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Papers
61,005 resultsShowing papers similar to Golden Seal Project: An IoT-Driven Framework for Marine Litter Monitoring and Public Engagement in Tourist Areas
ClearA Mobile Application to Assist in Reporting and Cleaning Spots of Ocean Litters using Machine Learning
Researchers developed a mobile application that uses machine learning to help users report and locate ocean litter, aiming to improve community-driven cleanup efforts and generate spatial data on marine plastic pollution.
Microplastics Detection in Soil and Water: Leveraging IoT Technologies for Environmental Sustainability
Researchers explored the integration of IoT sensor technologies for detecting and monitoring microplastics in soil and water environments, proposing a connected sensing framework for real-time environmental surveillance. The system enables automated data collection and remote monitoring of microplastic contamination.
The Smart Drifter Cluster: Monitoring Sea Currents and Marine Litter Transport Using Consumer IoT Technologies
Researchers introduced a Smart Drifter Cluster concept — low-cost IoT-enabled floating sensors — to track ocean currents and monitor the transport of marine litter and plastic debris. This technology could provide real-time data on microplastic distribution across coastal and open ocean environments.
Smart and Sustainable Technological Framework for Microplastic Pollution Mitigation
Researchers proposed a smart technological framework for microplastic pollution mitigation that integrates IoT-based monitoring, machine learning analytics, and eco-friendly remediation technologies. The system uses low-power sensors for continuous detection of microplastic contamination and sustainable filtration mechanisms with biodegradable adsorbent materials for cleanup. The framework emphasizes modular design and renewable energy integration to support long-term deployment across diverse aquatic environments.
Ro-Boat: IoT-Based Non-Autonomous Garbage Collector Boat for Organic, Metal, and Non-Metal Waste
Researchers developed Ro-Boat, an IoT-based non-autonomous garbage-collecting vessel designed to remove organic, metal, and non-metal waste from rivers, lakes, and coastal waters. The prototype uses sensor-based waste detection and optimised collection mechanisms to address aquatic plastic and debris pollution in operational water body environments.
Marine Litter Tracking System: A Case Study with Open-Source Technology and a Citizen Science-Based Approach
Researchers deployed GPS-tracked drifter devices in the Arno River using open-source hardware and citizen science approaches to track how plastic litter moves through river systems toward the ocean, providing empirical data on plastic transport dynamics that can improve models of river-to-ocean plastic flux.
Smart Bin and IoT: A Sustainable Future for Waste Management System in Nigeria
Researchers proposed a smart waste bin system using Internet of Things technology to improve waste management in Nigerian cities. The system uses sensors and Wi-Fi connectivity to monitor bin fill levels remotely, enabling more efficient waste collection routes. The study highlights how affordable IoT-based solutions could help developing nations reduce plastic waste accumulation and environmental pollution.
Real-time detection and monitoring of public littering behavior using deep learning for a sustainable environment
Researchers developed an AI-powered surveillance system called SAWN that uses video cameras and deep learning models to detect public littering by vehicles and pedestrians in real time, achieving up to 99.5% accuracy — offering a scalable tool to reduce plastic pollution at its source.
Towards an IOT Based System for Detection and Monitoring of Microplastics in Aquatic Environments
This paper proposes using Internet of Things (IoT) sensors to build a real-time monitoring network for microplastics in aquatic environments. Automated, continuous monitoring systems could provide much better spatial and temporal coverage than current sampling-based approaches.
Ecología robótica desde el litoral: resultados de un programa fortalecedor de las habilidades para la ciencia
A STEAM-based robotics program was used with students on coastal beaches to study and address the problem of shoreline plastic waste. The program combined science education with hands-on environmental monitoring, demonstrating that educational robotics can help raise awareness about marine litter among young people.
Development of Drifting Debris Detection System using Deep Learning on Coastal Cleanup
Researchers developed a deep learning-based system to detect litter on beaches using images and automated object recognition. Efficient litter detection tools could help coastal cleanup programs identify and remove plastic debris before it breaks down into microplastics.
Design and Development of Smart Beach Debris Collection and Segregation System
Researchers designed and built a smart automated system for collecting and segregating beach debris, using sensors and robotics to identify and sort plastic waste from natural material on shorelines. The system demonstrated effective separation of plastic debris in field tests.
Citizen Science Protocol for beach plastic monitoring using aerial drones
Researchers developed a citizen science protocol using aerial drones to monitor plastic pollution on beaches. The study outlines systematic methods for community-based beach surveys to track the accumulation of plastic debris, from large items to microplastics, supporting environmental monitoring efforts along coastal areas.
Detection and assessment of marine litter in an uninhabited island, Arabian Gulf: A case study with conventional and machine learning approaches
Researchers surveyed marine litter on a remote Arabian Gulf island after a large cleanup, then trained a YOLO-v5 deep learning model on 10,400 beach images to automatically detect debris, achieving 90% detection accuracy and demonstrating that windward shores accumulate significantly more litter from neighboring countries.
A Proposed Technology Solution for Preventing Marine Littering Based on Uavs and Iot Cloud-based Data Analytics
This paper proposes a technological solution using unmanned aerial vehicles and automated collection systems to prevent marine littering at coastal hotspots. The approach aims to intercept plastic waste before it enters the ocean and breaks down into microplastics.
Global assessment of innovative solutions to tackle marine litter
Researchers reviewed 177 validated innovative solutions for preventing, monitoring, and cleaning marine litter from a global search, finding that most focused on monitoring rather than prevention, few had reached commercial maturity, and none had been validated for environmental impact or efficiency.