Predicting TB status of dairy cows from mid infra-red spectral data using machine learning

Project Details


Bovine tuberculosis (bTB) is a chronic, infectious and zoonotic (i.e., it can be transmitted to humans) disease endemic in the UK and other countries, and presents a significant challenge to the UK cattle sector particularly in the south west of England and south Wales. The Department for Environment, Food and Rural Affairs (DEFRA) lists bTB as one of the four most important livestock diseases globally. The continued spread of bTB among cattle in England and Wales has been a socioeconomic disaster for over 40 years, causing catastrophic and devastating damage to farming businesses both large and small. In 2017 the number of animals in the UK slaughtered due to bTB was in excess of 43,500. The disease has proven difficult to completely eradicate using techniques that are socially acceptable and at a cost acceptable to the UK taxpayer. Current costs are estimated at over £175 million per year with an average cost of £34,000 per bTB outbreak per farm. The continued polarised debate on the role of wildlife as a farmed cattle disease reservoir is making progress slow. This project seeks to develop a non-invasive tool created from routine milk recording of dairy cattle to predict bTB status from milk analysis (by spectrophotometry) by exploiting state of the art Deep Learning techniques. Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms (an algorithm is a process followed to solve calculations). Deep learning works by imitating the way that the human brain works and involves feeding a computer system a large volume of data, which it can use to make decisions about other data. This method of analysis has been successfully deployed by our group to predict pregnancy status in dairy cows with high accuracy and hence expectations are high that bTB leaves a signal in milk that can be detected with Deep Learning applied to MIR spectral data. The involvement of a commercial partner (NMR, National Milk Records) that is extensively active in the bTB area ensures that results can be rapidly applied to maximise impact in the short term. Furthermore, NMR has a long history of supporting dairy farmers in herd management (including disease) and so the results of this project will be exploited in a familiar context for dairy farmers ensuring its widespread uptake.
Effective start/end date1/01/196/09/21


  • Biotechnology and Biological Sciences Research Council

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  • SDG 3 - Good Health and Well-being


Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.