Osama Alshabani

Artificial Intelligence Specialization · Cohort 2026

Fish Classification with Wideband Hydroacoustics and Deep Learning

The province of Ontario in Canada has one of the greatest densities of lakes in the world. Sustaining and managing these important populations is vital for maintaining the ecosystem and allowing species persistence despite harvesting. A key measure that fisheries managers require is species abundance. This allows them to understand how abundances change spatially and temporally in response to various stressors and to implement effective management strategies. Traditionally, fish population abundances are tracked through invasive capture methods which require time, labour, and material investments and result in the mortality of many fishes.Hydroacoustic surveying has become an alternative to invasive capture methodologies and is currently being tested by the Ontario Ministry of Natural Resources as a possible alternative approach. In this, sonar is used to locate organisms and objects in the water and the sound emitted at up to 400 distinct frequencies bounces off organisms back to a receiver. These signals received may act as a species “fingerprint” allowing the classification of species and abundance calculations. Automating species identification from acoustic responses “remains the ‘Holy Grail’ to acoustic researchers”. Achieving species recognition through hydroacoustic processes will revolutionize the monitoring and management of commercially important fish populations in Ontario and beyond.

Read the final report (PDF)