Spectroscopy and Spectral Analysis, Volume. 41, Issue 12, 3837(2021)

Study on the Identification Method of Citrus Leaves Based on Hyperspectral Imaging Technique

Ye-lan WU1、*, Yi-yu CHEN1、1;, Xiao-qin LIAN1、1;, Yu LIAO2、2;, Chao GAO1、1;, Hui-ning GUAN1、1;, and Chong-chong YU1、1;
Author Affiliations
  • 11. Key Laboratory of Internet and Big Data in Light Industry, Beijing Technology and Business University, Beijing 100048, China
  • 22. Institute of Agricultural Engineering, Jiangxi Academy of Agricultural Sciences, Nanchang 330200, China
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    Figures & Tables(9)
    Hyperspectral imaging system
    Hyperspectral images of leaves of five citrus species
    Spectral curves of 13 250 samples
    Average spectral curves of five types of leaves
    The first four principal component load curves of the original spectrum
    Sample distribution scatter diagramFigure (a), (b), (c) and (d) respectively represent the scatter plots of sample distribution using original spectra and the spectra after preprocessing by 1st Der, MSC and SNV
    • Table 1. Modeling results of full-band data

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      Table 1. Modeling results of full-band data

      模型预处理
      方法
      识别率/%
      正常
      叶片
      溃疡病
      叶片
      除草剂
      叶片
      红蜘蛛
      叶片
      煤烟病
      叶片
      SVM原始100.0099.7890.03100.0092.08
      1st Der100.0099.7891.0599.5894.09
      MSC99.7399.7880.6997.3588.38
      SNV100.0099.7879.8098.6087.17
      RF原始99.4698.4484.9198.7493.19
      1st Der99.4699.3374.0498.7493.29
      MSC98.9197.1072.7696.2385.97
      SNV98.9197.1073.4096.3787.17
    • Table 2. PCA characteristic wavelength modeling results

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      Table 2. PCA characteristic wavelength modeling results

      模型预处理
      方法
      识别率/%
      正常
      叶片
      溃疡病
      叶片
      除草剂
      叶片
      红蜘蛛
      叶片
      煤烟病
      叶片
      SVM原始100.0099.1172.5198.4681.66
      1st Der99.1899.7879.2898.4687.27
      MSC98.1098.4472.7694.9781.56
      SNV98.1098.6672.5194.8381.16
      RF原始98.6496.6574.3098.4685.97
      1st Der99.1896.6580.0598.0491.58
      MSC97.5597.3268.8093.8580.96
      SNV97.2897.3268.4193.7281.06
    • Table 3. Modeling results under different pretreatment methods of all-band and PCA wavelength data

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      Table 3. Modeling results under different pretreatment methods of all-band and PCA wavelength data

      模型评价指标FSPCA
      原始1st DerMSCSNV原始1st DerMSCSNV
      SVMOA95.23%95.98%91.30%91.03%87.53%90.82%86.50%86.32%
      Kappa0.938 50.948 20.887 90.884 40.839 20.881 60.826 00.823 6
      RFOA93.84%91.42%88.01%88.56%88.77%91.79%84.93%84.81%
      Kappa0.920 50.889 20.845 30.852 30.855 00.894 00.805 60.804 0
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    Ye-lan WU, Yi-yu CHEN, Xiao-qin LIAN, Yu LIAO, Chao GAO, Hui-ning GUAN, Chong-chong YU. Study on the Identification Method of Citrus Leaves Based on Hyperspectral Imaging Technique[J]. Spectroscopy and Spectral Analysis, 2021, 41(12): 3837

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    Paper Information

    Category: Research Articles

    Received: Oct. 26, 2020

    Accepted: --

    Published Online: Dec. 17, 2021

    The Author Email: Ye-lan WU (wuyel@th.btbu.edu.cn)

    DOI:10.3964/j.issn.1000-0593(2021)12-3837-07

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