Autonomous monitoring of dairy cattle body mass and oestrus behaviour using a deep learning approach


The goal of this study is to develop an autonomous monitoring system of dairy cattle body mass and oestrus behaviour using a deep learning approach. This would enable farmers and veterinarians to make more timely interventions during early lactation to improve dairy cattle health, fertility, and productivity. We will build on the early success of a commercially available lameness detection system developed by our industry collaborator CattleEye.

Early lactation, typically defined as the first 100 days post-calving, is the most critical period of a dairy cow’s productive life because management during this period influences health, fertility, production and longevity with the herd. Most dairy cows experience a negative energy balance (NEB) during this period and lose weight because the energy demand of peak milk production exceeds energy intake. A NEB predisposes to numerous metabolic diseases and impairs fertility. Up to half (28 to 50%) of cows remain anovulatory after 50 days post-calving and so fail to express normal oestrus behaviour. Furthermore, conception rate decreases by 10 % per 0.5-unit loss of body condition score or 5% of body mass.

We will use the CattleEye video system for the collection of 2D images of approximately 15,000 cows from twelve participating dairy farms. We are already collecting these data as part of an Innovate UK project. The student will train the neural network using multisource data and temporal patterns, such as the 20 to 24 day oestrus cycle. Data sources will include technologies already available for oestrus detection such as pedometers and accelerometers. 


Applications should be made by emailing  with:

·        a CV (including contact details of at least two academic (or other relevant) referees);

·        a covering letter – clearly stating your first choice project, and optionally 2nd ranked project, as well as including whatever additional information you feel is pertinent to your application; you may wish to indicate, for example, why you are particularly interested in the selected project(s) and at the selected University;

·        copies of your relevant undergraduate degree transcripts and certificates;

·        a copy of your IELTS or TOEFL English language certificate (where required);

·        a copy of your passport (photo page).

A GUIDE TO THE FORMAT REQUIRED FOR THE APPLICATION DOCUMENTS IS AVAILABLE AT Applications not meeting these criteria may be rejected.

In addition to the above items, please email a completed copy of the Additional Details Form (as a Word document) to . A blank copy of this form can be found at:

Informal enquiries may be made to 

The deadline for all applications is 12noon on Monday 9th January 2023. 


Open to students worldwide

Funding information

Funded studentship

CASE studentships are funded by the Biotechnology and Biological Sciences Research Council (BBSRC) for 4 years. Funding will cover tuition fees at the UK rate only, a Research Training and Support Grant (RTSG) and stipend. We aim to support the most outstanding applicants from outside the UK and are able to offer a limited number of bursaries that will enable full studentships to be awarded to international applicants. These full studentships will only be awarded to exceptional quality candidates, due to the competitive nature of this scheme.



Post-partum muscle is associated with delayed time to first oestrus and commencement of luteal activity in an all-year-round calving dairy herd. Journal of Dairy Science (in press).
Automated monitoring of behaviour in zebrafish after invasive procedures. Scientific reports, 9(1), pp.1-13, 2019.
Skeletal muscle and adipose tissue reserves and mobilisation in transition Holstein cows: Part 2 association with postpartum health, reproductive performance and milk production. Animal: an international journal of animal bioscience, 16(9), 100626.
Association of Body Condition Score with Ultrasound Measurements of Backfat and Longissimus Dorsi Muscle Thickness in Periparturient Holstein Cows
Animals, 11(3), 818.