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AWI applying artificial intelligence in wool industry
31 Aug '18
2 min read
Australian Wool Innovation (AWI), a not-for-profit company, in partnership with neXtgen Agri and the University of Sydney is studying the potential of applying artificial intelligence (AI) in the sheep and wool industry. The first of its kind AI research project aims to provide sheep breeders with tools to use advanced phenotypes and AI technologies.
Dr. Jane Littlejohn, AWI’s general manager of research outlined 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,” Littlejohn explained.
“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,” Littlejohn said.
“AWI’s farm automation programme 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,” Littlejohn added.
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, according to Littlejohn.
Mark Ferguson, neXtgen Agri project lead, 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,” Ferguson said.
Results from the project are expected to be analysed mid-2019. (SV)
Dr. Jane Littlejohn, AWI’s general manager of research outlined 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,” Littlejohn explained.
“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,” Littlejohn said.
“AWI’s farm automation programme 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,” Littlejohn added.
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, according to Littlejohn.
Mark Ferguson, neXtgen Agri project lead, 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,” Ferguson said.
Results from the project are expected to be analysed mid-2019. (SV)
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