Intellipig: An automated on-farm pig health monitoring system

Our proposed System is a completely non-intrusive face-based (artificial) intelligent monitoring system that can automatically capture welfare and health data for monitoring and management of pigs.

Why? Being able to continuously monitor and assess farm animal health and welfare depends on the deployment of practical, valid, and reliable measurement tools. Current on-farm welfare assessment protocols involve daily spot-checks by staff, and periodic spot-checks by veterinarians or quality assurance inspectors. Assessed welfare parameters are often resource-based concerning provision for basic needs, or animal-based, looking mainly at easily-measurable factors such as physical condition. Most are performed at a group level as individual identification can be difficult. Rarely is animal behaviour recorded and even rarer still are measures that can tell us something about the emotional state of the individual animal.

How will the system work? Our innovative approach is to measure an animal's emotional state as well as its body condition using a completely non-intrusive face-based, (artificial) intelligent monitoring system. We have already successfully developed machine learning algorithms that identify individuals using facial biometrics and are able to detect changes in facial expression that indicate whether a pig is stressed or not. We have also developed body condition scores and weight estimation using these techniques. Here we propose to combine all of these capabilities into one face based, non-intrusive animal health and welfare monitoring station for use on commercial farms. By employing these state-of-the-art machine learning techniques, our system will offer the capacity for on-going learning about individual animals, and consequently allow for early detection of altered health/welfare, personalised thresholds for intervention, and tailored treatment approaches. Such individualised data recording can be integrated with other measurable parameters, such as individual food and water intake, treatment history, growth and weight gain, which will allow better optimisation of farm production efficiency.