Early detection of health and welfare compromises through automated detection of behavioural changes in pigs

Stephen G Matthews,Amy L Miller,James Clapp,Thomas Plötz,Ilias Kyriazakis

doi:10.1016/j.tvjl.2016.09.005

Abstract

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.

Highlights

In recent years, there has been increased concern over pig welfare under intensive farming systems, with the scientific consensus being that an animal’s welfare state should be enhanced (Mellor, 2016)
Growing pigs show a diurnal rhythm of activity (Costa et al, 2009; Chung et al, 2014), and typically display increased activity from social and exploratory behaviour follows feeding in growing pigs, with approximately 70% of their time inactive (Maselyne et al, 2014a)
Previous reviews have focused on physiology (Eigenberg et al, 2008) and the ability to measure behaviour (Frost et al, 1997; Wathes et al, 2008; Cornou and Kristensen, 2013); this review focuses on digital automation for monitoring behaviour and detecting behavioural change

Summary

Introduction

There has been increased concern over pig welfare under intensive farming systems, with the scientific consensus being that an animal’s welfare state should be enhanced (Mellor, 2016). Detection of health and welfare compromises will increase treatment success, may contain problems, and enhance pig welfare and system sustainability. Early detection typically requires human observation, which can be subjective, and examination of individuals to detect salient changes and clinical signs (Radostits et al, 2007a). One way to achieve early detection of health and welfare compromises in animals is to utilise behavioural changes. Such changes precede clinical signs of disease or injury and affect animal performance (Hulsen and Scheepens, 2006; González et al, 2008; Kyriazakis and Tolkamp, 2010). Automation in commercial piggeries presents substantial challenges (Banhazi et al, 2015) for sensor and computer hardware, sensor data processing, computer vision, and machine learning

Objectives

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Conclusion