Machine learning helps accelerate NOAA fish surveys


Artificial intelligence is changing one of the most tedious of biologists’ tasks: fish counting.

With advances in underwater camera technology and machine-learning-based image processing, biologists with NOAA Fisheries have been able to complete some fish surveys in a fraction of the amount of time previously needed.

The survey data is incorporated into stock assessments, which help determine changes in the abundance of fishery stocks and are fundamental to management decisions, including setting quota.

Traditionally, NOAA – the National Oceanic and Atmospheric Association – has used survey methods including bottom trawling and acoustic surveys. Acoustic surveys give biologists and idea of the amount of fish in the middle of the water column but cannot identify the species present.

Now, camera-based surveys are being tested in a number of situations, NOAA fisheries biologist Kresimir Williams said.

“The one that I’m working with most closely at the moment involves having an actual camera in a trawl net, and having the trawl net just sort of aggregate the fish,” Williams said. “Then they can just be let go after that. We’re just collecting images as they go by.”

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