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Advancing image segmentation with DBO-Otsu: Addressing rubber tree diseases through enhanced threshold techniques.

Zhenjing Xie1, Jinran Wu1, Weirui Tang1

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Summary

This study introduces Dung Beetle Optimization-Otsu (DBO-Otsu) for improved Tapping Panel Dryness (TPD) detection in rubber trees. The DBO-Otsu method enhances image segmentation accuracy and speed for better TPD severity assessment.

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Optimization Algorithms

Background:

  • Tapping Panel Dryness (TPD) significantly impacts global rubber yield and quality.
  • Accurate and efficient detection of TPD is crucial for the rubber industry.

Purpose of the Study:

  • To introduce an optimized Otsu threshold segmentation technique (DBO-Otsu) for TPD detection.
  • To enhance the accuracy and speed of TPD severity assessment using image analysis.

Main Methods:

  • Implemented Dung Beetle Optimization (DBO) to enhance the Otsu threshold segmentation method.
  • Utilized morphological characteristics for TPD severity assessment.
  • Evaluated DBO-Otsu against standard metrics (PSNR, SSIM, FSIM) and other algorithms.

Main Results:

  • DBO-Otsu demonstrated superior image segmentation quality and processing speed compared to existing methods.
  • Achieved 80% accuracy in identifying TPD severity levels (1-5).

Conclusions:

  • DBO-Otsu is a highly effective algorithm for TPD image segmentation and recognition.
  • The method offers practical utility for improving TPD management in the rubber industry.