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Smart Ocean Cleanup: An AI-Integrated Autonomous System for Marine Waste Management
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This paper presents an AI-powered autonomous boat system designed to detect and collect marine pollution — including plastics, oil spills, and microplastics — using deep learning image classification, IoT sensors, and robotic collection mechanisms. The system demonstrated over 94% accuracy for pollutant detection and classification across several AI models. While focused more broadly on ocean cleanup technology than on microplastic science specifically, it demonstrates how AI-integrated robotics could help address the practical challenge of removing plastic waste from ocean surfaces before it breaks down further.
Marine pollution is a critical environmental concern, with plastic waste, oil spills, and industrial pollutants contaminating ocean surfaces. Conventional cleanup methods such as manual collection and mechanical skimmers are inefficient, labor-intensive, and unable to adapt in real time. To overcome these limitations, this research presents Smart Ocean Cleanup: An AI Integrated Autonomous System for Marine Waste Management, a novel framework using IoT enabled sensors, deep learning based pollutant classification, and autonomous robotic cleaning mechanisms for efficient waste removal. The system employs EfficientNetB7, DenseNet, YOLOv8, and CNN LSTM for high precision pollutant detection, classification, and real time monitoring. EfficientNetB7 classifies waste types like plastics and oil spills from captured images, while DenseNet enhances classification accuracy. YOLOv8 detects floating waste and guides the robotic cleaner for effective waste collection. CNN LSTM predicts pollution spikes by analyzing historical sensor data, allowing proactive intervention. IoT sensors including MQ135, Turbidity, pH, DS18B20, GPS, and Ultrasonic sensors continuously monitor ocean conditions and transmit data via LoRaWAN/MQTT to a cloud dashboard for live analysis. The system uses an Arduino microcontroller, with AI inference performed on Jetson Nano for edge processing and AWS SageMaker for cloud based analytics. An AI powered robotic boat fitted with floating nets, oil absorption pads, and a microplastic filtration unit autonomously navigates polluted areas for effective cleanup. Performance evaluation shows EfficientNetB7 achieves 96.11 % accuracy, YOLOv8 95.99 %, and CNN LSTM 94.85 % demonstrating the system's high effectiveness in pollutant detection, classification, and predictive analysis.
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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.
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Millions of tons of plastic enter our oceans every year, breaking down into tiny microplastics that work their way up the food chain and onto our plates. Researchers built an AI tool that scans underwater photos and automatically spots trash with about 82% accuracy, which could help cleanup groups and governments find and target pollution hotspots faster than manual monitoring allows. This kind of tech won't solve ocean plastic pollution on its own, but it's a practical step toward tracking the problem that ultimately threatens seafood safety and human health.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.