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Improved Pine Wood Nematode Disease Diagnosis System Based on Deep Learning.

Jiaming Xiao1, Jin Wu2, Dongdong Liu1

  • 1School of Technology, Beijing Forestry University, Beijing 100083, China.

Plant Disease
|April 23, 2025
PubMed
Summary

A new deep learning fluorescence detection system rapidly identifies pine wilt disease caused by the pine nematode (Bursaphelenchus xylophilus). This advanced method offers a faster, more accurate alternative to traditional PCR for forestry protection.

Keywords:
Bursaphelenchus xylophilusdeep learningfast detection systemobject detectionpine wood nematode disease

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Area of Science:

  • Forestry Science
  • Plant Pathology
  • Biotechnology

Background:

  • Pine wilt disease, caused by Bursaphelenchus xylophilus, poses a significant threat to global forestry.
  • Conventional PCR methods for detection are time-consuming and complex.
  • Rapid and effective detection is crucial to manage disease spread and minimize pine felling.

Purpose of the Study:

  • To develop a rapid and accurate fluorescence-based detection system for pine wilt disease using deep learning.
  • To enhance existing deep learning models for improved nematode detection accuracy and efficiency.
  • To integrate detection, analysis, and transmission capabilities into a user-friendly system.

Main Methods:

  • Development of a deep learning-based fluorescence detection system for Bursaphelenchus xylophilus.
  • Comparison of conventional machine learning algorithms with YOLOv5 and YOLOv10 for image processing.
  • Enhancement of the YOLOv5 model with Res2Net and SimAM attention, and replacement of PANet with Bi-FPN for improved feature fusion.

Main Results:

  • The enhanced YOLOv5 model achieved a 39.98% accuracy improvement, especially for large-size images.
  • The novel system can detect pine nematode DNA concentrations as low as 1 fg/μl within 20 minutes.
  • The system integrates detection instruments, computing, and mobile devices for potential field applications.

Conclusions:

  • The developed deep learning fluorescence detection system offers a rapid, sensitive, and accurate method for identifying pine wilt disease.
  • This technology significantly outperforms conventional detection methods in terms of speed and complexity.
  • The integrated and portable nature of the system holds great promise for practical, on-site forest health monitoring.