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Papers
61,005 resultsShowing papers similar to An Artificial Intelligence based Optical Sensor for Microplastic Detection in Seawater
ClearDetecting Microplastics in Seawater with a Novel Optical Sensor Based on Artificial Intelligence Models
Detecting microplastics in seawater quickly and accurately is a major technical challenge, and this study developed a novel optical sensor that uses artificial intelligence to identify plastic particles from light-scattering data in real time. The AI-powered system was tested on seawater samples and showed promising accuracy for classifying microplastic types without the need for time-consuming laboratory processing. Automated in-situ sensors like this could enable continuous, large-scale ocean monitoring for microplastic pollution.
Real-time detection of microplastics in aquatic environments using emerging technologies
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.
Automatic Identification and Classification of Marine Microplastic Pollution Based on Deep Learning and Spectral Imaging Technology
Researchers developed an AI system combining deep learning with multispectral imaging to automatically identify and classify marine microplastics, using a feature-selection method called ReliefF to reduce noise in complex ocean samples. The approach achieved high accuracy and offers a scalable solution for large-scale ocean microplastic monitoring that outperforms traditional manual inspection.
Design and Method Research of Intelligent Detection System for Marine Microplastics Driven by Microfluidic Chip
Researchers designed an intelligent detection system for marine microplastics using a microfluidic chip combined with machine learning image analysis. Simulation testing validated the chip's ability to capture and sort microplastic particles from seawater samples, with AI classification achieving high accuracy across particle types.
The Development of Sensors for Microplastic Detection Using Artificial Intelligence
This review examined AI-enhanced sensors developed for microplastic detection and characterization in aquatic environments, covering machine learning, deep learning, and spectroscopic sensor approaches. The authors found that AI substantially reduces the labor intensity of microplastic identification and improves detection of small particles, though training dataset standardization and real-world validation remain priority challenges.
Toward Nano- and Microplastic Sensors: Identification of Nano- and Microplastic Particles via Artificial Intelligence Combined with a Plasmonic Probe Functionalized with an Estrogen Receptor
Scientists created a sensor that combines artificial intelligence with a specialized light-based probe to detect and identify different types of nano- and microplastics in water. The AI-powered system could distinguish between various plastic types with high accuracy, offering a faster and more practical way to monitor plastic contamination in drinking water and environmental samples.
Optical System for In-situ Detection of Microplastics
Researchers developed a portable optical system capable of detecting, identifying, continuously monitoring, and quantifying microplastics in situ at natural water bodies. The system uses optical techniques to observe the temporal behavior of microplastic concentrations at fixed locations, enabling real-time environmental monitoring without sample collection and laboratory processing.
Real-Time Detection of Microplastics Using an AI Camera
Researchers developed a camera-based system using artificial intelligence to detect and measure microplastics in real time as they move through water. The system was tested with three different camera setups and could identify particles, measure their size, and track their speed. This technology could provide a faster and more practical alternative to the labor-intensive laboratory methods currently used to monitor microplastic pollution.
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.
IoT-Driven Image Recognition for Microplastic Analysis in Water Systems using Convolutional Neural Networks
Researchers developed an IoT-based system using artificial intelligence to automatically detect and count microplastics in water samples through image recognition. The system uses cameras at distributed sensor points to continuously monitor waterways and can identify microplastics of different sizes, shapes, and colors. This technology could improve environmental monitoring of microplastic pollution in real time, helping communities and agencies respond faster to contamination threats in drinking water sources.
IoT-Integrated Image Recognition System for Microplastic Detection and Classification
Researchers developed an IoT-based system that combines a microscopic camera with a YOLOv8 deep learning model to detect and classify microplastics in real time, including types like LDPE, PE, PHA, and PS. The system achieves high accuracy across diverse environmental conditions and visualizes data through a cloud-based dashboard. This scalable approach offers a practical tool for monitoring microplastic pollution, with potential for future integration on marine vessels.
Development of an Iot-Integrated AI System for Microplastic Detection in Water Samples
Researchers developed an IoT-integrated AI system using high-resolution microscopy, a Raspberry Pi platform, and machine learning to detect and classify microplastic particles in water samples in real time via MQTT, achieving detection accuracy exceeding 95% in simulated dataset validation.
Microplastic Identification in Seawater using Generative Adversarial Networks
Researchers trained a generative adversarial network (GAN) on microscope images of seawater samples and achieved 92.5% accuracy in automatically distinguishing microplastic particles from natural particulates. This AI-based detection approach could dramatically speed up the analysis of water samples, making routine monitoring of marine microplastic pollution faster and more scalable.
Deep Learning-Based Image Recognition System for Automated Microplastic Detection and Water Pollution Monitoring
This study developed a deep learning image recognition system to automate the detection and classification of microplastics from microscopy images of water samples. The system achieved high accuracy across particle types and sizes, offering a scalable and less labor-intensive alternative to manual microscopy for large-scale water pollution monitoring.
Optimized Classification of Suspended Particles in Seawater by Dense Sampling of Polarized Light Pulses
Researchers developed an optical method using polarized light pulses to classify suspended particles in seawater, aiming to distinguish microplastics from natural particles like algae in situ. A reliable in-water optical sensor for microplastics would greatly improve environmental monitoring capability.
Deep Learning Approaches for Detection and Classification of Microplastics in Water for Clean Water Management
Researchers applied dual deep learning models (YOLOv8, YOLOv11, and several CNN architectures) to detect and classify microplastics in water, finding that these AI approaches could accurately identify plastic types across both aquatic and non-aquatic datasets.
Automatic Detection of Microplastics in the Aqueous Environment
Researchers developed a deep-learning system for real-time detection and counting of microplastics in freshwater, achieving high accuracy for particles 1 mm and larger.
An Image Analysis of Coastal Debris Detection -Detection of microplastics using deep learning-
Researchers developed a deep learning-based coastal debris detection system using YOLOv7 and the SAHI vision library to identify microplastics in image data collected from shorelines. The system demonstrated effective detection performance and offers a scalable approach for automated monitoring of microplastic litter in coastal environments.
Particle and salinity sensing for the marine environment via deep learning using a Raspberry Pi
Researchers applied deep learning to analyze light scattering patterns from mixed particles in ocean water, enabling automated identification of different particle types including sediment and biological material. This technology could be adapted to detect and classify microplastics in marine environments alongside natural particles.
GoogLeNet-Based Deep Learning Framework for Underwater Microplastic Classification in Marine Environments
Researchers trained a GoogLeNet deep learning model on underwater images to classify microplastics into four categories, achieving strong classification performance for primary microplastics, secondary microplastics, non-microplastic debris, and marine biota in turbid coastal waters.
Portable On-Site Optical Detection and Quantification of Microplastics
Researchers built a portable, on-site optical device to detect and quantify microplastics in water. The device addresses the challenge of detecting small, often translucent particles without a laboratory setting. Portable microplastic detection tools could enable real-time monitoring in the field, supporting faster environmental assessments.
The Identification of Spherical Engineered Microplastics and Microalgae by Micro-hyperspectral Imaging
Scientists used hyperspectral imaging combined with machine learning to distinguish between microplastic particles and microalgae in seawater samples. Developing reliable automated methods for identifying microplastics in complex environmental samples is critical for accurate contamination monitoring.
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.
Detection of Microplastics Based on a Liquid–Solid Triboelectric Nanogenerator and a Deep Learning Method
Scientists developed a new microplastic detection device based on a liquid-solid friction generator combined with deep learning AI to identify different types of plastic particles. The system can classify microplastics by material type with high accuracy using electrical signals generated when plastic particles contact a liquid surface. This technology could make it easier and cheaper to monitor microplastic contamination in water supplies.