• Chinese Journal of Lasers
  • Vol. 47, Issue 11, 1111002 (2020)
Liu Lixin1、2、*, He Di1, Li Mengzhu1, Liu Xing3, and Qu Junle4
Author Affiliations
  • 1School of Physics and Optoelectronic Engineering, Xidian University, Xi''an, Shaanxi 710071, China
  • 2State Key Laboratory of Transient Optics and Photonics, Xi''an Institute of Optics and Precision Mechanics of Chinese Academy of Sciences, Xi''an, Shaanxi 710119, China
  • 3Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen, Guangdong 518118, China
  • 4College of Physics and Optoelectronic Engineering, Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen University, Shenzhen, Guangdong 518060, China
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    DOI: 10.3788/CJL202047.1111002 Cite this Article Set citation alerts
    Liu Lixin, He Di, Li Mengzhu, Liu Xing, Qu Junle. Identification of Xinjiang Jujube Varieties Based on Hyperspectral Technique and Machine Learning[J]. Chinese Journal of Lasers, 2020, 47(11): 1111002 Copy Citation Text show less
    Schematic of hyperspectral system
    Fig. 1. Schematic of hyperspectral system
    Spectral profiles of Jujube samples before and after preprocess. (a) Average spectra; (b) original spectra; (c) preprocessed spectra by MSC; (d) preprocessed spectra by SNV; (e) preprocessed spectra by 1-Der; (f) preprocessed spectra by SG smoothing
    Fig. 2. Spectral profiles of Jujube samples before and after preprocess. (a) Average spectra; (b) original spectra; (c) preprocessed spectra by MSC; (d) preprocessed spectra by SNV; (e) preprocessed spectra by 1-Der; (f) preprocessed spectra by SG smoothing
    Extracting characteristic bands by PCA. (a) Scores of the first three principal components; (b) variance contribution rate of the first ten principal components
    Fig. 3. Extracting characteristic bands by PCA. (a) Scores of the first three principal components; (b) variance contribution rate of the first ten principal components
    Root-mean-square error calculated according to the number of selected characteristic variables
    Fig. 4. Root-mean-square error calculated according to the number of selected characteristic variables
    Extracting characteristic bands by CARS. (a) Variation curve of the number of variables with the number of sampling; (b) variation curve of RMSECV with the number of sampling; (c) variation path of variable regression coefficient
    Fig. 5. Extracting characteristic bands by CARS. (a) Variation curve of the number of variables with the number of sampling; (b) variation curve of RMSECV with the number of sampling; (c) variation path of variable regression coefficient
    Pretreatment methodNumber of misjudgmentOverall accuracy /%
    Jinsi jujubeJun jujubeTan jujube
    Original37222065.35
    SG smoothing37222065.35
    MSC29263560.53
    SNV29253560.96
    1-Der29141176.32
    Table 1. Identification results of LDA model with different pretreatment methods
    Pretreatment methodNumber of misjudgmentOverall accuracy /%
    Jinsi jujubeJun jujubeTan jujube
    Original1816582.89
    SG Smoothing1816582.89
    MSC14121283.33
    SNV14121283.33
    1-Der000100
    Table 2. Identification results of KNN model with different pretreatment methods
    Pretreatment methodNumber of misjudgmentOverall accuracy /%
    Jinsi jujubeJun jujubeTan jujube
    Original54195.61
    SG Smoothing33296.49
    MSC24296.49
    SNV24296.49
    1-Der000100
    Table 3. Identification results of SVM model with different pretreatment methods
    Characteristic bandsextraction methodNumber of misjudgmentOverall accuracy /%
    Jinsi jujubeJun jujubeTan jujube
    FS29141176.32
    PCA13201977.19
    SPA18161080.70
    CARS1512685.53
    Table 4. Identification results of LDA model with different characteristic bands extraction methods
    Characteristic bandsextraction methodNumber of misjudgmentOverall accuracy /%
    Jinsi jujubeJun jujubeTan jujube
    FS000100
    PCA1010987.28
    SPA911688.60
    CARS03098.68
    Table 5. Identification results of KNN model with different characteristic bands extraction methods
    Characteristic bandsextraction methodNumber of misjudgmentOverall accuracy /%
    Jinsi jujubeJun jujubeTan jujube
    FS000100
    PCA810789.04
    SPA45394.74
    CARS22098.25
    Table 6. Identification results of SVM model with different characteristic bands extraction methods
    Characteristic bands extraction methodNumber of characteristic bandsAccuracy /%Runtime /s
    FS13561001.497
    PCA1089.040.026
    SPA1394.740.032
    CARS27598.250.167
    Table 7. Accuracy and runtime of SVM model based on different characteristic bands extraction methods
    Liu Lixin, He Di, Li Mengzhu, Liu Xing, Qu Junle. Identification of Xinjiang Jujube Varieties Based on Hyperspectral Technique and Machine Learning[J]. Chinese Journal of Lasers, 2020, 47(11): 1111002
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