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61,005 resultsShowing papers similar to Research on Soil Microplastics Detection Algorithm based on Hyperspectral Imaging Technology
ClearApplication 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.
Study 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.
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 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.
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.
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.
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.
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.
VNIR and SWIR Hyperspectral Imaging for Microplastic detection on Soil
Researchers applied VNIR (400-1000 nm) and SWIR (1000-2000 nm) hyperspectral imaging to detect and identify seven types of cryo-milled microplastic polymers mixed into soil surfaces. Partial least squares regression models successfully distinguished polymer types, offering a non-destructive, rapid screening approach for identifying microplastics directly in soil environments.
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.
Coupling hyperspectral imaging with machine learning algorithms for detecting polyethylene (PE) and polyamide (PA) in soils.
Researchers combined hyperspectral imaging with machine learning algorithms to detect polyethylene and polyamide microplastics in soil samples. This rapid detection approach could support large-scale soil monitoring for microplastic contamination, which is important given that agricultural soils may accumulate plastics from mulch films, irrigation water, and sewage sludge.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Hyperspectral Imaging Algorithms and Applications: A Review
This paper is not about microplastics. It is a broad review of hyperspectral imaging algorithms and their applications across agriculture, healthcare, earth sciences, industrial manufacturing, and security, tracing development from early image processing through modern deep learning approaches. While hyperspectral imaging can be applied to microplastic detection, this review covers the technology's full range of applications rather than focusing on environmental contamination.
Development of robust models for rapid classification of microplastic polymer types based on near infrared hyperspectral images
Researchers used near-infrared hyperspectral imaging combined with machine learning to classify nine types of microplastic particles, finding reliable results even for small particles on wet filters. This method could enable faster, automated identification of diverse microplastic types in environmental water samples.
Hyperspectral Imaging and Data Analysis for Detecting and Determining Plastic Contamination in Seawater Filtrates
Researchers tested whether hyperspectral imaging combined with multivariate data analysis could detect and identify plastic particles on filters from seawater samples, finding the method could locate plastic contamination and distinguish polymer types. This approach could offer a faster and more automated alternative to manual microscopy for environmental microplastic monitoring.
A novel and simple method for measuring nano/microplastic concentrations in soil using UV-Vis spectroscopy with optimal wavelength selection
Researchers developed a simple UV-Vis spectroscopy method for measuring nano- and microplastic concentrations in soil, using optimized wavelength combinations to account for interference from soil particles. The study demonstrated a linear relationship between spectroscopic measurements and actual plastic concentrations, offering a potentially practical tool for monitoring plastic contamination across different soil types.