Evidence map›Paper›PMID 31869364›Full record

SynthesisPloS one2019

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

Kaitlin Wurtz, Irene Camerlink, Richard B D'Eath, Alberto Peña Fernández, Tomas Norton, Juan Steibel, Janice Siegford

Abstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
43citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

43 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Review
  5. Article
  6. AI Ethics Guidelines: Time to Include Animals.Science and engineering ethics · 2026
    Article
  7. Review
  8. Review
  9. Review
  10. Article
  11. Review
  12. Review
  13. Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. Using Sound Location to Monitor Farrowing in Sows.Animals : an open access journal from MDPI · 2023
    Article
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Kaitlin WurtzDepartment of Animal Science, Michigan State University, East Lansing, Michigan, United States of America.ORCID 0000-0001-7566-1573
Irene CamerlinkDepartment of Farm Animals and Veterinary Public Health, Institute of Animal Welfare Science, University of Veterinary Medicine Vienna, Vienna, Austria.ORCID 0000-0002-3427-2210
Richard B D'EathAnimal Behaviour & Welfare, Animal and Veterinary Sciences, Scotland's Rural College (SRUC), Edinburgh, United Kingdom.
Alberto Peña FernándezM3-BIORES- Measure, Model & Manage Bioresponses, KU Leuven, Leuven, Belgium.
Tomas NortonM3-BIORES- Measure, Model & Manage Bioresponses, KU Leuven, Leuven, Belgium.ORCID 0000-0002-0161-3189
Juan SteibelDepartment of Animal Science, Michigan State University, East Lansing, Michigan, United States of America.
Janice SiegfordDepartment of Animal Science, Michigan State University, East Lansing, Michigan, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Animals, DomesticAnimal HusbandryAnimalsAnimal WelfareBehavior, AnimalVideo Recording

Identifiers

PMID31869364
PMCPMC6927615

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.