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Early intervention is critical to improving treatment outcomes and reducing recurrence of lameness in dairy cows; however, this is dependent on the reliable detection of lame cows. Advances in sensor and smart computing technologies and their use on-farm provide possibilities to achieve this and therefore potential to produce huge gains for the industry through lameness reduction.
This research aims to explore and develop novel data driven solutions for accurate automated identification of lameness in dairy cattle, using cutting-edge sensor technologies. The approaches to develop technology-based objective methods to measure lameness will include;
1) Using existing commercially available senor technologies to classify lameness
2) Investigate the feasibility of novel sensors to classify lameness
3) Optimise the use of multiple sensors and performance of learning algorithms from sensor data
The research will combine advanced data analytics, including machine learning, with the practical aspects of developing a novel methodology of measurement. In addition, the industrial partner (Agriculture and Horticulture Development Board; AHDB) will provide the student with the opportunity to participate in work related to translation of research outputs to the industry. The successful applicant will gain knowledge in the feature engineering and use various machine learning algorithms, such as Neural Networks, K-nearest Neighbor, Support Vector Machines and Decision Trees.
The research will be conducted at the ‘Centre for Dairy Science Innovation’ (CDSI) at Nottingham, utilising recent investments in this high-level research infrastructure. The successful student will also spend a period of time with the industrial partner, AHDB.
Further information and Application:
Applicants should have a first or 2.1 undergraduate degree (or a minimum of a 2.2 degree in addition to a Masters degree) in Animal Science, Veterinary Science, Applied Statistics, Veterinary Epidemiology or similar subjects, and should have a strong interest in quantitative analysis and epidemiology.
Informal enquiries should be addressed to: laura.randall@nottingham.ac.uk
Funding details
Funding is available for four years from October 2019. A full award would be fees plus an annual stipend. This is set by the Research Councils and will be £15,009 for 2019/20. A higher rate stipend is available to students with a recognised veterinary degree qualification; for 2018-2019 this is £22,806.
Eligibility for full funding is restricted to UK students.
Click here to apply for this project.
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