• Spectroscopy and Spectral Analysis
  • Vol. 41, Issue 7, 2181 (2021)
Wen XIAO1;, Ying-li CAO1; 2; *;, Shuai FENG1;, Ya-di LIU1;, Kai-lun JIANG1;, Zheng-xin YU1;, and Li YAN1;
Author Affiliations
  • 1. College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110161, China
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    DOI: 10.3964/j.issn.1000-0593(2021)07-2181-07 Cite this Article
    Wen XIAO, Ying-li CAO, Shuai FENG, Ya-di LIU, Kai-lun JIANG, Zheng-xin YU, Li YAN. Detection of Rice Sheath Blight Disease Index Based on Split-Window Gram-Schmidt Transformation and PSO-SVR Algorithm[J]. Spectroscopy and Spectral Analysis, 2021, 41(7): 2181 Copy Citation Text show less
    Distribution of 20 rice plots
    Fig. 1. Distribution of 20 rice plots
    The original spectral reflectance curves of rice under the condition of three degrees of rice sheath blight(a): Low altitude remote sensing canopy; (b): Ground canopy
    Fig. 2. The original spectral reflectance curves of rice under the condition of three degrees of rice sheath blight
    (a): Low altitude remote sensing canopy; (b): Ground canopy
    First-order derivative spectral reflectance curves of rice under the condition of three degrees of rice sheath blight(a): Low altitude remote sensing canopy; (b): Ground canopy
    Fig. 3. First-order derivative spectral reflectance curves of rice under the condition of three degrees of rice sheath blight
    (a): Low altitude remote sensing canopy; (b): Ground canopy
    Inverse-log spectral reflectance curves of rice under the condition of three degrees of rice sheath blight(a): Low altitude remote sensing canopy; (b): Ground canopy
    Fig. 4. Inverse-log spectral reflectance curves of rice under the condition of three degrees of rice sheath blight
    (a): Low altitude remote sensing canopy; (b): Ground canopy
    Main base and characteristic wavelength based on window dividing Gram-Schmidt transform(a): IRS of the low altitude remote sensing canopy; (b): FDRS of the ground canopy
    Fig. 5. Main base and characteristic wavelength based on window dividing Gram-Schmidt transform
    (a): IRS of the low altitude remote sensing canopy; (b): FDRS of the ground canopy
    降维方法光谱变换SVR_R2SVR_RMSEPSO-SVR_R2PSO-SVR_RMSE
    分窗Gram-Schmidt
    变换
    低空冠层ORS0.6580.2600.6830.201
    低空冠层FDRS0.6650.2250.7000.160
    低空冠层IRS0.6810.2350.7310.151
    地面冠层ORS0.6710.2700.7010.159
    地面冠层FDRS0.7650.1840.7780.147
    地面冠层IRS0.7070.2070.7520.149
    主成分分析
    PCA
    低空冠层ORS0.5910.5020.6210.440
    低空冠层FDRS0.6610.2760.6750.218
    低空冠层IRS0.6810.2280.7050.160
    地面冠层ORS0.6150.4720.650.424
    地面冠层FDRS0.6820.2140.7150.152
    地面冠层IRS0.6450.3080.6720.264
    连续投影法
    SPA
    低空冠层ORS0.5140.5720.5960.465
    低空冠层FDRS0.6270.4830.6580.372
    低空冠层IRS0.6520.3370.6700.289
    地面冠层ORS0.5740.5820.6490.433
    地面冠层FDRS0.6460.2620.6910.168
    地面冠层IRS0.5790.4630.6600.359
    Table 1. The result of regression modeling with support vector machine
    Wen XIAO, Ying-li CAO, Shuai FENG, Ya-di LIU, Kai-lun JIANG, Zheng-xin YU, Li YAN. Detection of Rice Sheath Blight Disease Index Based on Split-Window Gram-Schmidt Transformation and PSO-SVR Algorithm[J]. Spectroscopy and Spectral Analysis, 2021, 41(7): 2181
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