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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 Raboisson, Didier

PloS one, 2019-12, Vol.14 (12), p.e0226669-e0226669 [Periódico revisado por pares]

United States: Public Library of Science

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Título:
Recording behaviour of indoor-housed farm animals automatically using machine vision technology: A systematic review
Autor: Wurtz, Kaitlin ; Camerlink, Irene ; D'Eath, Richard B ; Fernández, Alberto Peña ; Norton, Tomas ; Steibel, Juan ; Siegford, Janice
Raboisson, Didier
Assuntos: Agricultural equipment ; Algorithms ; Analysis ; Animal behavior ; Animal genetic engineering ; Animal Husbandry - methods ; Animal sciences ; Animal Welfare ; Animals ; Animals, Domestic - physiology ; Automatic control ; Automation ; Behavior, Animal ; Biology and Life Sciences ; Cameras ; Computer and Information Sciences ; Computer vision ; Data bases ; Data collection ; Documents ; Engineering and Technology ; Farm management ; Farms ; Gait ; Global positioning systems ; GPS ; Group size ; Health screening ; Housing ; Image detection ; Information sources ; Internet of Things ; Literature reviews ; Livestock ; Machine vision ; Occupancy ; Phenotypes ; Phenotyping ; Physical Sciences ; Posture ; Poultry ; Qualitative analysis ; Recording ; Research and Analysis Methods ; Researchers ; Reviews ; Sensors ; Social Sciences ; Specifications ; Swine ; Systematic review ; Technology ; Veterinary medicine ; Video cameras ; Video Recording - methods ; Vision systems ; Zoology
É parte de: PloS one, 2019-12, Vol.14 (12), p.e0226669-e0226669
Notas: ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Undefined-1
ObjectType-Feature-3
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Competing Interests: The authors have declared that no competing interests exist.
Descrição: 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.
Editor: United States: Public Library of Science
Idioma: Inglês

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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 Raboisson, Didier

United States: Public Library of Science

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