Tun-AI: Machine Learning for Tuna Biomass Estimation
Published:
Smart buoys attached to drifting Fish Aggregating Devices (dFADs) are deployed across tropical oceans by tuna purse-seine fleets. Each buoy carries an echosounder that measures acoustic backscatter in the water column and transmits data hourly by satellite. The raw signal is rich in information about fish presence, but interpreting it at scale required automation and a model that combined acoustic and environmental data.
Tun-AI uses three-day windows of echosounder data alongside satellite-derived oceanographic variables (sea surface temperature, currents, chlorophyll) to estimate tuna biomass under the buoy. As training signal, the model draws on more than 5000 documented capture events with confirmed tuna catch tonnage, provided by OPAGAC. The system aims to automate a task that was previously done by expert biologists.
I developed Tun-AI (a tuna detection software) at Komorebi AI, in collaboration with Satlink, the buoy manufacturer. The work was published in Fisheries Research (Q2) and attracted coverage in Research Features magazine.