Pang et al., 2021 - Google Patents
Hyperspectral imaging coupled with multivariate methods for seed vitality estimation and forecast for Quercus variabilisPang 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 …
- 238000000701 chemical imaging 0 title abstract description 36
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infra-red light
- G01N21/359—Investigating 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
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infra-red light
- G01N21/3563—Investigating 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
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infra-red light
- G01N21/3577—Investigating 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
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/314—Investigating 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
- G01N2021/3155—Measuring 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 |