Use of precision technologies and advanced analytical techniques for early detection and measurement of lameness in dairy herds

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.