Application of a machine learning approach for effective stock management of abalone (Old ID 26742)
Role
Chief Investigator
Description
Determining the number and size distribution of abalone present at various stages of production is critical information for effective stock management. Currently the Australian abalone aquaculture industry spends in the order of $25,000 per annum, per farm, gathering this information by hand. However, the resulting data is of mediocre quality, is limited in its scope, and collecting the data causes stress to the animals which can compromise growth and survival. Automated counting and measuring of abalone will increase farm efficiency and productivity in the short term and, in the longer term, will provide an advanced platform for further R&D improvements. Artificial intelligence and machine learning has now matured to a point that accurately counting and measuring abalone is possible using this approach. This project would involve the development, training and validation of a machine learning model to identify, segment and measure quantitative abalone traits in production systems, and render the product data to be accessible and applicable for farmers.
Date
24 Jul 2020 - 31 Aug 2022
Project Type
GRANT
Keywords
Abalone (Haliotidae);Machine Learning
Funding Body
Fisheries Research & Development Corporation
Amount
115649
Project Team
Marcus Sheaves;Ickjai Lee;Kyungmi Joanne Lee;Carlo Mattone;Jason Holdsworth;Art Suwanwiwat