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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

  ,  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

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.  


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