Harnessing 3D cameras and deep learning for on-the-fly automated body condition and mobility analysis to improve cattle welfare

A growing world population and climate change are stressing food availability. High animal welfare and health
practices are more important than ever to satisfy societal demands for the livestock sector. The use of precision
monitoring instrumentation for dairy cattle is key to optimising production while maintaining animal health. In this
PhD project, 3D video technology with the latest Intel cameras will be used to unobtrusively provide stress-free
monitoring of incremental changes in individual cow mobility and body condition with the aim of understanding
behavioural cues preceding observed lameness to improve cow health, welfare and productivity and hence increase
the climate and environmental sustainability of milk production. These traits are currently measured by manual visual
assessment, requiring high skill levels and training, but are nevertheless open to the subjectivity of individuals and
rarely capture the longitudinally detail needed for novel behaviour research. This will realise a system that can be
transplanted into farms without extensive instrumentation, allowing farmers and others in the value chain such as
vets, nutritionists and livestock advisers to make use of much more precise, consistent and frequent measurements,
creating greater opportunities to improve cow performance and welfare.
The studentship would suit either a mathematical or computational student interested in sustainable food production,
or someone with veterinary or biosciences expertise who wishes to build up artificial intelligence skills - in either case
a tailored training package will be developed to suit. The student will learn the key facets of animal welfare assessment,
and use this and cutting edge AI to build and apply the system across the studentship timeline so we can develop a
better understanding of lameness. The student will be based 50%/50% at two leading, geographically close institutes
- Bristol Robotics Laboratory at the University of West of England, and Bristol Veterinary School & Visual Information
Laboratory at the University of Bristol, and will benefit from a broad cross-disciplinary supervision team, led by Prof
Melvyn Smith (Machine Vision) and Prof Andrew Dowsey (One Health Data Science), who have published state-of-the art work in this area that will be built upon (see https://doi.org/10.1016/j.compind.2018.02.011 ,
https://arxiv.org/abs/2006.09205, https://www.biorxiv.org/content/10.1101/2020.08.03.234203v2).
Data collection and validation will harness the Bristol Veterinary School's John Oldacre Centre for Sustainability and
Welfare in Dairy Production, a new research centre based at our Wyndhurst dairy farm, which will include blanket
24/7 video coverage of all our 185 cows linked to data on production, emissions and veterinary assessments
(https://www.bristol.ac.uk/vet-school/research/john-oldacre-centre--farm-research-data-platform)