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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Veröffentlicht in:PloS one 2019-12, Vol.14 (12), p.e0226669-e0226669
Hauptverfasser: Wurtz, Kaitlin, Camerlink, Irene, D'Eath, Richard B, Fernández, Alberto Peña, Norton, Tomas, Steibel, Juan, Siegford, Janice
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Camerlink, Irene
D'Eath, Richard B
Fernández, Alberto Peña
Norton, Tomas
Steibel, Juan
Siegford, Janice
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.
doi_str_mv 10.1371/journal.pone.0226669
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subjects 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
Livestock housing
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
title Recording behaviour of indoor-housed farm animals automatically using machine vision technology: A systematic review
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