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Related Experiment Video

Updated: Jun 25, 2026

Infection of In Vivo and In Vitro Pines with the Pinewood Nematode Bursaphelenchus xylophilus and Isolation of Induced Volatiles
08:42

Infection of In Vivo and In Vitro Pines with the Pinewood Nematode Bursaphelenchus xylophilus and Isolation of Induced Volatiles

Published on: September 27, 2024

ESE-PWDNet: an efficient early-stage pine wilt disease detection network.

Zhemin Ma1, Fang Wang2, Haifeng Lin1

  • 1College of Information Science and Technology, Nanjing Forestry University, Nanjing, China.

Frontiers in Plant Science
|June 24, 2026
PubMed
Summary
This summary is machine-generated.

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This study introduces ESE-PWDNet, an efficient Unmanned Aerial Vehicle (UAV) remote sensing model for early Pine Wilt Disease (PWD) detection. The model excels at recognizing small disease targets in complex forest environments, offering improved precision and recall with high efficiency.

Area of Science:

  • Forestry
  • Remote Sensing
  • Computer Vision
  • Plant Pathology

Background:

  • Pine Wilt Disease (PWD) poses a significant threat to forest ecosystems.
  • Early detection of PWD is crucial for effective management and mitigation.
  • Recognizing small, early-stage disease indicators using remote sensing presents a substantial challenge.

Purpose of the Study:

  • To develop an efficient Unmanned Aerial Vehicle (UAV) remote sensing detection model for early Pine Wilt Disease (PWD) identification.
  • To enhance the recognition of small and subtle PWD targets in complex forest scenes.
  • To provide a robust technical solution for precise and efficient forestry pest monitoring.

Main Methods:

  • Construction of a multi-temporal, multi-view high-resolution dataset of early-stage PWD using a DJI Air3 UAV.
Keywords:
UAV imagerycomputer visiondeep learningpine wilt diseasesmall object detection

Related Experiment Videos

Last Updated: Jun 25, 2026

Infection of In Vivo and In Vitro Pines with the Pinewood Nematode Bursaphelenchus xylophilus and Isolation of Induced Volatiles
08:42

Infection of In Vivo and In Vitro Pines with the Pinewood Nematode Bursaphelenchus xylophilus and Isolation of Induced Volatiles

Published on: September 27, 2024

  • Design of the Efficient Visual Linear Unit (EFVLU) as a foundational module for the backbone network.
  • Development of a novel neck network incorporating the Attention State Space Block (ASSB) for high-resolution image processing.
  • Integration of Efficient Multi-scale Attention (EMA) and Lightweight Shared Detail Enhanced Convolutional Detection Head (LSDECD) in the prediction head.

    • Main Results:

    • The proposed ESE-PWDNet model significantly improves the recognition performance of tiny PWD targets in complex environments.
    • The model achieves a Precision (P) of 75.9% and a Recall (R) of 75.1%.
    • ESE-PWDNet demonstrates high inference efficiency with low computational complexity (6.5 GFLOPs, 2.6M parameters), outperforming comparative models.

    Conclusions:

    • ESE-PWDNet offers a reliable and efficient technical solution for early and precise UAV remote sensing monitoring of PWD.
    • The developed dataset and model provide a strong foundation for future research in automated forestry pest detection.
    • This approach facilitates timely intervention strategies to combat the spread of Pine Wilt Disease.