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Papers
61,005 resultsShowing papers similar to Microplastic Identification Using AI-Driven Image Segmentation and GAN-Generated Ecological Context
ClearMicroplastic 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.
A Deep Learning Approach for Microplastic Segmentation in Microscopic Images
Researchers developed a deep learning model for automated segmentation and classification of microplastics in microscopic images, identifying five distinct categories including fibers, fragments, spheres, foam, and film. The model achieved high accuracy while maintaining low computational requirements, making it suitable for high-throughput deployment in environmental monitoring. The study offers a tool that could help overcome the measurement bottleneck in microplastic characterization for toxicological and risk assessment studies.
A Critical Review on Artificial Intelligence—Based Microplastics Imaging Technology: Recent Advances, Hot-Spots and Challenges
Researchers reviewed the use of artificial intelligence and machine learning techniques for detecting and identifying microplastics in environmental samples. The study found that AI-based imaging tools can significantly speed up analysis and improve accuracy compared to traditional manual methods. However, challenges remain around standardizing datasets and making these tools accessible for routine environmental monitoring.
Rapid Classification of Microplastics by Using the Application of a Convolutional Neural Network
Researchers used convolutional neural networks (deep learning) to automatically classify microplastic particles in microscopy images into four categories: fragments, pellets, films, and fibers. The models achieved high classification accuracy, reducing the time and labor needed for manual identification. Automated AI classification could greatly accelerate large-scale microplastic monitoring programs.
Proceeding the categorization of microplastics through deep learning-based image segmentation
Researchers developed a deep learning-based image segmentation method using Mask R-CNN to automatically identify and classify microplastic shapes in microscopic images, demonstrating a practical step toward standardized and automated microplastic categorization.
Big Data, Tiny Targets: An Exploratory Study in Machine Learning-enhanced Detection of Microplastic from Filters
Researchers applied machine learning algorithms to microscopy images of microplastics on filter papers, demonstrating that AI-assisted automated detection significantly reduces the manual analysis time required for high-throughput microplastic screening.
Morphological Detection and Classification of Microplastics and Nanoplastics Emerged from Consumer Products by Deep Learning
Researchers created a new open-source dataset of microscopy images for training AI models to automatically detect and classify micro- and nanoplastics. The dataset fills an important gap in available tools for microplastic research and provides a foundation for developing faster, more efficient methods to identify plastic contamination across environmental samples.
Automated micro-plastic detection and classification using deep convolution neural network pre-trained models and transfer learning
Researchers compared several artificial intelligence models for automatically detecting and classifying microplastics into categories like beads, fibers, and fragments from images. While the models performed well at identifying fiber-type microplastics, they struggled with beads and fragments, highlighting the need for better training data and techniques. Improving automated detection is important because it could enable faster, cheaper environmental monitoring of microplastic contamination in water and food sources.
A new approach to classifying polymer type of microplastics based on Faster-RCNN-FPN and spectroscopic imagery under ultraviolet light
Scientists developed an AI-based method using UV light photography to automatically identify and classify different types of microplastics, achieving 86-88% accuracy. This approach is faster and cheaper than traditional lab analysis methods that require expensive equipment. Better detection tools like this are essential for understanding how widespread microplastic contamination really is in coastal environments where people live and eat seafood.
Microplastic Binary Segmentation with Resolution Fusion and Large Convolution Kernels
Researchers developed an improved machine-learning model to automatically detect and segment microplastic particles in images, achieving better accuracy than previous approaches by combining multi-resolution image analysis with large convolution kernels. Reliable automated detection tools are essential for scaling up microplastic monitoring, since manual identification is too slow and inconsistent for the volumes of environmental samples that need to be processed.
GANsemble for Small and Imbalanced Data Sets: A Baseline for Synthetic Microplastics Data
Researchers developed a generative adversarial network (GAN) ensemble approach to generate synthetic training data for microplastic image classification, addressing the challenge that real microplastic image datasets are small and imbalanced by polymer type. The synthetic augmentation improved classifier accuracy and recall, particularly for underrepresented plastic categories.
Identification of microplastics using a convolutional neural network based on micro-Raman spectroscopy
Researchers combined micro-Raman spectroscopy with a neural network to identify microplastics, achieving over 99% accuracy across 10 different plastic types. The system was also tested on real environmental samples and performed well at classifying unknown particles. This AI-powered approach could make microplastic identification faster and more reliable for environmental monitoring.
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.
An Artificial Intelligence based Optical Sensor for Microplastic Detection in Seawater
Researchers developed an AI-based optical sensor system combining an optical detection subsystem and an image acquisition subsystem to detect and identify microplastic particles in seawater, distinguishing them from naturally occurring marine particles. The device applies AI algorithms to analyze consecutive image frames and classify particles as microplastic or non-microplastic, with the full system housed in two portable cases.
Deep learning-powered efficient characterization and quantification of microplastics
Researchers developed an artificial intelligence framework that uses deep learning to automatically identify and quantify microplastics from infrared spectra and visual images. The system achieved high accuracy in classifying plastic types and counting particles, dramatically reducing the time needed compared to manual analysis. This tool could make large-scale microplastic monitoring faster and more consistent across different research laboratories.
Intelligent classification and pollution characteristics analysis of microplastics in urban surface waters using YNet
Researchers developed an AI-based system called YNet to automatically identify and classify microplastics in urban water samples from their visual appearance. The system achieved over 90% accuracy in distinguishing different microplastic shapes and was used to analyze pollution patterns in wetlands and reservoirs. The study demonstrates that artificial intelligence can make microplastic monitoring faster and more consistent compared to traditional manual identification methods.
Automatic classification of microplastics and natural organic matter mixtures using a deep learning model
Researchers developed a deep learning model using a convolutional neural network with spatial attention to classify microplastics mixed with natural organic matter from Raman spectra. The model achieved 99.54% accuracy compared to just 31.44% from conventional spectral library software, demonstrating that AI-based approaches can dramatically improve microplastic identification accuracy while reducing the need for time-intensive preprocessing steps.
Artificial intelligence in microplastics domain: Current progress, challenges, and sustainable prospects
This critical review assesses how artificial intelligence tools—including machine learning and image recognition—are being applied to detect, characterize, and predict the behavior of microplastics in the environment. AI approaches show promise for overcoming persistent bottlenecks in large-scale microplastic analysis, but the authors highlight challenges around data quality, model interpretability, and standardization that must be addressed for these tools to reach their potential.
Detection of Microplastics Using Machine Learning
Researchers reviewed and demonstrated machine learning approaches for detecting and classifying microplastics in environmental samples, finding that automated image analysis and spectral classification methods can improve the speed and accuracy of microplastic monitoring compared to manual methods.
GANsemble for Small and Imbalanced Data Sets : A Baseline for Synthetic Microplastics Data
Researchers applied a generative adversarial network ensemble approach to address the problem of small and imbalanced microplastic datasets in machine learning, producing synthetic data to improve model training for plastic particle classification.
Quantitative image analysis of microplastics in bottled water using Artificial Intelligence
Researchers developed an artificial intelligence-based image analysis approach to quantitatively detect microplastics in bottled water, addressing the absence of validated analytical methods and enabling more reliable assessment of consumer exposure to microplastic contamination.
The Role of Artificial Intelligence in Microplastic Pollution Studies and Management
This review explores how artificial intelligence is transforming microplastic research, from automating detection in microscopy images and spectral analysis to predicting how plastics interact with pollutants and living organisms. AI-powered sensors and real-time monitoring systems are also being integrated into wastewater treatment to reduce microplastic release, making the technology a powerful tool for both understanding and managing plastic pollution.
Neural Network Analysis for Microplastic Segmentation
Researchers developed a neural network-based image analysis method for automatically detecting and segmenting microplastic particles in photos of beach sand. The approach uses U-Net and MultiResUNet architectures to identify the small particles. Automated image analysis tools like this could significantly speed up the labor-intensive process of counting and characterizing microplastics in environmental samples.
Identification of Microplastics Based on the Fractal Properties of Their Holographic Fingerprint
Researchers developed an AI-enabled holographic imaging approach to identify microplastics in water using the fractal properties of their holographic fingerprints, offering a fast, label-free identification method.