ARTIFICIAL intelligence use in the wool industry will be investigated by a collaboration of Australian Wool Innovation, neXtgen Agri Ltd and the University of Sydney.
The first-of-its-kind artificial intelligence research project will investigate the potential of applying AI in the sheep and wool industry, with the overall objective of providing sheep breeders with tools to use advanced phenotypes and AI technologies.
AWI’s general manager of research Dr Jane Littlejohn said the first stage of the project is to provide a proof of concept that novel phenotyping technologies based on image analysis, bio-marker and bio-sensor technologies combined with deep learning AI technologies can unlock new horizons for the Australian sheep industry.
“This project will provide a proof of concept that semi-automated images captured and combined with machine learning techniques can be used to determine identification through facial recognition, wrinkle scores, face cover and live-weight in sheep,.
“The long-term goal of the project is to evaluate the use of advanced phenotypes and artificial intelligence technologies for the prediction of lifetime performance at young ages, management of performance changes in real time, and provide advanced highly predictive phenotypes as inputs for ongoing selection decisions” Dr Littlejohn said.
“AWI’s farm automation program ranges from bringing new tech minds into the industry through the Australian eChallenge Wool Innovation, which is a collaboration with the University of Adelaide, to making remote properties fully serviced with WiFi and linking this to smart tags.
“This project, in partnership with neXtgen Agri is just another approach to bringing technology on-farm. Exploring the potential use of artificial intelligence in everyday practises on-farm is an exciting and future focussed prospect,” she said.
neXtgen Agri project leader Mark Ferguson said the potential of this project lies in the possibility of remote and automatic weighing and identification of animals without extensive infrastructure.
“This project will also lay the foundation for new and innovative ways to assess traits in sheep without additional time and effort from farm managers,” he said.
Results from the project are expected to be analysed mid-2019.
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