WSC, First Nations develop Salmon Vision, a real-time machine learning model to track salmon returns

The Wild Salmon Center has partnered with several First Nations to use a combination of cutting-edge artificial intelligence tools and traditional Indigenous fishing methods to gain a better understanding of salmon runs in real time. The Salmon Vision deep learning model, which uses advanced artificial intelligence tools to identify and count fish species, is currently being utilized in various rivers around the North and Central Coasts of British Columbia. By 2024, Salmon Vision aims to provide reliable real-time fish count data to First Nations fisheries managers, thereby increasing their involvement in fisheries management decisions.

Fisheries managers on British Columbia’s Central Coast have to make decisions without knowing how many salmon are returning until after fishing seasons are over. They have to make forecasts and set harvest targets for commercial and recreational fisheries based on modeled data from the past. Emergency closures also have to be decided on when salmon populations start to decline. However, with the unpredictable and accelerating effects of climate change, it is increasingly difficult to rely on past data to predict future salmon returns. 

Dr. Will Atlas, Wild Salmon Center Senior Watershed Scientist, suggests a solution called “Salmon Vision.” A first-of-its-kind technology that combines artificial intelligence with ancient fishing weir technology, the Salmon Vision computer deep learning model can identify and count fish species. Developed by WSC in data partnership with the Gitanyow Fisheries Authority and Skeena Fisheries Commission, Salmon Vision aims to enable real-time salmon population monitoring for First Nations fisheries managers and beyond.

Automating fish counting is crucial for informed decisions while salmon are still running, according to many of our First Nations partners. Dr. Atlas suggests that underwater video technology can help us see those salmon returning to rivers. 

The Salmon Vision pilot study has annotated over 500,000 video frames captured at Indigenous-run fish counting weirs on the Kitwanga and Bear Rivers of B.C.'s Central Coast. Early assessments indicate that the technology is adept at tracking 12 different fish species passing through custom fish-counting boxes at the two weirs, with scores surpassing 90 and 80 percent accuracy for coho and sockeye salmon: two of the principal fish species targeted by First Nations, commercial, and recreational fishers. 

The Heiltsuk Nation is running Salmon Vision on a weir on the Koeye River. For First Nations like the Heiltsuk, weirs represent more than a revitalization of an age-old fishing technology. The rebuilding of weirs on rivers like the Koeye is a statement of First Nations sovereignty and their seat at the table in fisheries management decisions, as they were banned in the late 1800s by Canada's Department of Fisheries and Oceans as a way to consolidate control of fishery resources. 

"Modern-day expression of Heiltsuk title and rights and an avenue for us to be a part of the latest science," says William Housty, Associate Director of the Heiltsuk Integrated Resource Management Department. "And to make decisions not just for the betterment of this creek, but for the whole ecosystem." 

The Salmon Vision team is implementing automated counting on a trial basis in several rivers around the B.C. North and Central Coasts with partner First Nations. The goal is to provide reliable real-time fish count data to these partners by 2024. Ultimately, Dr. Atlas says, this groundbreaking A.I. technology could be in place in rivers across the North Pacific. 

"How many salmon are returning everywhere that we're fishing for salmon is the information we need," Dr. Atlas says. "You can't tell me with a straight face that you're having a sustainable fishery if you don't know how many fish you have coming back. And that's a problem right around the Pacific Rim." 

It's a problem with a promising solution, one that's just now coming into focus.