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.
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.
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