Climate change is placing increasing pressure on livestock systems, with rising temperatures and humidity leading to heat stress in cattle. Heat stress reduces animal welfare, lowers productivity, and contributes to economic and environmental inefficiency in food production. Developing tools to detect and mitigate heat stress is therefore crucial for building sustainable and climate-resilient agri-food systems.

This project will develop cutting-edge computer vision approaches to automatically extract respiration rates from calves and cows as a key physiological marker of heat stress. Respiration rate is a sensitive and early indicator of heat load, but existing measurement techniques are invasive, labour-intensive, or impractical at scale. By leveraging video data (including visible and thermal imaging), the student will design algorithms to detect subtle movements or thermal fluctuations associated with breathing.

To provide a holistic assessment, respiration data will be combined with environmental measures such as temperature and humidity, specifically through the Temperature-Humidity Index (THI), a widely used predictor of heat stress. This fusion of animal- and environment-level data will enable robust prediction of heat stress events. A further challenge, and key focus of the PhD, will be the development of reliable methods for individual animal identification and attribution of respiration rates in group-housed environments, using biometric features such as facial or coat pattern recognition.

You will gain advanced training in computer vision, machine learning, sensor fusion, and precision livestock farming. You will work with multidisciplinary teams and industry stakeholders to validate your system in real farm environments. The outcomes will not only contribute to animal welfare and sustainability but also advance AI methodologies for physiological monitoring in complex, real-world settings.

This project offers the opportunity to contribute to the transformation of livestock farming into a more sustainable, efficient, and climate-resilient system through AI innovation.

Further information

This 4-year PhD programme is under the UKRI AI Centre for Doctoral Training in Sustainable Understandable agri-food Systems Transformed by Artificial Intelligence (SUSTAIN). SUSTAIN is a collaboration between the Universities of Lincoln, Aberdeen, Queen’s Belfast and Strathclyde, and focuses on the application of Artificial Intelligence (AI) to sustainable agri-food. Academic staff and partners have co-created projects based on key industry challenges, which will be shaped with your input during the first year of your PhD. 

All projects will benefit from external partner involvement. Recruitment is being co-ordinated by the University of Lincoln. 

Shortlisting will take place week commencing 27 October, with interviews taking place online between 12 November and 1 December. Candidates will be informed week commencing 1 December. Thereafter, the successful candidate will then complete and submit their application to the University of Strathclyde.