We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Implementing AI in Ocean Waste Tracking and Management
Summary
This review paper looks at how artificial intelligence, like satellite image analysis, can help spot and clean up ocean pollution such as plastic waste and oil spills more efficiently than traditional methods. This matters because ocean plastic breaks down into microplastics that enter the seafood we eat and even our drinking water, so better detection and cleanup could mean less of this pollution ending up in our bodies. The authors note that AI isn't a perfect fix yet, it requires huge amounts of data and has its own environmental costs, like energy use, so more research and teamwork across science, government, and industry are still needed.
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.