Using AI for Sustainable Fisheries and Marine Biodiversity Conservation

Abstract

Overfishing and marine biodiversity loss threaten global food security and ecosystem stability. This paper investigates the role of machine learning in sustainable fisheries management, focusing on AI-powered fish population assessments, illegal fishing detection, and ocean health monitoring. Techniques such as deep learning, image recognition, and sonar-based AI models are utilized to track fish stocks, predict migration patterns, and optimize sustainable fishing practices. Case studies highlight AI applications in marine conservation efforts, demonstrating their effectiveness in maintaining ecological balance and preventing species depletion.