AI in fishing involves the use of technologies like computer vision, machine learning, and data analytics to enhance fishery management and practices. It aids in fish stock assessment, identification of fishing zones, optimizing catch rates, and minimizing bycatch, contributing to sustainable fishing practices and resource conservation.
A new study led by the Wildlife Conservation Society shows that AI and machine learning can predict coral reef futures with greater accuracy and optimism than traditional climate models. By integrating decades of field data, the models reveal that local management of fishing, pollution, and coastal impacts can foster reef resilience even under global warming.
Aaron Courville, incoming director of IVADO and co-founder of Mila, champions an optimistic and pragmatic vision for artificial intelligence, emphasizing its potential to solve societal challenges. Rather than fearing AI, Courville advocates for responsible, collaborative development to maximize its benefits across fields from climate action to public policy.
Researchers at Cornell University have developed a machine learning-based method using underwater microphones to estimate North Atlantic Right whale numbers, offering a safer and cost-effective alternative to aerial surveys.
Researchers at Rutgers University-New Brunswick have developed an AI tool that predicts endangered whale habitats, guiding ships to avoid deadly collisions and supporting marine conservation efforts.
Researchers developed a real-time underwater video processing system leveraging object detection models and edge computing to count Nephrops in demersal trawl fisheries. Through meticulous experimentation, optimal configurations balancing processing speed and accuracy were identified, highlighting the potential for enhanced sustainability through informed catch monitoring.
Study by Global Fishing Watch and partners, using machine learning and satellite imagery, reveals 75% of the world's industrial fishing vessels are untracked, highlighting extensive "dark" ocean activity, including in Africa and South Asia.
This review explores the applications of artificial intelligence (AI) in studying fishing fleet (FV) behavior, emphasizing the role of AI in monitoring and managing fisheries. The paper discusses data sources for FV behavior research, AI techniques used in monitoring FV behavior, and the uses of AI in identifying vessel types, forecasting fishery resources, and analyzing fishing density.
Researchers have introduced a groundbreaking Full Stage Auxiliary (FSA) network detector, leveraging auxiliary focal loss and advanced attention mechanisms, to significantly improve the accuracy of detecting marine debris and submarine garbage in challenging underwater environments. This innovative approach holds promise for more effective pollution control and recycling efforts in our oceans.
Developed by the University of Southampton, the MARLIN AI system monitors underwater activities, enhancing protection for marine ecosystems and mammals.
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