Quality assessment of fish using image and video

This project aims to gauge fish size, species and freshness using computer vision and artificial intelligence. A computer-based digital system that provides an objective assessment based on ‘seeing’ and ‘smelling’ will be developed and applied to the emerging e-trade at Sydney Fish Market.

Artificial intelligence

Data science

Robotics and automation



4 years

Academic team

Associate Professor Jian Zhang

Engagement model


Sydney Fish Market

Food Agility CRC

Future applications

E-trade systems for produce markets


The current purchasing process at Sydney Fish Market is a hands on affair in which traders physically inspect the produce before they decide what to buy. There is no objective benchmark for quality and subjective assessments sometimes lead to disagreements and issues around trust, while paperwork inefficiencies can delay sales.

As the Fish Market moves towards an online trading system, they need to bring transparency to the supply chain and provide traders and consumers alike with immediate, verified information about the origin and quality of produce.



The UTS Tech Lab team will design a digital fish provenance and quality tracking system, using snapper as the test species.

An app will be created to allow fishers to record catch photos stamped with the time and location. AI-based image processing and e-nose technology, which analyses trace gases emitted by fish, will be integrated into an online platform and mobile app for use by both suppliers and retail customers. The technology will identify fish species, estimate size and evaluate freshness.

The project will also deliver a simplified sales process with a shorter time to market and access to catch lists and a quality assurance system.