ML for sustainable fishing - based on the FishAI competition by NORA

Motivation

This project dives into using data to plan better fishing routes. By analyzing past environmental data like sea temperature, moon phase, and fishermen’s notes, we aim to create data-driven models that help fishermen catch more fish efficiently.

Comment: This project is based on the Fish AI competition by NORA.

Research Question

How can machine learning techniques optimize fishing routes using historical environmental data?

Tasks and Timeline

Stage 1:

• Define the fishing planning problem;
• Delimitate the solution concept to the use of historical data;
• Review the state-of-the-art and proposed solutions in the literature;
• Brainstorm potential solutions for the problem.

Stage 2:

• Plan the experiments and test for the expected solution;
• Describe the dataset;
• Develop a pre-processing data methodology;
• Select meaningful features from the dataset;
• Design a proof-of-concept for potential solutions via minimum viable product concept;
• Analyze the proposed proof-of-concept.

Stage 3:

• Evaluate the proof-of-concept with the proposed experiments and tests;
• Benchmark the proof-of-concept against other solutions in the literature.

Stage 4:

• Document the problem description and solution-design methodology;
• Show the results to reviewers and non-technical audiences.

Expected Outcomes

Mandatory Products

• A system to plan routes to catch fish based on historical data;
• Mathematical analysis of the proposed algorithm;
• Software with documentation associated with the project;
• Technical report with problem description, proposed solutions, experimental results, and project conclusions by following the University guidelines;
• A public dissertation following the University guidelines.

Optional Products

• Summary paper from the technical report suitable for conferences or journals;
• 3-minute elevator pitch video of the project;
• Blog post or video explaining the problem and proposed solution for a general audience.

Bibliography

• Nordmo, T. A. S., Kvalsvik, O., Kvalsund, S. O., Hansen, B., Halvorsen, P., Hicks, S., … & Riegler, M. A. (2022). Fish AI: Sustainable Commercial Fishing. Nordic Machine Intelligence (NMI), 2(2), 1-3.