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Implementing AI in Ocean Waste Tracking and Management
Summary
This review paper looks at how artificial intelligence, like satellite image analysis and smart tracking systems, can help spot and clean up ocean pollution, including plastic waste and oil spills, faster and more accurately than older methods. This matters because ocean plastic breaks down into microplastics that end up in seafood and drinking water, so better cleanup technology could mean less of that pollution making its way into our bodies. That said, this is a summary of existing studies rather than new experiments, and the authors note that AI tools still need more data, cross-team collaboration, and attention to their own environmental costs before they can fully deliver on this promise.
Marine pollution has emerged as a significant environmental challenge, with millions of tons of waste flowing intooceans each year, causing disruptions in marine ecosystems. Traditional methods for monitoring and addressing thispollution are inadequate in dealing with its ineffable complexity. Researchers are thoroughly investigating the potentialof artificial intelligence (AI) as a revolutionary tool for tracking and reducing ocean pollution. This literature reviewexplores the application of cutting-edge technology in tracking and mitigating marine pollution, such as plastic waste,oil spills, and wastewater contamination. A comprehensive review of recent research was performed, concentrating ontechniques that employ advanced technology for the detection, forecasting, and elimination of marine debris. Researchshows that advanced computer vision and machine learning techniques significantly boost the efficiency and precisionof pollution detection, such as recognizing plastic waste through satellite images, and improving clean-up strategies bydirecting collection vessels for maximum effectiveness. Efforts are underway to form partnerships among governmententities, industry players, and academic scholars to advance these data-centric solutions. However, obstacles persist;AI systems typically demand significant amounts of data and are subject to time limitations, and it is essential toconsider the environmental impacts of AI deployment, including energy use and electronic waste. This paper bringstogether current applications, assesses their effectiveness and limitations, and highlights gaps in the existing research.The ability of AI to transform ocean waste management is substantial; however, achieving its complete potentialnecessitates collaboration across disciplines, strict data governance, and thoughtful attention to sustainability in AIresearch.