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Papers
20 resultsShowing papers similar to Machine Learning Powered Microalgae Classification by Use of Polarized Light Scattering Data
ClearPulse Feature-Enhanced Classification of Microalgae and Cyanobacteria Using Polarized Light Scattering and Fluorescence Signals
Researchers used polarization-sensitive flow cytometry with enhanced pulse feature analysis to classify microalgae and cyanobacteria in mixed samples, training classifiers on optical signatures that distinguish cell types without staining. The method achieved high classification accuracy and offers potential for rapid, label-free phytoplankton monitoring in environmental water samples.
Machine learning for microalgae detection and utilization
This review assessed machine learning applications for microalgae detection, classification, and utilization in aquaculture and bioproduction, finding that deep learning approaches achieve the highest accuracy for species identification from microscopy images. The authors highlighted ML as an enabling technology for automating microalgae monitoring and optimizing production in industrial bioreactors.
Classification of Microplastic Particles in Water using Polarized Light Scattering and Machine Learning Methods
Researchers developed a reflection-based, in-situ classification method for microplastic particles in water using polarized light scattering combined with machine learning, successfully identifying colorless particles in the 50-300 micrometer range. The approach circumvents transmission-based interference problems and offers a pathway toward continuous, large-scale microplastic monitoring in aquatic environments.
Digital Image Identification of Plankton Using Regionprops and Bagging Decision Tree Algorithm
Researchers developed a digital image classification system using machine learning to identify and count plankton from microscopy images. The method reduced the time and subjectivity of manual identification while maintaining accuracy. Automated plankton identification could also be adapted to distinguish microplastics from biological particles in environmental water samples.
Application of a convolutional neural network for automated multiclass identification of field-collected microplastics and diatom algae from optical microscopy images
Researchers developed and evaluated a convolutional neural network model using transfer learning to automatically classify field-collected microplastics and diatom algae from optical microscopy images, using a dataset of real microplastics sampled from a freshwater reservoir. The model achieved automated multi-class identification, including detection of diatom frustules that survive hydrogen peroxide processing, addressing challenges posed by the lack of standardised microplastic analysis protocols.
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.
Differentiation of suspended particles by polarized light scattering at 120°
A polarized light scattering method was developed to rapidly distinguish different types of suspended particles in seawater, including microplastics, microalgae, and sediment. This optical approach could enable faster, real-time monitoring of microplastic concentrations in ocean water.
Classification of suspended particles in seawater using an in situ polarized light scattering prototype
This study developed and field-tested an underwater sensor that uses polarized light scattering to distinguish between microplastics, sediment particles, and phytoplankton in seawater in real time. Lab tests showed classification accuracy above 85%, and the device was successfully deployed in a Chinese coastal bay across two seasons. Such in-situ monitoring tools could greatly improve our ability to track microplastic concentrations in the ocean without the labor-intensive sample collection and lab analysis currently required.
Probing Individual Particles in Aquatic Suspensions by Simultaneously Measuring Polarized Light Scattering and Fluorescence
Researchers developed a portable optical sensor that simultaneously measures polarized light scattering and fluorescence from individual particles in water, enabling classification of microplastics versus microalgae in situ. This dual-measurement approach improves particle identification accuracy compared to single-measurement methods.
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.
Plankton classification with high-throughput submersible holographic microscopy and transfer learning
Researchers used underwater holographic microscopes and transfer learning — an AI technique that applies knowledge from one task to another — to automatically classify diverse plankton species from images, including rare forms. The system shows promise for large-scale, automated ocean monitoring without needing constant human analysis.
Rapid identification of marine microplastics by laser-induced fluorescence technique based on PCA combined with SVM and KNN algorithm
Researchers developed a laser-based fluorescence method combined with machine learning algorithms to rapidly identify different types of marine microplastics. The system achieved classification accuracy above 97 percent for four common plastic types at various concentrations. The technique offers a fast, non-destructive alternative to traditional laboratory methods for monitoring microplastic pollution in ocean environments.
Material analysis with polarization holography and machine learning
Researchers developed a polarization holographic imaging system combined with machine learning to identify different materials, demonstrating the approach on microplastic identification. This novel optical method could become a fast, non-destructive tool for classifying microplastics in environmental samples.
Imaging and spectroscopic analysis of pathogens in water, and their classification with machine learning algorithms
Researchers developed an integrated approach for automated classification of cyanobacterial pathogens in water using dark-field illumination imaging combined with Raman spectroscopy, with machine learning algorithms applied for rapid species identification. The system aims to reduce pathogen detection times in water quality monitoring compared to conventional culture-based methods.
FE-YOLO: An Efficient Deep Learning Model Based on Feature-Enhanced YOLOv7 for Microalgae Identification and Detection
The FE-YOLO deep learning model integrates a coordinate attention group shuffle convolution module and improved IoU loss function into YOLOv7, achieving superior accuracy in identifying and detecting microalgae cells compared to baseline YOLO architectures.
On the use of machine learning for microplastic identification from holographic phase-contrast signatures
This study applied machine learning to identify microplastic types from holographic phase-contrast imaging signatures, achieving rapid automated classification. Automated identification tools are important for scaling up microplastic monitoring in marine waters where manual identification is too slow and labor-intensive.
Machine learning-based prediction and model interpretability analysis for algal growth affected by microplastics
Researchers used machine learning models to predict how microplastics affect algal growth and found that exposure time, microplastic concentration, and particle size are the most important factors. Smaller microplastics and longer exposure periods had the greatest negative effects on algae, particularly when particles were smaller than the algal cells. The study provides a data-driven approach for assessing the ecological risks of microplastic pollution in aquatic environments.
Statistical Mueller matrix driven discrimination of suspended particles
Researchers developed a statistical method using polarized light scattering to distinguish between different types of suspended particles. This technique has potential applications for identifying and characterizing microplastic particles in water samples.
Recent advances in the application of machine learning methods to improve identification of the microplastics in environment
This review examined a decade of progress in applying machine learning algorithms to microplastic identification, finding that support vector machines and artificial neural networks significantly improve detection accuracy and efficiency when combined with spectroscopic techniques like FTIR and Raman.
Data Study Group Final Report: Centre for Environment, Fisheries and Aquaculture Science
Machine learning was applied to the challenge of automatically classifying plankton species from underwater images collected by fisheries monitoring systems. The AI classifier could identify dozens of plankton categories with high accuracy, reducing the need for time-consuming manual identification. Automated plankton monitoring improves understanding of marine food web health and ecosystem responses to environmental change.