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  Using artificial intelligence and machine vision for real time identification and prediction of aggressive behaviour in livestock


   School of Science, Engineering and Environment

  Dr Ali Alameer, Prof Sunil Vadera  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Information on this PhD research area can be found further down this page under the details about the Widening Participation Scholarship given immediately below.

Applications for this PhD research are welcomed from anyone worldwide but there is an opportunity for UK candidates (or eligible for UK fees) to apply for a widening participation scholarship.

Widening Participation Scholarship: Any UK candidates (or eligible for UK fees) is invited to apply. Our scholarships seek to increase participation from groups currently under-represented within research. A priority will be given to students that meet the widening participation criteria and to graduates of the University of Salford. For more information about widening participation, follow this link: https://www.salford.ac.uk/postgraduate-research/fees. [Scroll down the page until you reach the heading “PhD widening participation scholarships”.] Please note: we accept applications all year but the deadline for applying for the widening participation scholarships in 2024 is 28th March 2024. All candidates who wish to apply for the MPhil or PhD widening participation scholarship will first need to apply for and be accepted onto a research degree programme. As long as you have submitted your completed application for September/October 2024 intake by 28 February 2024 and you qualify for UK fees, you will be sent a very short scholarship application. This form must be returned by 28 March 2024. Applications received after this date must either wait until the next round or opt for the self-funded PhD route.

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Project description: Currently health and welfare assessment are done by trained assessors and as a result is time and labour consuming. This process can also be subjective, and it is sometimes questionable if the auditor is measuring animals under normal farm conditions. On the other hand, the automatic recording of animal-based parameters provides objective measurements in real-time, reducing the need to send specialized personnel out to farms. This field is known as Precision Livestock Farming (PLF) and PLF technology has a large market, as it can be used by farmers, veterinarians and other companies active in the livestock sector.

Behaviour parameters such as animal location and activity can be derived from real-time video monitoring and linked with health and welfare events on the farm. This project is aimed at the development of a health and welfare monitor for livestock based on automatic detection of visual behaviour parameters. Machine vision algorithms shall be used and further developed to calculate several characteristic parameters, such as animal position and movement. These parameters shall be related to specific behaviours, such as aggressive social behaviour.

In this project, you will enjoy developing on state-of-the-art deep learning methods which operates on both 2D and 3D (i.e., depth sensor and time of flight cameras) imaging systems for effectively identifying livestock behaviours and presents automated approaches for monitoring and investigation of livestock feeding, drinking, lying, locomotion and aggressive behaviour. You will be supported closely by experts in the field at the University of Salford: Dr Ali Alameer () and Professor Sunil Vadera ().

Please do not hesitate to contact us if you require any information or to discuss further project ideas within the field of Artificial Intelligence. 

Research training:

●       Designing and executing cutting edge experimental research

●       Analysis and interpretation of results

●       Contributing to manuscript writing

●       Training in state-of-the-art machine learning techniques

●       Presentation of data to expert audiences.  

Computer Science (8) Mathematics (25) Veterinary Sciences (35)

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