Early detection of health and welfare compromises through automated detection of behavioural changes in pigs.
Early detection of health and welfare compromises in commercial piggeries is essential for timely intervention to enhance treatment success, reduce impact on welfare, and promote sustainable pig production. Behavioural changes that precede or accompany subclinical and clinical signs may have diagnostic value. Often referred to as sickness behaviour, this encompasses changes in feeding, drinking, and elimination behaviours, social behaviours, and locomotion and posture. Such subtle changes in behaviour are not easy to quantify and require lengthy observation input by staff, which is impractical on a commercial scale. Automated early-warning systems may provide an alternative by objectively measuring behaviour with sensors to automatically monitor and detect behavioural changes. This paper aims to: (1) review the quantifiable changes in behaviours with potential diagnostic value; (2) subsequently identify available sensors for measuring behaviours; and (3) describe the progress towards automating monitoring and detection, which may allow such behavioural changes to be captured, measured, and interpreted and thus lead to automation in commercial, housed piggeries. Multiple sensor modalities are available for automatic measurement and monitoring of behaviour, which require humans to actively identify behavioural changes. This has been demonstrated for the detection of small deviations in diurnal drinking, deviations in feeding behaviour, monitoring coughs and vocalisation, and monitoring thermal comfort, but not social behaviour. However, current progress is in the early stages of developing fully automated detection systems that do not require humans to identify behavioural changes; e.g., through automated alerts sent to mobile phones. Challenges for achieving automation are multifaceted and trade-offs are considered between health, welfare, and costs, between analysis of individuals and groups, and between generic and compromise-specific behaviours.
Open Lab, School of Computing Science, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK. Electronic address: stephen.matthews@newcastle.ac.uk.
Stephen G
SG
Matthews
School of Agriculture, Food and Rural Development, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK.
Amy L
AL
Miller
School of Agriculture, Food and Rural Development, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK.
James
J
Clapp
Open Lab, School of Computing Science, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK.
Thomas
T
Plötz
School of Agriculture, Food and Rural Development, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK.
Ilias
I
Kyriazakis
D000822:Animal Husbandry / Q000295:instrumentation / Q000379:methods*; D000827:Animal Welfare; D000818:Animals; D001331:Automation / Q000295:instrumentation*; D001522:Behavior, Animal; D006304:Health Status; D034421:Sus scrofa
Animal behaviour; Automated detection; Health and welfare; Pigs; Sensors