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 (as it is removed from the water) 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 including accurate data collection during experimental trials (e.g. feeds, temperature). Artificial intelligence and machine learning has now matured to a point that accurately counting and measuring abalone is possible using this approach, however specific application to the abalone industry is yet to be achieved. 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.