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A smart fishing net that can identify individual species could help reduce the amount of fish discarded at sea
Spotted: According to Our World in Data, globally we throw just under 10 per cent of the fish and marine animals we catch back into the ocean. This often happens when the wrong species are caught in bottom-fishing nets, which indiscriminately hoover up everything they encounter. In an attempt to reduce this tragic waste, France’s National Institute for Ocean Science (Ifremer) is working with a number of partners to pilot an AI-powered smart net—called Game of Trawls—that can sort fish in the water.
The device (whose name is both a reference to Game of Thrones, and an acronym for Giving Artificial, Monitoring Intelligence to Fishing Trawls) uses a network of sensors and cameras to detect and identify the species that enter the fishing nets in real-time. This allows fishers to select the specific species and size of fish they want before they drag the net back on board the boat. Unwanted fish are released through a trap in the net.
The system requires the AI to recognise a living organism in real time, in motion, and in a poorly-lit environment. For this, a very large amount of data was needed. At the moment, the system recognises around 25 species.
Eric Guygniec, head of fishing company Apak, which is a partner in the project, explained the need for the smart net. “I’m not interested in having the fish on the deck and sorting it once it’s dead”, he says. “I prefer to sort it on the bottom. With such a device, we know at all times what goes into the net, the size of the fish and the species, and if the species is not of interest to us, we can open a trap door.”
The FAO has recently found that 34.2% of fisheries are overfished, meaning they are below sustainable levels. This has a big impact on both livelihoods and on the the ocean ecosystem as a whole. To combat this, a number of organisations, in addition to Ifremer, are working on solutions. Ideas include lab-grown fish and monitoring platforms to reduce illegal fishing.