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


Autoři: Kaitlin Wurtz aff001;  Irene Camerlink aff002;  Richard B. D’Eath aff003;  Alberto Peña Fernández aff004;  Tomas Norton aff004;  Juan Steibel aff001;  Janice Siegford aff001
Působiště autorů: Department of Animal Science, Michigan State University, East Lansing, Michigan, United States of America aff001;  Department of Farm Animals and Veterinary Public Health, Institute of Animal Welfare Science, University of Veterinary Medicine Vienna, Vienna, Austria aff002;  Animal Behaviour & Welfare, Animal and Veterinary Sciences, Scotland’s Rural College (SRUC), Edinburgh, United Kingdom aff003;  M3-BIORES– Measure, Model & Manage Bioresponses, KU Leuven, Leuven, Belgium aff004;  Department of Fisheries and Wildlife, Michigan State University, East Lansing, Michigan, United States of America aff005
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0226669

Souhrn

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.

Klíčová slova:

Algorithms – Animal behavior – Birds – Cameras – Computer vision – Livestock – Poultry – Swine


Zdroje

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