AgTech with Artificial Intelligence in Monitoring of Dairy Cattle Welfare

Closing Date

31st December 2020*

Applicants should contact the primary supervisor, and submit their Expression of Interest (EOI) and Application as soon as possible.

*unless filled earlier

Colleges Involved
  • College of Science and Engineering
  • College of Arts, Law and Education
The Research Project

A study of animal behaviour is potentially the most powerful indicator of animal welfare (Dawkins, 2004). The incorporation of technologies (e.g. camera) into dairy farms does allow the behaviours of dairy cattle to be closely monitored but does not provide a level of intelligence to support decision making, such as associating changes in behaviour patterns with wellbeing. Recent developments in artificial intelligence (AI) have driven great progress in modelling human activity and behaviour using data obtained from visual sensors (Herath et al., 2017).

In this research, the same idea could be used to monitor the behaviour of dairy cattle or calves in real time. Advanced techniques such as deep learning (Lecun et al., 2015), which outperformed humans in several tasks, can be applied to identify behaviour patterns and link them to animal welfare. For example, changes in behaviour (i.e. from high to low activity, decline or absence of positive behaviour such as play, visits to the water trough/feeder) are a sensitive, sub-clinical indicator of poor health. This research aims to develop an innovative and global leading technological system to monitor wellbeing of dairy cattle, using state-of-art AI and machine learning technologies. Dawkins, M. S. (2004). Using behaviour to assess animal welfare. Animal Welfare, 13(Suppl), S3-S7. Samitha Herath, Mehrtash Harandi, Fatih Porikli. Going deeper into action recognition. Image Vision Computing. Volume 60 Issue C,  Pages 4-21, 2017. Yann LeCun, Yoshua Bengio, Geoffrey Hinton. Deep learning. Nature. Volume 521, pages 436–444 (28 May 2015).

This project will be based at the Newnham Campus, Launceston, TAS.

Eligibility
  • The project is open to domestic (Australian and New Zealand) and international applicants. Preference will be given to applicants who can commence in 2019
  • The PhD study must be undertaken on a full-time basis.
  • Applicants must hold a degree from a Recognised Tertiary Institution as being in the appropriate discipline and equivalent to: - A Bachelor Honours degree of at least second class upper standard with a Minimum Research Component - A Masters of Research Degree with a Minimum Research Component - A Masters Degree (Coursework) or Graduate Diploma with a Minimum Research Component.
  • Applicants for whom English is not their first language will be required to provide: IELTS with Minimum Overall Score of 6.5, while achieved 6.5 for writing and speaking and no other band less than 6.0 (academic module), or TOEFL with Minimum Overall Score of 92, while achieved 26 for writing and speaking, and 20 for reading and listening, or PTE Academic with Minimum Overall Score of 58, while achieved 58 for writing and speaking with no other score lower than 50
  • Candidates must have knowledges in the following fields: artificial intelligence, machine learning and computer vision

See the following web page for entry requirements: www.utas.edu.au/research/degrees/what-is-a-research-degree

Application Process

Applicants who require more information or are interested in this specific project should first contact the listed Supervisor. Information and guidance on the application process can be found on the Apply Now website.

Information about scholarships is available on the Scholarships webpage.

More Information

Please contact, Dr Winyu Chinthammit for further information.