Unleashing the Potential: Big Data and AI Revolutionizing the Fishing Industry
Commercial fishing is a much more significant industry than most of us think to be. In fact, it plays a crucial part in the food, medical, and beauty industries. However, as pollution and population rise, overfishing is now a problem in the oceans. There are several problems brought on by overfishing, including: Degradation of marine ecosystems, Territorial conflicts, Loss of marine biodiversity, Illegal fishing, Endangered food security, Extinction of several species. Sustainable fishing offers a solution to these problems. Hence, Sustainable fishing is using fishing methods that respect habitats and boundaries, ensure there are enough fish in the ocean, and provide a living for those who depend on fishing.
According to a McKinsey analysis “Overall, the world’s fish consumption is predicted to increase by 20% from 2020 to 2030, driven by global population growth, the development of the middle class, and more urbanization.” Technology is being used on a global scale to promote sustainable fishing. Utilizing technology like artificial intelligence (AI), machine learning (ML), satellite data, and geospatial datasets can make fish farming sustainable and provide proof of it.
Deep learning-based tools for object and image recognition are becoming increasingly important in this field. For instance, onboard cameras and image recognition give fishermen crucial information about their catch, including its volume, size, surroundings, distance, and many other factors.
Today, it is much simpler for fisheries to transmit data from fishing vessels to be supplied to algorithms for analysis thanks to land- and satellite-based mobile networks and smartphones. Commercial fisheries will benefit from these developments by being better able to make decisions during the pre-catch, catch, and post-catch phases of the fishing process.
How AI empowers aquaculture decision-making?
Fisheries use AI to collect data on the various organized and operational fisheries items. It is a geographic information system that is used for the creation, upkeep, and updating of distribution maps of marine species with significant commercial value.
- Collecting the majority of the data from their sensors.
- Predictive analytics will be developed using Sensing Aqua technology to improve data-driven decision making.
- Artificial intelligence is used by the robotic fish Shoal to identify pollution in the water.
- The robots must be able to navigate their surroundings after being launched as a group.
The use of video and image analytics in marine environments is one example of artificial intelligence in fisheries. VIAME is an open-source system that Kitware created in collaboration with NOAA’s Automated Image Analysis Strategic Initiative (AIASI) for the analysis of underwater video and imagery for fisheries stock assessment. VIAME will make it possible to quickly and affordably integrate new algorithmic modules, datasets, and workflows.
Driven improvements:
Big data technologies for monitoring of fisheries:
However, the authorities now face a new and more serious issue as a result of the nearly complete monitoring of fisheries: the camera recordings are only useful if they are carefully examined. The only reliable method for government departments to learn about fishing practices and mark illegal activity is through this. Due to this, some control authorities only perform infrequent audits of the records before using “trust basis” comparisons to compare their findings with the catch logbooks of the fishermen. However, this undermines efforts to effectively manage fisheries and has given rise to new strategies for handling the problem. Currently, machine learning and artificial intelligence are being used to enhance the vast amount of images into more useful “big data.” Big Data comprises customer transaction records, production databases, web traffic logs, automation, satellites, sensors and IoT.
Benefits for seafood consumers:
Conclusion:
Although computer-controlled fisheries have come a long way largely owing to big data and artificial intelligence, there is still a long way to go before they are fully automated. However, investing fully in AI and automation will enable us to produce significantly more seafood to feed the world’s expanding population while also lowering our environmental impact and costs. Complete automation is not yet possible, despite the development of AI. Researchers are developing technology that can run without any input from humans. With nearly 95% accuracy in operations, AI aquaculture farms can be managed and maintained much more easily. If AI is applied correctly, the production of aquaculture products can rise quickly.
Author: Sakshi Gupta