Machine Learning Engineering and Optimization for Improving Seafood Production

In this project, machine learning and optimization will be applied to a 20 GB dataset on raw fish quality and process control parameters collected by the Tally software over a three-year period in a large industrial tuna cannery processor. The goal of the research is to design predictive machine learning and optimization algorithms maximizing the production yields and reducing waste, which could save hundreds of thousands or even millions of dollars a year depending on the seafood processor scale. The partner organization, ThisFish Inc. will embed the AI solutions obtained in this research project in the existing Tally software and offer to its customers, seafood processing companies in Canada and abroad. In addition, the project has an obvious sustainability aspect as it increases traceability of a product in the supply chain ensuring that seafood comes from environmentally and socially responsible harvesters and processors.

Faculty Supervisor:

Peter Khaiter

Student:

Bahareh Teimouri Lotfabadi

Partner:

ThisFish Inc.

Discipline:

Other

Sector:

Agriculture

University:

York University