Project Outline

Maintaining animal health and welfare is crucial for the productivity and sustainability of livestock agriculture. Manual observation of animals to monitor their health and welfare is prohibitively laborious, especially when their numbers are large or they are spread over extensive areas. Sensor data, such as inertial measurements and positioning collected via wearable devices like collars or ear tags, can be used to infer animal behaviour, movement patterns, and interactions with the environment or other animals.

In this project, we aim to develop innovative data analytics and machine learning algorithms based on generative models to enable livestock health and welfare monitoring. By utilising lower-level information extracted from multimodal sensor data, such as behaviour, activity, and social interactions, we will analyse existing datasets to uncover hidden short- and long-term patterns. These insights will guide the development of new generative-AI-powered models and algorithms that relate key health and welfare parameters to multimodal sensor data. Our approach integrates cutting-edge generative AI and machine learning techniques with domain-specific knowledge in animal science, potentially advancing the field of precision livestock farming and enabling more proactive and efficient management of animal well-being.

We will consider implementing the developed algorithms on embedded systems of wearable devices and collect further data to refine the learned models. We will also analyse the theoretical properties of the proposed solutions.

The technologies developed in this research will revolutionise livestock management by enabling automated monitoring of animal health and welfare. This advancement will significantly enhance production efficiency while reducing the industry’s environmental footprint. By empowering farmers with data-driven insights, these innovations will promote higher animal welfare standards, improve regulatory compliance, and drive the livestock sector towards a more sustainable future.

To register an expression of interest, click here. You will need to outline why you have selected the research project and how your skills, experience and/or knowledge meet the project requirements.