-scale phenotyping of animal behaviour traits is time consuming and has led to increased demand for technologies that can automate these procedures. Automated tracking of animals has been successful in controlled laboratory settings, but recording from animals in large groups in highly variable...

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Autores principales: Wurtz, Kaitlin, Camerlink, Irene, D’Eath, Richard B., Fernández, Alberto Peña, Norton, Tomas, Steibel, Juan, Siegford, Janice
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6927615/
https://www.ncbi.nlm.nih.gov/pubmed/31869364
http://dx.doi.org/10.1371/journal.pone.0226669
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author Wurtz, Kaitlin
Camerlink, Irene
D’Eath, Richard B.
Fernández, Alberto Peña
Norton, Tomas
Steibel, Juan
Siegford, Janice
author_facet Wurtz, Kaitlin
Camerlink, Irene
D’Eath, Richard B.
Fernández, Alberto Peña
Norton, Tomas
Steibel, Juan
Siegford, Janice
author_sort Wurtz, Kaitlin
collection PubMed
description Large-scale phenotyping of animal behaviour traits is time consuming and has led to increased demand for technologies that can automate these procedures. Automated tracking of animals has been successful in controlled laboratory settings, but recording from animals in large groups in highly variable farm settings presents challenges. The aim of this review is to provide a systematic overview of the advances that have occurred in automated, high throughput image detection of farm animal behavioural traits with welfare and production implications. Peer-reviewed publications written in English were reviewed systematically following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. After identification, screening, and assessment for eligibility, 108 publications met these specifications and were included for qualitative synthesis. Data collected from the papers included camera specifications, housing conditions, group size, algorithm details, procedures, and results. Most studies utilized standard digital colour video cameras for data collection, with increasing use of 3D cameras in papers published after 2013. Papers including pigs (across production stages) were the most common (n = 63). The most common behaviours recorded included activity level, area occupancy, aggression, gait scores, resource use, and posture. Our review revealed many overlaps in methods applied to analysing behaviour, and most studies started from scratch instead of building upon previous work. Training and validation sample sizes were generally small (mean±s.d. groups = 3.8±5.8) and in data collection and testing took place in relatively controlled environments. To advance our ability to automatically phenotype behaviour, future research should build upon existing knowledge and validate technology under commercial settings and publications should explicitly describe recording conditions in detail to allow studies to be reproduced.
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spelling pubmed-69276152020-01-07 Recording behaviour of indoor-housed farm animals automatically using machine vision technology: A systematic review Wurtz, Kaitlin Camerlink, Irene D’Eath, Richard B. Fernández, Alberto Peña Norton, Tomas Steibel, Juan Siegford, Janice PLoS One Research Article Large-scale phenotyping of animal behaviour traits is time consuming and has led to increased demand for technologies that can automate these procedures. Automated tracking of animals has been successful in controlled laboratory settings, but recording from animals in large groups in highly variable farm settings presents challenges. The aim of this review is to provide a systematic overview of the advances that have occurred in automated, high throughput image detection of farm animal behavioural traits with welfare and production implications. Peer-reviewed publications written in English were reviewed systematically following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. After identification, screening, and assessment for eligibility, 108 publications met these specifications and were included for qualitative synthesis. Data collected from the papers included camera specifications, housing conditions, group size, algorithm details, procedures, and results. Most studies utilized standard digital colour video cameras for data collection, with increasing use of 3D cameras in papers published after 2013. Papers including pigs (across production stages) were the most common (n = 63). The most common behaviours recorded included activity level, area occupancy, aggression, gait scores, resource use, and posture. Our review revealed many overlaps in methods applied to analysing behaviour, and most studies started from scratch instead of building upon previous work. Training and validation sample sizes were generally small (mean±s.d. groups = 3.8±5.8) and in data collection and testing took place in relatively controlled environments. To advance our ability to automatically phenotype behaviour, future research should build upon existing knowledge and validate technology under commercial settings and publications should explicitly describe recording conditions in detail to allow studies to be reproduced. Public Library of Science 2019-12-23 /pmc/articles/PMC6927615/ /pubmed/31869364 http://dx.doi.org/10.1371/journal.pone.0226669 Text en © 2019 Wurtz et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Wurtz, Kaitlin
Camerlink, Irene
D’Eath, Richard B.
Fernández, Alberto Peña
Norton, Tomas
Steibel, Juan
Siegford, Janice
Recording behaviour of indoor-housed farm animals automatically using machine vision technology: A systematic review
title Recording behaviour of indoor-housed farm animals automatically using machine vision technology: A systematic review
title_full Recording behaviour of indoor-housed farm animals automatically using machine vision technology: A systematic review
title_fullStr Recording behaviour of indoor-housed farm animals automatically using machine vision technology: A systematic review
title_full_unstemmed Recording behaviour of indoor-housed farm animals automatically using machine vision technology: A systematic review
title_short Recording behaviour of indoor-housed farm animals automatically using machine vision technology: A systematic review
title_sort recording behaviour of indoor-housed farm animals automatically using machine vision technology: a systematic review
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6927615/
https://www.ncbi.nlm.nih.gov/pubmed/31869364
http://dx.doi.org/10.1371/journal.pone.0226669
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