Tun-AI

Overview

After successful delivery of the project on dFAD drift prediction, Satlink wanted to develop an AI algorithm to automate and improve the conversion of raw echosounder signal to presence of tuna, which lies at the core of their business. Before, this information with complex spatio-temporal patterns had to be often interpreted by an expert biologist, and often had strong bias, besides being expensive and lacking scalability to a fleet of thousands of vessels. As supervised signal, we received data from 20 years of tuna fishing captures from OPAGAC, and we harvested echosounder data and oceanographic variables on the days prior to the capture as predictor variables to build the first tuna presence detector to be deployed at commercial scale by Satlink.

The Tun-AI project develops an AI-driven pipeline to transform raw data from echosounder buoys attached to drifting Fish Aggregating Devices (dFADs) into meaningful insights about tuna presence and biomass at sea. These buoys — widely deployed across tropical oceans as part of tuna purse-seine fisheries — continuously transmit location and acoustic backscatter information that can indicate the presence of fish under the buoy. By combining buoy data with satellite-derived oceanographic information (e.g., temperature, currents, chlorophyll) and historical catch records, the Tun-AI system uses machine learning models to estimate tuna biomass with high accuracy, effectively turning industrial monitoring infrastructure into a global biological sensor network.

Beyond improving biomass estimation for fishery operations, Tun-AI opens new avenues for scientific research into tuna behaviour and ecology. Traditional ecological studies of highly migratory species like tuna are costly and geographically limited; by harnessing data from thousands of buoys over long time periods, the project offers a cost-effective way to observe patterns of tuna aggregation and movement on an ocean scale. This collaborative effort between industry (buoy manufacturers and fleets) and researchers demonstrates how AI can add value to existing maritime technologies and support more sustainable, data-informed fishery management.

The project allowed Stalink to automate and serve predictions in real time with higher accuracy than the human predictions in use before the project. The AI model is currently in use by more than 1500 fishing vessels. My role in the project was principal investigator and responsible for the contract, which was executed by Komorebi AI, where I supervised a team of 4 data scientists who built, evaluated and deployed the model in production.

Media Coverage

• Tun-AI: Using echosounder buoy technology to study tuna behaviour at sea, Research Features Issue 149 (22/09/23)