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Details of Grant 

EPSRC Reference: EP/S00128X/1
Title: Automatic disease detection and monitoring in calves
Principal Investigator: Fennell, Dr J
Other Investigators:
Talas, Dr L
Researcher Co-Investigators:
Project Partners:
AHDB (Agri & Horticulture Dev Board) ARLA Foods UK IRT Diagnose KG
MSD Animal Health ROBDOK Kft Siculovet SRL
Vetservis Volac International Ltd Westpoint Veterinary Group
Department: Experimental Psychology
Organisation: University of Bristol
Scheme: EPSRC Fellowship - NHFP
Starts: 01 June 2018 Ends: 31 May 2021 Value (£): 609,617
EPSRC Research Topic Classifications:
Artificial Intelligence Image & Vision Computing
EPSRC Industrial Sector Classifications:
Pharmaceuticals and Biotechnology
Related Grants:
Panel History:
Panel DatePanel NameOutcome
08 May 2018 EPSRC UKRI CL Innovation Fellowship Interview Panel 3 - 8 and 9 May 2018 Announced
Summary on Grant Application Form
Bovine respiratory disease (BRD) is the most common and costly disease affecting cattle in the world. BRD is a complex bacterial infection that can be fatal and is estimated to cost the UK cattle industry £80M annually. Although manual scoring systems exist to aid early identification of the disease, they are time consuming and rarely used in practice. Commonly, identification of BRD is only in later stages of the disease when antibiotics are essential for treatment. Early and automated identification of BRD will have significant impact: 1) on the economic cost to farmers; 2) reducing the quantity of antimicrobial medicines used to treat the disease; and 3) improving the general welfare of animals.

The proposed project uses artificial intelligence techniques, coupled with visible-range and thermal cameras, to identify BRD at the earliest possible stage with main goals of establishing: 1) how early in disease development affected animals can be reliably identified; 2) the best way to scale up image capture and machine learning to automatically screen animals and alert farmers to those needing treatment; together with 3) developing a protocol for effective use of the trained system. The aim of the proposal is to develop a system based on providing the best possible information in a timely manner, which is key to making right judgements for farmers and vets alike. It is believed that a system based on low cost cameras and sensors, together with state of the art deep neural networks, can provide this.

The completed fellowship will result in a working and tested prototype system capable of development into a viable commercial product. During the fellowship a network of industry collaborators (including farmers, vets, advisory/regulatory bodies, equipment manufacturers and food producers) will be developed to support and promote the research and resulting product.
Key Findings
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Potential use in non-academic contexts
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Date Materialised
Sectors submitted by the Researcher
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Project URL:  
Further Information:  
Organisation Website: http://www.bris.ac.uk