Machine Learning for Sustainable Fisheries Management and Ocean Conservation
Abstract
Overfishing and marine ecosystem degradation pose severe threats to global biodiversity and food security. This paper explores the use of machine learning in sustainable fisheries management, focusing on fish stock assessment, illegal fishing detection, and habitat monitoring. AI models, including convolutional neural networks (CNNs) and deep reinforcement learning, are applied to analyze satellite imagery, sonar data, and ecological trends. Case studies showcase AI-driven initiatives in marine conservation, emphasizing the role of machine learning in ensuring sustainable fisheries and protecting ocean ecosystems.
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