[go: up one dir, main page]

Pang et al., 2021 - Google Patents

Hyperspectral imaging coupled with multivariate methods for seed vitality estimation and forecast for Quercus variabilis

Pang et al., 2021

Document ID
13660917993112391752
Author
Pang L
Wang J
Men S
Yan L
Xiao J
Publication year
Publication venue
Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy

External Links

Snippet

In this study, the feasibility of estimation and forecast of different vitality Quercus variabilis seeds by a hyperspectral imaging technique were investigated. Artificially accelerated aging was conducive to achieve the division of four vitality levels. Hyperspectral data in the first 10 …
Continue reading at www.sciencedirect.com (other versions)

Classifications

    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
    • G01N21/17Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infra-red light
    • G01N21/359Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infra-red light using near infra-red light
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
    • G01N21/17Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infra-red light
    • G01N21/3563Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infra-red light for analysing solids; Preparation of samples therefor
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
    • G01N21/17Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infra-red light
    • G01N21/3577Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infra-red light for analysing liquids, e.g. polluted water
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
    • G01N21/17Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/314Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry with comparison of measurements at specific and non-specific wavelengths
    • G01N2021/3155Measuring in two spectral ranges, e.g. UV and visible

Similar Documents

Publication Publication Date Title
Pang et al. Hyperspectral imaging coupled with multivariate methods for seed vitality estimation and forecast for Quercus variabilis
An et al. Advances in infrared spectroscopy and hyperspectral imaging combined with artificial intelligence for the detection of cereals quality
Ma et al. Rapid and non-destructive seed viability prediction using near-infrared hyperspectral imaging coupled with a deep learning approach
Pang et al. Rapid vitality estimation and prediction of corn seeds based on spectra and images using deep learning and hyperspectral imaging techniques
Li et al. Accurate prediction of soluble solid content in dried Hami jujube using SWIR hyperspectral imaging with comparative analysis of models
Wang et al. Application of long-wave near infrared hyperspectral imaging for determination of moisture content of single maize seed
Jin et al. Determination of viability and vigor of naturally-aged rice seeds using hyperspectral imaging with machine learning
ElMasry et al. Utilization of computer vision and multispectral imaging techniques for classification of cowpea (Vigna unguiculata) seeds
Li et al. Discriminating varieties of tea plant based on Vis/NIR spectral characteristics and using artificial neural networks
Zhang et al. Non-destructive identification of slightly sprouted wheat kernels using hyperspectral data on both sides of wheat kernels
Wang et al. Assessment of protein content and insect infestation of maize seeds based on on-line near-infrared spectroscopy and machine learning
Liu et al. Rapid determination of rice protein content using near-infrared spectroscopy coupled with feature wavelength selection
Pang et al. Rapid seed viability prediction of Sophora japonica by improved successive projection algorithm and hyperspectral imaging
Zhou et al. Hyperspectral imaging of beet seed germination prediction
Liu et al. Variety classification of coated maize seeds based on Raman hyperspectral imaging
Sun et al. Rapid and nondestructive watermelon (Citrullus lanatus) seed viability detection based on visible near‐infrared hyperspectral imaging technology and machine learning algorithms
Yang et al. A recognition method of corn varieties based on spectral technology and deep learning model
Jin et al. Predicting the nutrition deficiency of fresh pear leaves with a miniature near-infrared spectrometer in the laboratory
Chen et al. Quality detection and variety classification of pecan seeds using hyperspectral imaging technology combined with machine learning
Xiao et al. Rapid detection of maize seed germination rate based on Gaussian process regression with selection kernel function
Cheng et al. Hyperspectral and imagery integrated analysis for vegetable seed vigor detection
CN117933084A (en) Inversion method for nitrogen content of apple canopy leaf blade based on hyperspectrum
Fan et al. Non-destructive detection of single-seed viability in maize using hyperspectral imaging technology and multi-scale 3D convolutional neural network
Phanomsophon et al. Rapid measurement of classification levels of primary macronutrients in durian (Durio zibethinus Murray CV. Mon Thong) leaves using FT-NIR spectrometer and comparing the effect of imbalanced and balanced data for modelling
Yuan et al. In-field and non-destructive determination of comprehensive maturity index and maturity stages of Camellia oleifera fruits using a portable hyperspectral imager