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Machine Learning Applications for Fisheries Management adsa


IAP and Spring


SEAG: Sea Grant Program

Faculty Supervisor:

Michael Triantafyllou

Faculty email:


Apply by:

December 3, 2020


Contact Brian Anthony and Rob Vincent; banthony@mit.edu and rvincent@mit.edu

Project Description

Imaging surveys used in fisheries management are challenging in terms of time, accuracy, cost, and resource availability. Currently, humans are required to spend hours manually reviewing images and attempting to identify and quantify species present. Image recognition is one solution, yet challenges in training computers to accurately distinguish and quantify finfish and shellfish species persist. Information and technology generated from this work will enable resource managers to more accurately assess fisheries resources and improve management of sustainable fisheries. The project will develop machine learning software for automated identification and quantification of sea scallops and associated finfish species present in survey photographs. The objective is to develop effective, low cost, and automated image recognition capabilities to better serve the fisheries management community. Students will be involved in programming, design, testing, and analysis. Due to COVID-19, this will be a remote position involving computer programming and zoom meetings.


The project involves machine learning programming and development for image recognition. Experience with Matlab, Python, Yolo, Darknet and/or OpenCV would be helpful.