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Ecosense: A Youth-Driven IOT and AI-Based Digital Platform for Real-Time Environmental Monitoring and Plastic Waste Detection
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Researchers proposed EcoSense, a youth-developed platform combining IoT environmental sensors, computer vision, and cloud analytics to detect plastic waste in real time and generate location-specific alerts, aiming to bridge monitoring gaps in ecologically sensitive and under-resourced regions.
Environmental degradation, particularly due to plastic pollution, has emerged as a major global concern, especially in ecologically sensitive and remote regions where monitoring systems are inadequate. This study proposes EcoSense, a youth-driven digital platform that integrates Internet of Things (IoT) sensors, computer vision, and cloud computing to enable real-time environmental monitoring and plastic waste detection. The platform combines sensor-based environmental data collection with AI-enabled image processing to identify plastic waste and generate timely alerts. The system architecture includes smart sensors, camera modules, IoT gateways, and a cloud-based analytics dashboard. The proposed model aims to enhance monitoring efficiency, enable early detection of environmental risks, and promote community engagement in climate action. The study highlights the potential of digital technologies in empowering youth and improving environmental governance through scalable, real-time solutions.
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Microplastics Detection in Soil and Water: Leveraging IoT Technologies for Environmental Sustainability
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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.
Golden Seal Project: An IoT-Driven Framework for Marine Litter Monitoring and Public Engagement in Tourist Areas
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Researchers developed the Golden Seal project, an IoT-driven framework that deploys sensor networks and gamified recycling systems in coastal tourist areas to monitor and reduce marine litter while increasing public engagement with plastic pollution issues.
Garbage Watch AI
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Researchers developed a computer vision prototype called 'Garbage Watch AI' to detect floating plastic trash in freshwater environments using cameras and machine learning. The system aims to provide continuous, open-access data on plastic sources and pathways in rivers and streams. It is a technology development paper rather than an environmental monitoring study.
Real-time detection and monitoring of public littering behavior using deep learning for a sustainable environment
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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.
Real-time detection of microplastics in aquatic environments using emerging technologies
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Researchers proposed a real-time microplastic detection system combining AI-enhanced optical sensors and IoT devices, capable of automatically classifying microplastics in ocean water without the time-consuming manual steps required by spectroscopy or microscopy.
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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.