Commercial fishing is a bigger business than most of us think of it to be. It has a critical role in industries such as food, medicine, and beauty, for that matter. However, with increasing pollution and population, oceans today are witnessing overexploitation.

According to a report by McKinsey, “overall, the world’s fish consumption is predicted to increase by 20 per cent from 2016 to 2030, driven by global population growth, the expansion of the middle class, and greater urbanization.”

Overfishing is leading to various issues such as:

• Degradation of marine ecosystems
• Territorial conflicts
• Loss of marine biodiversity
• Illegal fishing
• Endangered food security
• Extinction of several species

The solution to these issues is Sustainable Fishing. 

So, what is sustainable fishing?

Sustainable fishing is employing those techniques of fishing that leave enough fish in the ocean, respect habitats and boundaries, and ensure people who depend on fishing make their livelihoods.

Globally, technological aids are being used for achieving sustainable fishing. Applying technology such as AI, ML, satellite data, and geospatial datasets can make fish farming sustainable while providing evidence to prove it.

Image recognition and object-detection tools with the help of deep learning are playing a significant role in the area. For example, onboard cameras and image recognition provide essential information to fisherman around their catch, its real-time location tracking, volume, size, surroundings, distance, and much more. 

Land and satellite-based mobile networks, smartphones today make it much easier for fisheries to transmit data from fishing vessels to be fed to algorithms for analysis. These developments will help commercial fisheries in making informed decisions during pre-catch, catch, and post-catch phases of the fishing process. 

Better decision-making will lead to better economic efficiency and decrease unintended fish mortality. Adopting advanced image processing and machine learning software can help analyze fish catch images onboard CCTV or any handheld device. This way, better information can be gathered about fish stocks and will ensure compliance with fisheries management regulations.

IBM, on these lines, is working with the aquaculture sector around the utilization of technology, data, and ML for both ecological and economic improvements.

In a report published by the UN’s Food and Agricultural Organisation (FAO), the percentage of fish stocks that are within biologically sustainable levels decreased from 90% in 1974 to 65.8% in 2017. This truly points towards the need for more fair and sustainable practices around fishing.

Important aspects in sustainable fishing are cost-cutting around monitoring and operational fishing costs. Artificial intelligence has the potential to cut these costs down and improve efficiency for fisheries.

Usage of AI for fishing can address global environmental issues and will work in harmony with seafood retailers and consumers while maintaining awareness in all the stakeholders for their contribution towards sustainability. Data acquisition through sensing platforms such as Onboard or underwater devices, satellites, drones is improving the whole game with better insights. However, there are cost constraints that are hindering many companies from adopting this. But with the pace at which AI and related technology are advancing, it is now time to take a more aggressive yet sustainable approach towards fishing and aquaculture.

The idea is to bring AI, economics, and ecology together to achieve desirable outcomes in favour of all the stakeholders while preserving biodiversity and benefiting society at large.