We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Papers
61,005 resultsShowing papers similar to Autonomous Waste Classification Using Multi-Agent Systems and Blockchain: A Low-Cost Intelligent Approach
ClearArtificial intelligence for waste management in smart cities: a review
Researchers reviewed how artificial intelligence (AI) is being applied to nearly every aspect of waste management, from sorting recyclables with up to 99.95% accuracy to cutting transportation costs by over 36%. Their findings show AI could dramatically improve how cities handle plastic and other waste, reducing pollution and public health burdens.
A Smart Garbage Classification based on Deep Learning
Researchers developed an AI-powered garbage classification system using deep learning to automatically sort waste categories. Accurate automated waste sorting could improve plastic recycling rates, reducing the amount of plastic that eventually breaks down into environmental microplastics.
Source separation, transportation, pretreatment, and valorization of municipal solid waste: a critical review
Researchers reviewed the full chain of municipal solid waste management — from source separation through collection, pretreatment, and valorization — finding that AI and the Internet of Things are emerging as powerful tools for optimizing collection routing and sorting efficiency within circular waste management systems.
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.
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.
Indonesian Waste Database: Smart Mechatronics System
This study developed a mechatronic waste sorting robot paired with a smart database of Indonesian waste types, covering six categories including plastics. Automated waste classification technology can improve recycling rates and reduce the plastic that ends up in environments where it breaks down into microplastics.
Design and Fabrication of Material Separation Machine for Sustainable Development
This paper is not relevant to microplastics research — it describes the design and fabrication of a robotic material separation machine intended to sort recyclable waste more efficiently using AI-inspired engineering principles.
Enhancing Waste Management with a Deep Learning-based Automatic Garbage Classifier
This paper is not about microplastics; it presents a deep learning convolutional neural network system for automatically classifying garbage by material type to improve waste sorting efficiency and reduce the labor burden of manual waste management.
Blockchain Technology for Sustainable Waste Management
This paper examines how blockchain technology could improve transparency and traceability in waste management systems, helping ensure that materials are actually recycled rather than sent to landfill. Improved waste tracking is relevant to reducing the plastic waste that ultimately degrades into microplastics in the environment.
Multi-Modal Autonomous Aerial Waste Collection and Categorization System: Integrating Computer Vision, Ultrasonic Material Classification, and Intelligent Sorting for Sustainable Environmental Management
Researchers proposed a multi-modal autonomous aerial waste collection system integrating computer vision with ultrasonic material recognition to overcome the 92-95% detection accuracy ceiling of vision-only drone systems. The system combines visual feature extraction and acoustic signature analysis to differentiate and selectively sort waste materials in real-time, enabling targeted collection into dedicated recycling compartments.
Application of AI-Enabled Computer Vision Technology for Segregation of Industrial Plastic Wastes
Researchers developed an AI-powered computer vision system to segregate mixed industrial plastic wastes by polymer type, addressing a key barrier to effective plastic recycling. The system achieved high classification accuracy across common plastic categories, demonstrating that machine vision can improve sorting efficiency and recycled plastic quality.
Efficient Plastic Recycling and Remolding Circular Economy Using the Technology of Trust–Blockchain
This paper proposed integrating blockchain technology into plastic waste recycling supply chains to improve collection efficiency, tracking, and stakeholder coordination, arguing that unique digital identifiers assigned to plastic items can enable transparent, automated sorting and accountability throughout the plastic lifecycle from manufacturer to recycler.
An Automatic Garbage Classification System Based on Deep Learning
Researchers developed an automated garbage classification system using a deep learning algorithm based on ResNet-34, achieving 99% classification accuracy with a processing time of under one second per item. Automated waste sorting technology like this could improve the efficiency of plastic waste recovery and reduce mismanaged plastic that eventually becomes environmental pollution.
A Machine Arm to Assist in Trash Sorting using machine Learning and Object Detection
Not relevant to microplastics — this paper describes a robotic arm system that uses machine learning and computer vision to sort recyclable waste materials, focused on automation of waste sorting processes.
Artificial Intelligence-Based Robotic Technique for Reusable Waste Materials
This paper describes an AI-based robotic arm system that uses a customized deep learning model to classify and sort waste materials including plastics and cartons by material type for automated recycling. The integrated system combines gripping, motion control, and AI-driven material classification into a full-automation architecture for waste recovery.
Smart Ocean Cleanup: An AI-Integrated Autonomous System for Marine Waste Management
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.
FindingPlastic: Underwater Plastic Bag Detection and Retrieval
Engineers developed an automated system using artificial intelligence to detect, track, and capture floating plastic bags underwater before they break down into microplastics. The system combines modern object detection and tracking algorithms and was successfully tested in a tank environment, offering a potential tool for robotic ocean cleanup efforts.
GRUBin: Time-Series Forecasting-Based Efficient Garbage Monitoring and Management System for Smart Cities
Researchers developed GRUBin, a smart waste monitoring system using time-series forecasting with GRU neural networks to predict bin fill levels and optimize collection schedules, outperforming IoT-only approaches in reducing unnecessary waste collection trips in smart city 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.
A Systematic Review of Solid Waste Management (SWM) and Artificial Intelligence approach
This systematic review found that artificial intelligence and machine learning are increasingly being applied to solid waste management for tasks like waste classification, collection route optimization, and landfill monitoring. AI-based approaches showed significant improvements over traditional methods in sorting accuracy and operational efficiency.
Towards Accessible Aquatic Cleanup: A Low-Cost Solution for Floating Waste Extraction
Researchers designed and tested a low-cost autonomous floating waste extractor using a conveyor mechanism to capture lightweight surface pollutants including microplastics, demonstrating high efficiency in capturing debris and offering an affordable solution for resource-constrained settings.
Enhancing Decentralised Recycling Solutions with Digital Technologies
This paper is not about microplastics; it reviews how digital technologies can enhance decentralised plastic recycling systems, particularly in the African context.
Development of IoT-based Smart Recycling Machine to collect the wasted Non-woven Fabric Face Mask (NFM)
This study developed an IoT-connected smart recycling machine designed to collect used non-woven face masks and prevent them from entering the environment as microplastic waste. Single-use masks became a major source of microplastic pollution during the COVID-19 pandemic, and automated collection systems could significantly reduce this form of plastic waste.
Location-aware hazardous litter management for smart emergency governance in urban eco-cyber-physical systems
Researchers proposed an autonomous framework for managing littered face masks in smart cities, combining a deep neural network trained on a novel dataset — achieving 96% detection accuracy at ten times the speed of comparable models — with location intelligence to predict high-risk litter zones and guide emergency response.