AI-powered monitoring of livestock health and welfare using multimodal sensor data
Supervisors: Reza Arablouei (CSIRO), Greg Bishop-Hurley (CSIRO) and Rachael Rodney Harris (ANU).
Maintaining animal health and welfare is crucial for productivity and sustainability of livestock agriculture. Manual observation of animals to monitor their health and welfare is prohibitively laborious, especially when their number, or the area they spread over, is large. Multimodal sensor data collected via wearable devices, such as collars or ear tags, can be used to infer animal behaviour, movement patterns, and interactions with the environment or other animals.
In this project, the PhD candidate will develop new machine-learning-based models and analytics algorithms that enable remote monitoring of livestock health and welfare utilizing multimodal sensor data. The candidate will analyse existing sensor data to gain insights into hidden short- or long-term patterns that can guide development of new models and algorithms. They will then collect further data and refine the learned models.
The developed algorithms will be implemented on embedded systems of wearable devices to evaluate their real-world performance. The developed technologies will help automate monitoring livestock health and welfare, and hence manage them more effectively. This will improve the efficiency of livestock agriculture, reduce its environmental footprint, and make it more sustainable, in addition to promoting animal welfare and regulatory compliance.