• Laser & Optoelectronics Progress
  • Vol. 59, Issue 19, 1930001 (2022)
Xin Ma, Biao Wang, Chun Li, Qingxiao Ma, Yan Teng, and Ling Jiang*
Author Affiliations
  • College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, Jiangsu, China
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    DOI: 10.3788/LOP202259.1930001 Cite this Article Set citation alerts
    Xin Ma, Biao Wang, Chun Li, Qingxiao Ma, Yan Teng, Ling Jiang. Maturity Identification of Camellia Seeds Based on Mid- and Far-Infrared Data Fusion[J]. Laser & Optoelectronics Progress, 2022, 59(19): 1930001 Copy Citation Text show less
    Camellia fruit and camellia seed sample diagrams. (a) Ripe amellia fruit; (b) camellia seeds after removing the shell of camellia; (c) camellia seed slice sample for mid-infrared spectroscopy test; (d) compressed samples of camellia seeds mixed with polyethylene powder for far-infrared spectroscopy
    Fig. 1. Camellia fruit and camellia seed sample diagrams. (a) Ripe amellia fruit; (b) camellia seeds after removing the shell of camellia; (c) camellia seed slice sample for mid-infrared spectroscopy test; (d) compressed samples of camellia seeds mixed with polyethylene powder for far-infrared spectroscopy
    Mid-infrared absorption spectra of camellia seeds with different oil contents
    Fig. 2. Mid-infrared absorption spectra of camellia seeds with different oil contents
    Far-infrared absorption spectra of camellia seeds with different oil contents
    Fig. 3. Far-infrared absorption spectra of camellia seeds with different oil contents
    Optimization results of c and g parameters of SVM model based on intermediate data fusion. (a) Three-dimensional view optimized by GS algorithm;(b) contour map optimized by GS algorithm; (c) accuracy curve optimized by GA algorithm;(d) accuracy curve optimized by PSO algorithm
    Fig. 4. Optimization results of c and g parameters of SVM model based on intermediate data fusion. (a) Three-dimensional view optimized by GS algorithm;(b) contour map optimized by GS algorithm; (c) accuracy curve optimized by GA algorithm;(d) accuracy curve optimized by PSO algorithm
    Picking timeMinimum /%Maximum /%Average value /%
    September 25th13.1218.0215.23
    September 30th15.2919.3117.36
    October 6th18.2623.5020.65
    October 13th19.2223.5422.03
    October 20th20.6024.3323.08
    Table 1. Oil content of camellia seed
    Spectral typeFeature extraction methodNumber of variablesNumber of misjudgments in the training setNumber of misjudgments in the prediction setTraining set accuracy /%Prediction set accuracy /%
    Mid-infraredNONE186814980.0070.00
    PCA95692.8680.00
    SPA325592.8683.33
    UVE4307890.0073.33
    Far-infraredNONE1743495.7186.67
    PCA92397.1490.00
    SPA232397.1490.00
    UVE1633495.7186.67
    Table 2. Prediction results of SVM model under different feature extraction methods of mid-infrared and far-infrared spectra
    Spectral typeParameter optimization methodSVM parameterNumber of misjudgmentsPrediction set accuracy /%
    cg
    Mid-infraredGS2.29746.9644390.00
    GA2.85237.0050293.33
    PSO5.83979.7162390.00
    Far-infraredGS1.00002.8284293.33
    GA3.34530.4628196.67
    PSO60.10000.0100196.67
    Table 3. Comparison of classification results of optimized SVM model
    Optimization algorithmOptimal parametersNumber of misjudgmentsPrediction set accuracy /%
    cg
    Grid search0.75791.3195196.67
    Genetic algorithm4.30981.38230100
    Particle swarm algorithm1.10671.23190100
    Table 4. Results of SVM model after parameter optimization based on intermediate data fusion
    Xin Ma, Biao Wang, Chun Li, Qingxiao Ma, Yan Teng, Ling Jiang. Maturity Identification of Camellia Seeds Based on Mid- and Far-Infrared Data Fusion[J]. Laser & Optoelectronics Progress, 2022, 59(19): 1930001
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