• Spectroscopy and Spectral Analysis
  • Vol. 42, Issue 10, 3226 (2022)
Rong-hua GAO1、*, Lu FENG1、1; 2; *;, Yue ZHANG3、3;, Ji-dong YUAN3、3;, Hua-rui WU1、1; 2;, and Jing-qiu GU1、1; 2;
Author Affiliations
  • 11. Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
  • 33. School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
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    DOI: 10.3964/j.issn.1000-0593(2022)10-3226-09 Cite this Article
    Rong-hua GAO, Lu FENG, Yue ZHANG, Ji-dong YUAN, Hua-rui WU, Jing-qiu GU. Early Detection of Tomato Gray Mold Disease With Multi-Dimensional Random Forest Based on Hyperspectral Image[J]. Spectroscopy and Spectral Analysis, 2022, 42(10): 3226 Copy Citation Text show less
    Tomato leaves hyperspectral image acquisition diagram
    Fig. 1. Tomato leaves hyperspectral image acquisition diagram
    Comparisons of reflectance of different ROIs after inoculation(a): Tomato leaf on the 6th day after infection; (b): Different ROIs selection;(c): Comparisons of reflectivity of different ROIs; (d): Comparisons of reflectivity of leaf on the 1st and 9th day after infection
    Fig. 2. Comparisons of reflectance of different ROIs after inoculation
    (a): Tomato leaf on the 6th day after infection; (b): Different ROIs selection;(c): Comparisons of reflectivity of different ROIs; (d): Comparisons of reflectivity of leaf on the 1st and 9th day after infection
    RGB image of leaves
    Fig. 3. RGB image of leaves
    Changes of spectral reflectance of samples observed for 7 consecutive data
    Fig. 4. Changes of spectral reflectance of samples observed for 7 consecutive data
    Original series (a) and interrelated series (b)
    Fig. 5. Original series (a) and interrelated series (b)
    Symbolic aggregate approximation (SAX) method
    Fig. 6. Symbolic aggregate approximation (SAX) method
    Symbolic Fourier approximation (SFA) method
    Fig. 7. Symbolic Fourier approximation (SFA) method
    Hyperspectral curve of diseased and healthy leaves for 7 consecutive observations(a): Consecutive 7-day reflectivity of infected leaf 1; (b): Consecutive 7-day reflectivity of infected leaf 2; (c): Consecutive 7-day reflectivity of healthy leaf 1; (b): Consecutive 7-day reflectivity of healthy leaf 2
    Fig. 8. Hyperspectral curve of diseased and healthy leaves for 7 consecutive observations
    (a): Consecutive 7-day reflectivity of infected leaf 1; (b): Consecutive 7-day reflectivity of infected leaf 2; (c): Consecutive 7-day reflectivity of healthy leaf 1; (b): Consecutive 7-day reflectivity of healthy leaf 2
    Recognition results of SDSS-SAX-SFA-MRF model
    Fig. 9. Recognition results of SDSS-SAX-SFA-MRF model
    Recognition results of MDSS-SAX-SFA-MRF model(a): SFA multidimensional spectrum; (b): SAX multidimensional spectrum; (c): SAX+SFA multidimensional spectrum
    Fig. 10. Recognition results of MDSS-SAX-SFA-MRF model
    (a): SFA multidimensional spectrum; (b): SAX multidimensional spectrum; (c): SAX+SFA multidimensional spectrum
    Comparison results of MDSS-SAX-SFA-MRF model and SDSS-SAX-SFA-MRF model
    Fig. 11. Comparison results of MDSS-SAX-SFA-MRF model and SDSS-SAX-SFA-MRF model
    模型参数参数选择(程序随机)
    决策树数量50
    符号化方法{SAX, SFA, SAX+SFA}
    字母表大小a{3, 4, 5}
    单词长度l{3, 4, 5, 6}
    滑动窗口w{20%, 30%, 40%, 50%, 60%}
    Table 1. Parameters of symbolic methods and weighted random forest model
    Rong-hua GAO, Lu FENG, Yue ZHANG, Ji-dong YUAN, Hua-rui WU, Jing-qiu GU. Early Detection of Tomato Gray Mold Disease With Multi-Dimensional Random Forest Based on Hyperspectral Image[J]. Spectroscopy and Spectral Analysis, 2022, 42(10): 3226
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