Automated detection of health and welfare compromises in pigs using video-based, machine learning behavioural analysis

Changes in animal behaviours are a useful aid in detecting early signs of compromised health or poor welfare. Early detection of problems allows timely intervention, mitigates adverse consequences and improves well-being. On a commercial scale, human observation of the subtle changes in behaviour that may indicate early-stage disease or abnormal behaviour (vice) is impractical. The aim of this studentship is to develop methods that automatically detect these behavioural changes using inexpensive, non-invasive and scalable technologies such as video analytics. 

The specific aims of the studentship are:

1) Develop machine-learning based methods for tracking the behaviour of individuals within a group, with or without additional sensors or explicit identification of individual pigs. Video detection methods will be combined with evidential reasoning networks to deduce behaviour patterns between pigs. 

2) Apply these novel methods to the early detection of two significance issues found in pig systems:

-the digestive disorders associated with the transition from milk to solids at weaning

-the occurrence of vice, such as tail-biting or flank chewing. 

These compromises are widespread within the industry. 

3) Establish unsupervised learning models that baseline normal behaviours that become further enriched over time; use advanced anomaly detection methods to identify behavioural changes occurring in each pen/group of pigs. 

4) Develop a pipeline to give early warning alerts to production supervisors that identify anomalous individuals or groups of pigs.

Specific requirements of applicants:

The successful applicant will have a minimum of an upper second class UK honours degree, or equivalent, in computing science, mathematics, applied mathematics, statistics or related disciplines, and preferably an MSc degree in the same or similar areas. Applicants with a degree related to data science are particularly encouraged.

Start date: 1 October 2021

Duration: 3 years

How to apply:

All applications must be submitted online through the Queen's Direct Applications Portal: https://dap.qub.ac.uk/portal/user/u_login.php