Tuna-AI: tuna biomass estimation with Machine Learning models trained on oceanography and echosounder FAD data

TL;DR

Tuna-AI leverages machine learning models trained on echo-sounder and oceanographic data to accurately estimate tuna biomass, enhancing fisheries monitoring and management.

Contribution

This paper introduces Tuna-AI, a novel machine learning approach that combines echo-sounder and oceanographic data for tuna biomass prediction.

Findings

Achieved accurate biomass estimation using 3-day echo-sounder data.

Utilized over 5000 set events for supervised training.

Improved tuna monitoring with integrated oceanographic information.

Abstract

Echo-sounder data registered by buoys attached to drifting FADs provide a very valuable source of information on populations of tuna and their behaviour. This value increases whenthese data are supplemented with oceanographic data coming from CMEMS. We use these sources to develop Tuna-AI, a Machine Learning model aimed at predicting tuna biomass under a given buoy, which uses a 3-day window of echo-sounder data to capture the daily spatio-temporal patterns characteristic of tuna schools. As the supervised signal for training, we employ more than 5000 set events with their corresponding tuna catch reported by the AGAC tuna purse seine fleet.