Optimizing Fisheries Management with Artificial Intelligence
The integration of artificial intelligence (AI) in the maritime industry, particularly in fisheries management, is transforming the way fishing activities are monitored, regulated, and sustained. This technological advancement is addressing long-standing challenges such as overfishing, illegal fishing practices, and the inefficiencies in data collection and analysis.
The Need for AI in Fisheries
Fisheries management has historically been plagued by issues such as unwanted catches, the difficulty in monitoring fishing activities, and the high costs associated with manual data collection and reporting. For instance, in the European Union, fishers are required to register all catches and bring them to shore, a process that is time-consuming, costly, and often inefficient[1].
AI-Based Solutions
To tackle these challenges, researchers and the fishing industry have collaborated to develop AI-based tools. One notable example is the Fully Documented Fisheries (FDF) project, funded by the EU and led by Wageningen University and Research (WUR) in the Netherlands. This project has developed an AI tool that automatically recognizes the size and species of each fish on board vessels. This is achieved through a video camera system that records fish as they move along a conveyor belt, and an on-board AI unit that decodes these video images to identify and record the species and size of each fish[1].
This technology not only facilitates the handling of fish and the recording of catches but also provides valuable data for fisheries management. It helps in distinguishing between catches intended for human consumption and unwanted catches, such as those below the minimum size limit. This selective fishing approach improves the working conditions for the crew and reduces the administrative burden associated with manual sorting and reporting[1].
Expanding the Scope of AI in Fisheries
The FDF project is just the beginning of a broader initiative to integrate AI into various aspects of fisheries management. The project has been expanded to include other fleet segments and other European countries, such as Denmark and Belgium. This expansion aims to make the European fishing sector more sustainable by enhancing transparency, improving data quality, and promoting more selective fishing practices[1].
Advanced Data Collection and Analysis
AI is not just limited to on-board vessel monitoring; it also plays a crucial role in analyzing vast amounts of data generated from various sources. For example, AI can combine data from location tracking devices, satellite imagery, and vessel transponders to provide a comprehensive picture of fishing vessel movements. This integrated approach helps in monitoring fishing activities in real-time, which is essential for managing data-poor or unassessed fish populations that account for up to 80% of the world’s fisheries[2].
Remote Electronic Monitoring (REM)
Remote Electronic Monitoring (REM) is another significant application of AI in fisheries management. REM systems installed on vessels collect data on fishing activities, but the review and interpretation of this data can be time-consuming and labor-intensive. AI and machine learning algorithms can automate this process, reducing the need for human reviewers and significantly cutting down the time and cost associated with data analysis[5].
For instance, the UK's Centre for Environment, Fisheries and Aquaculture Science (Cefas) has been working on developing AI solutions for REM data interpretation. This involves collecting and annotating images of fish from research vessels to train AI algorithms. These algorithms can then be used to identify and measure individual fish, track discards, and record interactions with protected species, even in chaotic and unpredictable fishing environments[5].
Commercial and Conservation Impacts
Recommended by LinkedIn
The integration of AI in fisheries management has far-reaching commercial and conservation benefits. OnDeck Fisheries AI, for example, provides a cloud analytics platform that automates the review and analysis of video footage from fishing vessels. This platform enables real-time insights into fishing activities, greatly enhancing the understanding of ecosystem health and fishing practices. It also helps fishermen make more informed decisions, reduce fuel consumption, and ensure the traceability of their seafood[4].
AI can also play a critical role in combating overfishing and illegal fishing. By automatically detecting instances of illegal fishing or overfishing, such as exceeding catch limits or fishing endangered species, AI systems can help enforce sustainable fishing practices. This aligns with global goals, such as the United Nations' Sustainable Development Goal 14 (Life Below Water), which aims to conserve and sustainably use the world's oceans[4].
Challenges and Future Directions
Despite the promising advancements, there are several challenges to overcome. One of the key limitations is the availability and quality of training data. AI algorithms require extensive and high-quality data to be effective, especially in the complex and often messy environment of commercial fishing vessels. Initiatives such as the SMARTFISH and EVERYFISH projects, funded by Horizon 2020 and Horizon Europe, are working to address this issue by collecting and annotating real-world data from fishing vessels[5].
Another challenge is the need for novel collaborations and partnerships across the fisheries community. This includes government managers, AI developers, fishers, and vessel owners, all of whom must work together to share data and methods, encourage new entrants to the field, and increase technical literacy. A common vocabulary for policy and technical concepts is essential for ensuring that AI systems meet the diverse needs of the fisheries sector[2].
Conclusion
The integration of AI in fisheries management is a transformative step towards achieving sustainable and healthy fishing sectors. By automating data collection, improving the accuracy of catch reporting, and enhancing the selectivity of fishing practices, AI is addressing some of the most pressing challenges in the industry. As the technology continues to evolve, it is likely to play an increasingly critical role in ensuring the long-term sustainability of global fisheries.
References