Machine learning as a tool for early detection of high-risk piglets

Two distinct machine learning approaches will be used to identify high-risk piglets predisposed to post-weaning diarrhoea and other diseases with the intention of being able to separate them from healthy individuals and/or reduce their occurrence by breeding management.

The first approach is based on facial feature analysis of stress in sows (intervention C), whereas the second is based on integrated analysis of faecal metagenomics data and metadata collected longitudinally from individual piglets (intervention D).

In both approaches, data will be analysed retrospectively to identify behavioural and metagenomics markers that can be used to predict disease.