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基于光谱图像的森林病虫害自动检测方法研究

周晓丽 周立君 伊力塔 刘宇

周晓丽, 周立君, 伊力塔, 刘宇. 基于光谱图像的森林病虫害自动检测方法研究[J]. 应用光学.
引用本文: 周晓丽, 周立君, 伊力塔, 刘宇. 基于光谱图像的森林病虫害自动检测方法研究[J]. 应用光学.
ZHOU Xiaoli, ZHOU Lijun, YI Lita, LIU Yu. Research on automatic detection method of forest diseases and insect pests based on spectral image[J]. Journal of Applied Optics.
Citation: ZHOU Xiaoli, ZHOU Lijun, YI Lita, LIU Yu. Research on automatic detection method of forest diseases and insect pests based on spectral image[J]. Journal of Applied Optics.

基于光谱图像的森林病虫害自动检测方法研究

基金项目: 兵器联合基金(6141B01020205)
详细信息
    作者简介:

    周晓丽(1978—),女,高级工程师,主要从事森林草原保护研究工作。E-mail:892851099@qq.com

  • 中图分类号: TP39

Research on automatic detection method of forest diseases and insect pests based on spectral image

  • 摘要: 我国天然林区分布范围广,地形复杂,依靠传统的护林员巡检方式进行林木病虫害防治,效率较低,难于及时发现早期的林木病虫害,可能因此错过防治的最佳时机。针对g该问题,设计了一种基于多光谱图像检测林木病虫害的深度学习网络,研发了一套检测软件,通过无人机挂飞实验,利用搭建的深度学习网络,完成林区染病区检测,对检测结果进行了分析。
  • 图  1  基于光谱图像的森林虫害检测的处理流程

    Fig.  1  Processing flow of forest pest detection based on spectral image

    图  2  健康林木和染病林木相对光谱反射率曲线对比

    Fig.  2  Comparison of relative spectral reflectance curves between healthy trees and infected trees

    图  3  深度学习检测网络结构

    Fig.  3  Deep learning detection network structure

    图  4  金字塔池化模块内部结构

    Fig.  4  Internal structure of pyramid pooling module

    图  5  注意力调整模块内部结构

    Fig.  5  Internal structure of attention adjustment module

    图  6  检测软件界面

    Fig.  6  Test software interface

    图  7  近距离染病区检测结果

    Fig.  7  Detection results of close infected area

    图  8  远距离染病区检测结果

    Fig.  8  Detection results of remote infected area

    表  1  不同尺度病害区域的检出率对比

    Table  1  Comparison of detection rates in different scale disease areas

    区域大小/像素检出率/%
    大于等于50×5092.3
    大于30×30小于50×5065.6
    小于等于30×3023.1
    下载: 导出CSV

    表  2  本文方法与主流方法的比较

    Table  2  Comparisons with state-of-the-art real-time methods

    模型图像分辨率/像素MIoU/%帧频/FPS
    ENet [20]1 024×51253.160.5
    ESPNet [21]1 024×51256.5108.3
    BiSeNetV2[22]1 024×51265.5152.1
    BiSeNetV2-L[22]1 024×51268.9117
    STDC1-Seg50[23]1 024×51271.1198.6
    STDC2-Seg50[23]1 024×51272.3149.8
    本文方法1 024×51275.2137.1
    下载: 导出CSV

    表  3  消融实验情况

    Table  3  Results of ablation experiments

    金字塔池化模块注意力调整模块MIoU/%帧频/FPS
    70.3156.6
    74.5113.8
    75.2137.1
    下载: 导出CSV
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  • 网络出版日期:  2022-09-17

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