The global population has a large—and growing—seafood appetite. Fish, crustaceans, and mollusks, largely from the oceans, account for over one-fifth of all animal protein consumed by humans. From 2016 to 2030, seafood consumption is expected to rise by 20%. As a result, the world's fisheries are under strain.
More seafood may gratify customers, but it depletes marine species that help to maintain ecosystem stability. To keep up with the fast-growing demand, fishermen are also targeting fish that haven't traditionally been popular, such as grenadiers and blue lingcods. Because these deep-water fish breed considerably more slowly than more common types such as carp, their extinction could have serious consequences.
Furthermore, because ocean biodiversity is disappearing, fishermen must work harder and harder to obtain fish. Because targeted species are in short supply, it takes five times as many kilowatt-hours of work to haul in a given number of fish as it did in 1950. Fishermen are now able to navigate their boats further out into the ocean and fish in deeper waters than ever before. In reality, since the 1950s, the area fished by fisherman has increased from 60% to 90% of the world's oceans.
Despite rising scarcity, fishermen have been able to enhance their catch of edible ocean species because to technological advancements. When it comes to tracking the availability of fish stocks, however, approaches remain mostly unchanged.
According to Matts Johansen, CEO of Aker BioMarine, a leading provider of krill-based goods, “right now, fishing firms employ old-school methods to measure biomass of different species.” “You go out with a net and catch in a grid in a specific region to see how much you can catch. It's incredibly expensive and time-consuming.”
This does not have to be the case. Advanced analytics, in combination with sophisticated sensing technologies, may be able to help fishermen satisfy rising seafood demand while also maintaining marine biodiversity and improving efficiency.
Precision fishing begins with the collection of more data of higher quality. Satellite-mounted optical sensors can provide fishermen with a high-resolution image of the maritime environment. Drones with cameras and sensors can be readily manoeuvred above the water's surface, as well as underwater. While fishing, onboard or underwater equipment can provide fisherman with real-time data.
Machine learning can analyse and interpret data as it comes in from sensing equipment in real time. Fishermen can learn more about the species, volume, and size of the fish they're catching this way. Fishermen will move away from relying on intuition, experience, and direct observation to employing high-resolution models and daily forecasts throughout the whole fishing sector to locate fish. Instead of physically sorting catches, workers will be able to scan them using cameras and use computerised sorting technologies to control their quality.
Large-scale fishing firms throughout the world might save $11 billion a year if they switch to an analytics-driven precision fishing model. Lower pricing for fish and seafood would benefit customers. Precision fishing techniques can also help with better ocean resource management. This may boost the industry's revenues by up to $53 billion by 2050, while also doubling or more than doubling overall fish biomass.
Authorities in charge of fisheries management can also improve their efficiency. They will be able to take continuous stock of fish populations all over the world and update management decisions accordingly, rather than assessing fisheries once a year and developing management approaches that do not alter until the following annual assessment.
“You obtain the information you need in real time if we can construct robust and verified data models. Then, depending on this information, you may alter regulations,” Johansen added.
While data and algorithms may appear to be better suited to boardrooms than to boats, they have helped some fisheries make significant increases. Adopting precision-fishing techniques on a larger scale could be beneficial.