AI-PigNet: The AI of social interactions for next gen smart animal breeding

Rapid population growth, climate change and increasing demand for animal products and higher animal welfare provide unprecedented challenges to livestock production. Automatic monitoring systems and cutting-edge Artificial Intelligence (AI) technologies, hold great promise for the quantification of animal-animal interactions to improve livestock management and selection. Next generation animal breeding can use data from animal interactions to select animals that are better suited for modern production conditions by enabling the selection of animals with improved behavioural patterns and higher social fitness. To accomplish this, it is necessary to overcome several challenges such as the ability to accurately monitor and quantify diverse types of social interactions in large number of individuals and to incorporate these measures into novel genetic prediction models that accurately predict the genetic merit for productivity and health of animals in different social environments.

The overall aim of this project is to develop novel AI routines that accurately capture social interactions of pigs from automated on farm monitoring systems and integrate these into genetic prediction models for smart animal breeding. To achieve this, we will for the first time unite three disciplines, i.e. machine/deep learning (ML/DL), social network analysis (SNA) and quantitative genetics, to achieve the following specific objectives:

AI to identify social interactions: Construct informative social interaction measures that describe the social structure and the role of each individual for different types of social interactions of putative welfare and productivity importance.
AI to establish associations between social interactions and key phenotypes. Establish how these networks and individual social interaction measures change over time and are affected by the genetic make-up of animals, and investigate their association with key productivity, health and welfare traits.
AI to improve predictions: Incorporate social interaction measures into smart breeding for animal productivity, health and welfare and evaluate improvement in prediction accuracies and genetic gain.
To accomplish this goal, we have established a multi-disciplinary team of experts in computational genetics, AI, SNA, animal behaviour and welfare, and breeding from leading academic institutes in the UK and USA, and PIC, the world's largest pig breeding company to translate knowledge to impact. We have access to a vast amount of unique, multi-dimensional data of thousands of commercial pigs (ranging from video data generated by the first successful automated system for reliably recording animal position and posture under commercial conditions, to production and health records, and genomic data) to establish and validate the computational pipeline to achieve the above project objectives.

This pump priming project focuses on pigs due to our strong background IP, research track record and available data. However, we anticipate that the methodology developed will be applicable to other production systems. The methods and knowledge generated will be beneficial for different stakeholders, e.g. scientists, breeders and farmers. Direct involvement of the world leading pig breeding company PIC, and close connections with the farming community will ensure a swift uptake of the research findings by the industry to generate impact at scale. This will help to improve the competitiveness of the UK livestock science and industry, with significant economic impact. Furthermore, exchange visits, and training courses and workshops organized during the life-time of this project allow skill development and knowledge exchange between the UK and USA partners, and establish professional networks for long-term collaborations in AI for smart animal breeding.