The seafood auction begins at 5:30am every weekday the Sydney Fish Market. Buyers can inspect the produce before deciding what to buy. But in a digital marketplace, how can buyers be confident in their purchases if they can’t touch and smell the fish?
This is the challenge Sydney Fish Market sought to address as it moves towards an online trading system. Consumers and suppliers alike want verified, trusted information about where their fish was caught, conditions of transportation and ultimately the quality attributes of the product.
This project designed a digital fish provenance and quality tracking system, using snapper as the test species. Starting with the catch, the team developed an app for fishers to upload information about how, when and where the fish was caught. Fishers can also upload a photo and verify the species using image processing technology.
Remote sensors then track the fish on its way to market. Data from IoT enabled packaging, temperature and location sensors and an 'eNose' that measures fish freshness, is added to the blockchain.
All of this information informs the fish quality index and the online auction trading, as well as potential integration to consumer apps to assure customers about quality and provenance.
June 2021 - Video: From 'bait to plate' with blockchain
December 2020 - Virtual Seminar, Prof Ren Ping Liu: Introducing BeFAQT; Blockchain Enabled Fish Provenance and Quality Tracking
November 2020 - Final Report: Seafood Tracking and Traceability
October 2020 - Recognition: Food Agility Project Team win AIIA award
Supply & Business Development Manager at Sydney Fish Market
Head of Discipline at SEDE Networking and Cybersecurity in the School of Electrical and Data Engineering at the University of Technology Sydney.
Associate Professor, School of Biomedical Engineering at the University of Technology Sydney.
Associate Professor, School of Electrical and Data Engineering at the University of Technology Sydney.
Yu, G., Zha, X., Wang, X., Ni, W., Yu, K., Yu, P., Zhang, J.A., Liu, R.P. and Guo, Y.J., 2020. Enabling Attribute Revocation for Fine-Grained Access Control in Blockchain-IoT Systems. IEEE Transactions on Engineering Management, DOI 10.1109/TEM.2020.2966643
Yu, G., Zha, X., Wang, X., Ni, W., Yu, K., Zhang, J.A. and Liu, R.P., 2020. A Unified Analytical Model for Proof-of-X Schemes. Computers & Security, p.101934, https://doi.org/10.1016/j.cose.2020.101934
Yu, G., Wang, X., Yu, K., Ni, W., Zhang, J.A. and Liu, R.P., 2020. Survey: Sharding in blockchains. IEEE Access, 8, pp.14155-14181, DOI: 10.1109/ACCESS.2020.2965147
