Enhanced Animal Behavioural Analytics For Improved Cattle Welfare, Health, Productivity and Sustainability

The Quant Foundry Artificial Intelligence (AI) Livestock Surveillance Solution in collaboration with Bristol Veterinary School at the University of Bristol aims to provide a world-class solution for the identification of anomalous cattle behaviour to aid in the rapid identification of different ailments. The solution combines AI-driven video analytics of animals within an automated farm framework to increase health and welfare and lower production costs and emissions.

While there are a number of existing solutions for remote monitoring of animals, many require an active involvement of people with little potential cost savings. Other solutions require the use of physical hardware that must be worn by the animal, requiring significant per-animal setup and maintenance costs. Internationally there are a number of ongoing research trials for image recognition, however there is little mention of their use identifying specific illnesses. Many video systems applied to livestock do not scale well with the number of animals, whereas the Quant Foundry system can identify and track multiple animals with little computational overhead. The unique innovation is our general computing model with standard off-the-shelf hardware that will be able to identify many different conditions for each animal. This considerably reduces the time and cost of development, deployment and upkeep.

This research and feasibility study will be performed across two areas: (i) classification and identification of key animal behaviour features to be applied to our deep learning algorithm, and (ii) a commercial feasibility study to assess the commercial effectiveness of the hardware and identification algorithm for identifying anomalous behaviours such as lameness and other abnormal motions. The validation study will involve an installation of the system at Agri-EPI Centre's South West Dairy Development Centre to record continuous video of every cow after milking over a 9-month period. Through an existing AI system developed by the University of Bristol, individual cows will be identified and linked to production/veterinary data and behavioural annotations from Bristol Vet School experts, verified by external assessors. This data will be used to validate and refine the Quant Foundry AI solution, and will also be curated for public dissemination as a resource for the field.

The final stage will be to assess the overall effectiveness of the primary lameness, mastitis and Johne's disease solution and determine its benefits for commercialisation and research. This would lead to further studies to advance fundamental animal welfare, behaviour and sustainability research.