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Leveraging Artificial Intelligence for Plastic Waste Management in Coastal Waters: A Drone-Based Approach for Identification and Hotspot Detection
Summary
A machine learning model trained on drone footage achieved 89.4% accuracy in classifying plastic debris types in coastal waters and identifying hotspot areas for targeted cleanup operations. The AI-driven approach offers faster and more scalable monitoring than traditional methods, though detection of small microplastics and environmental variability remain challenges.
Plastic pollution in coastal waters is one of the most significant environmental issues facing marine ecosystems today. The accumulation of plastic waste in coastal regions not only disrupts marine life but also affects biodiversity and human health. This study explores the potential of artificial intelligence (AI) in addressing this issue by leveraging drone footage to monitor and manage plastic waste. We developed a machine learning model to automatically identify and classify various types of plastic debris, such as bottles, bags, and microplastics, and used it to detect debris hotspots in coastal regions. The model, trained on a diverse dataset of high-resolution drone images, achieved an accuracy of 89.4%, with strong performance in detecting larger plastic items. By clustering debris into high-concentration areas, the system provides targeted guidance for cleanup operations, improving resource allocation. Compared to traditional methods, the AI-driven approach offers faster, scalable, and more precise monitoring, allowing for continuous surveillance over large, hard-to-reach areas. While challenges remain, particularly in detecting microplastics and addressing environmental variability, the results highlight the potential of AI in enhancing plastic waste management and guiding more efficient conservation efforts. Future research will focus on refining the model’s robustness, integrating real-time monitoring, and exploring the use of AI for autonomous cleanup operations.