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Papers
61,005 resultsShowing papers similar to Coupling hyperspectral imaging with machine learning algorithms for detecting polyethylene (PE) and polyamide (PA) in soils.
ClearStudy on detection method of microplastics in farmland soil based on hyperspectral imaging technology
Researchers developed a method using hyperspectral imaging and machine learning to rapidly detect and classify different types of microplastics in farmland soil. The technology achieved high accuracy in identifying common plastic types like polyethylene and polypropylene in soil samples. Better detection tools like this are essential for monitoring microplastic contamination in agricultural land and understanding its potential impact on food safety.
Accurate detection of low concentrations of microplastics in soils via short-wave infrared hyperspectral imaging
Researchers combined short-wave infrared hyperspectral imaging with machine learning algorithms to detect low concentrations of polyamide and polyethylene microplastics in soil samples, achieving accurate classification with implications for fast, non-destructive screening of agricultural land for plastic contamination.
Application of hyperspectral imaging technology in the rapid identification of microplastics in farmland soil
Researchers applied hyperspectral imaging technology combined with machine learning to rapidly screen and classify microplastics in farmland soil samples, demonstrating an efficient non-destructive identification method for soil microplastic contamination.
Application of hyperspectral and deep learning in farmland soil microplastic detection
Hyperspectral imaging combined with deep learning was applied to detect and classify microplastics in farmland soil, offering a non-destructive, rapid alternative to time-consuming chemical extraction methods. The model achieved high classification accuracy across polymer types, demonstrating the potential for field-deployable microplastic monitoring in agricultural settings.
Hyperspectral detection of soil microplastics via multimodal feature fusion and a dual-path attention residual convolutional network
A hyperspectral imaging approach combined with multimodal deep learning was developed to detect microplastics in soil, achieving high accuracy in identifying plastic particles against complex soil backgrounds. The method offers a faster, less destructive alternative to traditional chemical extraction and spectroscopy for soil monitoring.
Efficient screening of microplastics in soils using hyperspectral imaging in the short-wave infrared range coupled with machine learning – A laboratory-based experiment
Researchers tested short-wave infrared hyperspectral imaging combined with machine learning to detect three types of microplastics in soil, finding it could identify elevated contamination but was not sensitive enough for typical environmental background levels. The technique shows most promise for screening heavily polluted sites like landfills and industrial areas.
A Preliminary Study on the Utilization of Hyperspectral Imaging for the On-Soil Recognition of Plastic Waste Resulting from Agricultural Activities
Researchers explored the use of near-infrared hyperspectral imaging to detect and identify plastic waste in agricultural soils. They developed a classification model that could distinguish different types of plastic from soil and assess the degradation state of the material. The study demonstrates that hyperspectral imaging combined with chemometric analysis offers a rapid, non-destructive approach for monitoring plastic contamination in agricultural environments.
A novel way to rapidly monitor microplastics in soil by hyperspectral imaging technology and chemometrics
Hyperspectral imaging combined with chemometrics was demonstrated as a novel way to rapidly detect and map multiple types of microplastics in soil samples, identifying particles of different polymer types based on their spectral signatures. The approach could enable faster and more spatially detailed monitoring of microplastic contamination in agricultural and environmental soils.
Toward high-precision analysis of soil micro-and nanoplastics: A review of spectroscopy and machine learning approaches
Researchers reviewed multiple spectroscopy techniques — including infrared, Raman, and hyperspectral imaging — combined with machine learning as faster, cheaper alternatives to traditional methods for detecting microplastics and nanoplastics in soil. While promising, key challenges remain including poor detection of nanoplastics, limited real-world validation, and detection limits that often miss environmentally relevant concentrations.
Research on Soil Microplastics Detection Algorithm based on Hyperspectral Imaging Technology
Researchers developed a soil microplastic detection algorithm using hyperspectral imaging (400-1000 nm wavelength range) combined with three supervised classification approaches -- Support Vector Machine (SVM), Mahalanobis Distance (MD), and a third algorithm -- to enable convenient and efficient identification and classification of microplastic pollutants in soil.
Research on Identification and Classification Methods for Soil Microplastics in Hyperspectral Detection
Hyperspectral imaging was tested as a rapid, large-area detection method for identifying and classifying microplastics in soil, offering an alternative to time-consuming particle-by-particle Raman or FTIR spectroscopy. The approach could allow researchers to map microplastic distribution across soil samples far more efficiently. Faster detection technology is important for expanding the geographic scope of soil microplastic monitoring and for assessing contamination in agricultural land.
Critical evaluation of hyperspectral imaging technology for detection and quantification of microplastics in soil
Researchers evaluated whether hyperspectral imaging technology can reliably detect and quantify microplastics in soil under varying real-world conditions. They found that near-infrared imaging generally works well but is significantly affected by factors like soil moisture, microplastic color, and particle size. The study recommends sorting microplastics by size before analysis and further research into moisture effects, providing the first comprehensive evaluation of this emerging detection technology for soil monitoring.
Microplastic Analysis in Soil Using Ultra-High-Resolution UV–Vis–NIR Spectroscopy and Chemometric Modeling
Researchers tested a new method using UV-visible-near infrared spectroscopy combined with machine learning to identify microplastics in soil samples. They found the technique could rapidly and accurately distinguish between different plastic polymers and natural soil particles. The study offers a promising alternative to current labor-intensive identification methods, potentially making large-scale microplastic soil monitoring more practical.
VNIR and SWIR Hyperspectral Imaging for Microplastic detection on Soil
Researchers used non-destructive hyperspectral imaging in visible-near infrared and short-wave infrared ranges to detect microplastics on soil surfaces. Using seven different cryo-milled microplastic polymers and partial least squares analysis, the study demonstrates that hyperspectral imaging can identify microplastics in soil without the complicated, time-consuming steps required by conventional detection methods.
Application of hyperspectral imaging and machine learning for the automatic identification of microplastics on sandy beaches
Hyperspectral imaging combined with machine learning was applied to identify and classify microplastics on sandy beach surfaces, offering a faster and more scalable alternative to conventional spectroscopic analysis for large-area environmental monitoring.
Microplastic detection in soil by THz Time-Domain hyperspectral imaging combined with unsupervised learning analysis
Researchers applied terahertz time-domain hyperspectral imaging combined with multiple unsupervised machine-learning algorithms to detect and spatially map low-density polyethylene microplastics in soil, demonstrating that all five methods consistently separated plastic from soil without requiring labeled training data, establishing a reference-free detection approach.
Microplastic detection in arable soil using a 3D Laser Scanning Confocal Microscope coupled with a Machine-Learning Algorithm
Researchers used 3D laser scanning confocal microscopy paired with machine learning to detect microplastics in agricultural soil. The method successfully identified low-density polyethylene particles from mulching films, providing a faster and more precise tool for tracking plastic contamination in farmland.
Rapid detection of microplastics in plastic-covered soils using FT-NIR and ATR-FTIR spectral data fusion
Researchers developed a rapid, non-destructive method to detect microplastics in agricultural soils by combining two infrared spectroscopy techniques (FT-NIR and ATR-FTIR) with machine-learning models. The fused spectral approach substantially outperformed either technique alone, detecting microplastics down to around 7 parts per million. Fast, accurate soil screening tools are critical for understanding and managing the growing microplastic contamination in farmland.
Spectrometric Detection Of Microplastics In The Environment: A Novel Approach Using Hyperspectral Imaging System
This study developed a novel spectrometric approach to detect microplastics in environmental samples, combining spectral analysis with machine learning classification. The method enabled rapid, accurate identification of multiple polymer types without extensive sample preparation.
Application of Near-infrared Spectroscopy and Multiple Spectral Algorithms to Explore the Effect of Soil Particle Sizes on Soil Nitrogen Detection
Researchers applied near-infrared spectroscopy with machine learning algorithms to rapidly measure soil nitrogen content. While focused on agricultural management rather than microplastics, spectroscopic methods like near-infrared are also used for detecting microplastics in soil samples.
Hyperspectral remote sensing as an environmental plastic pollution detection approach to determine occurrence of microplastics in diverse environments
Researchers tested whether hyperspectral remote sensing technology could detect microplastics mixed into different environmental surfaces like soil, water, concrete, and vegetation. Using near-infrared and short-wave infrared imaging, they achieved over 90% accuracy in detecting and classifying six common plastic types at concentrations as low as 0.15%. The study suggests that remote sensing could become a practical, large-scale tool for monitoring microplastic pollution across diverse environments.
Study on Rapid Quantitative Detection of Soil MPs Based on Terahertz Time-Domain Spectroscopy
Researchers developed a rapid method for detecting and quantifying microplastics in soil using terahertz time-domain spectroscopy combined with machine learning algorithms. The classification models achieved high accuracy in identifying different types of microplastics including polyethylene, polystyrene, and polypropylene. The study suggests that terahertz spectroscopy could provide a faster and more efficient alternative to current methods for monitoring microplastic contamination in agricultural soils.
Hyperspectral Imaging for Detecting Plastic Debris on Shoreline Sands to Support Recycling
Researchers explored the use of hyperspectral imaging technology to detect and identify different types of plastic debris on beach sand. The method can distinguish between various polymer types, supporting more efficient recycling and cleanup operations. The study demonstrates a non-contact detection approach that could help prevent further degradation of shoreline plastics into microplastics.
Microplastics detection in agricultural soil combining 3D Laser Scanning Confocal Microscopy with machine learning
Scientists developed a new workflow combining 3D laser scanning confocal microscopy with machine learning to detect and quantify microplastics in agricultural soils more efficiently and without hazardous chemicals. The method was validated on soil samples spiked with transparent microplastics, which are particularly difficult to identify. Faster, safer detection tools are essential for understanding how widely microplastics from plastic mulch films and organic fertilizers contaminate farmland soils.