Remote Sensing Technology and Application, Volume. 40, Issue 3, 520(2025)

Monitoring of Pine Wilt Disease based on UAV Remote Sensing and Machine Learning

Longfei ZHOU1,2, Shiyi JIN2, Xiaowen XU3、*, Yixun WANG3, Lingli XU2, Mingquan CHEN4, Jinrong TIAN5, Hailong LIU5, and Ran MENG6,7
Author Affiliations
  • 1School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo454000, China
  • 2College of Resources and Environment, Huazhong Agricultural University, Wuhan430070, China
  • 3Hubei Academy of Forestry, Wuhan430070, China
  • 4Zhongxiang Panshiling forest farm, Zhongxiang431900, China
  • 5Zhongxiang Dakou forest farm, Zhongxiang431900, China
  • 6School of Computer Science and Technology, Harbin Institute of Technology, Harbin150001, China
  • 7National Key Laboratory of Smart Farm Technologies and Systems, Harbin150001, China
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    Figures & Tables(13)
    Geographical location of the study area and spatial distribution of UAV images and samples
    Research technology roadmap
    Relevant parameters of UAV
    Disease severity map of pine wilt disease
    Results of single tree crown extraction
    Comparison of characteristic variables of different disease severity
    Selection of sensitive characteristic variables of pine wilt disease
    Classification results of pine wilt disease
    Percentage of pine trees with different disease severity in the study area
    • Table 1. Calculation formula of vegetation index

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      Table 1. Calculation formula of vegetation index

      植被指数全称公式参考文献
      r标准化红波段R/(R+G+B)[34]
      g标准化绿波段G/(R+G+B)[34]
      b标准化蓝波段B/(R+G+B)[34]
      ExR过红指数1.4r-g[35]
      ExB过蓝指数1.4b-g[36]
      ExG过绿指数2g-r-b[37]
      GLI绿叶指数(2g-r-b)/(2g+r+b)[38]
      ExGR过绿指数减过红指数(2g-r-b)-(1.4r-g)[35]
      RGI红绿指数r/g[39]
      VARI可见光大气阻抗植被指数(g-r)/(g+r+b)[40]
      NGRDI归一化绿红波段差异指数(g-r)/(g+r)[41]
      NGBDI归一化蓝绿差异指数(g-b)/(g+b)[42]
    • Table 2. Texture feature calculation formula

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      Table 2. Texture feature calculation formula

      纹理特征全称公式参考文献
      MEA均值MeanΣiΣjP,j*j[44]
      VAR方差VarianceΣiΣjp,j*-Mean2
      HOM同质度HomogeneityΣiΣjp,j*11+-j2
      CON对比度ContrastΣiΣjp,j*-j2
      DIS相异性DissimilarityΣiΣjp,j*i-j
      ASM角二阶矩Angular Second MomentΣiΣjp,j²
      ENT熵EntropyΣiΣjp,j* lnp,j
      COR相关性CorrelationΣiΣj-Mean*j-Mean*p,j2Variance
    • Table 3. Accuracy verification of single tree crown extraction results

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      Table 3. Accuracy verification of single tree crown extraction results

      验证区AB总数
      实际数目574198
      分割数目513788
      TP453176
      FP6612
      FN121022
      Recall0.790.760.78
      Precision0.880.840.86
      F1-score0.830.790.82
    • Table 4. Comparison of pine wilt disease classification models based on different feature sets

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      Table 4. Comparison of pine wilt disease classification models based on different feature sets

      特征类型数量RFSVM
      特征变量OA/%Marco-F1OA/%Marco-F1
      VIs5VARI、r、NGRDI、EXR、RGI76.520.7779.680.79
      GLCM2R_VAR、R_MEA46.740.4762.090.54
      VIs&GLCM7VARI、r、NGRDI、EXR、RGI、R_VAR、R_MEA79.470.8085.450.85
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    Longfei ZHOU, Shiyi JIN, Xiaowen XU, Yixun WANG, Lingli XU, Mingquan CHEN, Jinrong TIAN, Hailong LIU, Ran MENG. Monitoring of Pine Wilt Disease based on UAV Remote Sensing and Machine Learning[J]. Remote Sensing Technology and Application, 2025, 40(3): 520

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    Paper Information

    Category:

    Received: May. 18, 2023

    Accepted: --

    Published Online: Sep. 28, 2025

    The Author Email: Xiaowen XU (xuxiaowen222@126.com)

    DOI:10.11873/j.issn.1004-0323.2025.3.0520

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