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Papers
61,005 resultsShowing papers similar to A Low-Cost Detection Method for Nitrite Content in a Mariculture Water Environment Based on an Improved Residual Network
ClearA Machine Learning Approach To Microplastic Detection And Quantification In Aquatic Environments
This study developed a machine learning approach for detecting and quantifying microplastics in aquatic environments, demonstrating that automated image analysis can improve throughput and accuracy compared to manual microscopic counting for environmental monitoring applications.
Identification of Microplastics in Aquatic Environments Using Oxidative Treatment and Automated Image Analysis
Researchers developed a cost-effective and replicable method for detecting microplastics in freshwater environments using oxidative treatment to digest organic matter from water samples, enabling cleaner isolation and more accurate identification of MP particles without requiring expensive instrumentation.
Predicting Aquaculture Water Quality Using Machine Learning Approaches
Researchers compared four machine learning approaches for predicting water quality parameters in industrial aquaculture systems, finding that back propagation and radial basis function neural networks outperformed support vector machine models for most parameters. The models achieved sufficient accuracy to support real-time management decisions without continuous in-situ monitoring.
From microplastics to pixels: Testing the robustness of two machine learning approaches for automated, Nile red-based marine microplastic identification.
Researchers tested the robustness of two automated machine learning approaches combined with Nile red fluorescent staining for marine microplastic identification, specifically evaluating performance on environmentally weathered particles that challenge the reliability of methods developed using pristine laboratory plastics.
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.
Underwater Image Detection for Cleaning Purposes; Techniques Used for Detection Based on Machine Learning
Researchers reviewed machine learning techniques for underwater image detection to support water pollution cleanup, focusing on convolutional neural networks and region-based CNN methods for identifying surface mucilage and debris. The study evaluated supervised classification algorithms as the most effective approach for automated aquatic waste detection systems.
Efficient and accurate microplastics identification and segmentation in urban waters using convolutional neural networks
Researchers developed convolutional neural network models for efficiently identifying and segmenting microplastics in urban water samples from southern China. The study found that deep learning approaches can significantly reduce the time and labor required for microplastic identification compared to manual methods, offering a scalable tool for monitoring microplastic pollution in urban waterways.
Quantitative analysis of microplastics in water environments based on Raman spectroscopy and convolutional neural network
Researchers developed a method combining Raman spectroscopy with a convolutional neural network to measure microplastic concentrations in water. The approach achieved high accuracy across six different sizes of polyethylene particles in five real-world water environments, outperforming other machine learning models and offering a practical tool for quantitative microplastic monitoring.
Efficient Microplastic Detection in Water Using ResNet50 and Fluorescence Imaging
Researchers applied a ResNet50 deep learning model to fluorescence microscopy images of water samples, achieving high-accuracy classification of microplastics, demonstrating that deep learning can efficiently automate microplastic identification from microscopy data.
From microplastics to pixels: testing the robustness of two machine learning approaches for automated, Nile red-based marine microplastic identification
Two machine learning approaches using Nile red fluorescence staining and automated image analysis were tested for robustness on marine microplastics of multiple polymer types and weathering states, finding performance varied with particle heterogeneity and environmental aging.
Harnessing Deep Learning for Real-Time Water Quality Assessment: A Sustainable Solution
Researchers developed a deep learning system that can predict water quality in real time based on measurements like pH, turbidity, and dissolved solids. While not directly about microplastics, this kind of AI-powered monitoring tool could eventually be adapted to detect microplastic contamination in water supplies more quickly and affordably than current lab-based methods.
Multi Analyte Concentration Analysis of Marine Samples Through Regression Based Machine Learning
Researchers used Raman spectroscopy combined with machine learning to identify concentrations of multiple chemical compounds in marine water samples. The study demonstrates that this approach offers a low-cost, portable method for monitoring ocean chemistry, which is relevant for understanding environmental health in marine ecosystems.
Enhancing marine debris identification with convolutional neural networks
A deep learning model was developed to identify and classify marine debris components captured by underwater remotely operated vehicle imagery, addressing the challenge of widely distributed ocean waste including microplastics. The convolutional neural network demonstrated improved accuracy for debris detection and classification compared to conventional image analysis methods.
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.
Plastic Waste on Water Surfaces Detection Using Convolutional Neural Networks
Researchers evaluated state-of-the-art convolutional neural network architectures for automatically detecting plastic waste on water surfaces, training models on a dataset representing four categories of plastic litter including plastic bags. The study benchmarked multiple CNN object detection models following extensive dataset preprocessing to determine the most effective approach for automated plastic pollution identification.
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.
Identification and detection of microplastic particles in marine environment by using improved faster R–CNN model
Researchers developed an improved Faster R-CNN deep learning model for identifying and detecting microplastic particles in marine environments. The model achieved an average detection confidence of 99% and successfully distinguished polystyrene microplastics from mixed particle suspensions across varying backgrounds and conditions, demonstrating a promising automated approach for monitoring microplastic pollution.
Water Quality Grade Identification for Lakes in Middle Reaches of Yangtze River Using Landsat-8 Data with Deep Neural Networks (DNN) Model
Researchers developed a deep neural network model applied to Landsat-8 satellite data to automatically identify water quality grades for lakes in the middle Yangtze River reaches, demonstrating that machine learning and remote sensing can provide cost-effective large-scale monitoring as an alternative to labor-intensive in situ measurements.
Water Pollution and Its Causes in the Tuojiang River Basin, China: An Artificial Neural Network Analysis
Researchers used artificial neural network analysis to assess water quality and identify pollution causes in the Tuojiang River Basin in China, examining parameters including dissolved oxygen and ammonia-nitrogen to understand contamination patterns and risks in this waterway.
Riverine Microplastic Quantification: A Novel Approach Integrating Satellite Images, Neural Network, and Suspended Sediment Data as a Proxy
Researchers developed satellite-based models using neural network algorithms to estimate riverine microplastic concentrations, using suspended sediment concentration as a proxy, offering a cost-effective approach for broad-scale freshwater microplastic monitoring.
Raman Spectroscopy Enhanced By Machine Learning For Effective Microplastic Detection In Aquatic Systems
Researchers explored combining Raman spectroscopy with machine learning techniques to improve microplastic detection and classification in aquatic systems. The study found that deep learning models, particularly convolutional neural networks, achieved high classification accuracy and significantly reduced reliance on labor-intensive manual spectral analysis for real-time environmental monitoring.
Battle Models: Inception ResNet vs. Extreme Inception for Marine Fish Object Detection
Not relevant to microplastics — this paper compares two deep learning models (Inception ResNet and Xception) for detecting and classifying marine fish species in underwater images, with no connection to plastic pollution.
From microplastics to pixels: Testing the robustness of two machine learning approaches for automated, Nile red-based marine microplastic identification.
Researchers tested the robustness of decision tree and random forest machine learning classifiers combined with Nile red fluorescent staining for automated detection and identification of microplastic polymers weathered under semi-controlled surface water and deep-sea conditions for up to one year. They found both models achieved comparable accuracy above 90% for pristine plastics, but assessed how environmental weathering affected classification reliability, also evaluating analysis time, model complexity, size detection limits, and interoperability.
Detection of Microplastics in Freshwater Sediments Based on Raman Spectroscopy and Convolutional Neural Networks
Researchers developed a method combining Raman spectroscopy and convolutional neural networks to detect and classify microplastics in complex freshwater sediment samples, training the CNN on mixed spectra from extracted sediment fractions to improve detection accuracy.