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Multi-level thresholding image segmentation for rubber tree secant using improved Otsu's method and snake optimizer.

Shenghan Li¹, Linlin Ye¹

¹Hainan University, Haikou 570228, China.

Mathematical Biosciences and Engineering : MBE

|June 16, 2023

Summary

This summary is machine-generated.

Early diagnosis of tapping panel dryness (TPD) in rubber trees is crucial. A new SO-Otsu image segmentation method improves TPD diagnosis efficiency and accuracy, outperforming existing techniques.

Keywords:

Otsu's method image segmentation snake optimizer tapping panel dryness

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Area of Science:

Agricultural Science
Computer Vision
Image Processing

Background:

Tapping panel dryness (TPD) significantly reduces natural rubber production.
Early diagnosis of TPD is essential for effective management of rubber trees.
Image segmentation techniques can aid in the early detection and diagnosis of TPD.

Purpose of the Study:

To develop an efficient image segmentation method for diagnosing TPD in rubber trees.
To enhance Otsu's method for multi-level thresholding of TPD images.
To evaluate the proposed SO-Otsu method against other optimization algorithms.

Main Methods:

Investigated TPD image properties and enhanced Otsu's method.
Combined the snake optimizer with the improved Otsu's method to create SO-Otsu.
Compared SO-Otsu with six other image segmentation algorithms.

Main Results:

SO-Otsu demonstrated superior performance compared to competing methods.
The proposed method excelled in running duration, detail effect, and degree of fidelity.
SO-Otsu proved to be an efficient image segmentation technique for TPD images.

Conclusions:

SO-Otsu is an effective and efficient method for segmenting TPD images.
The SO-Otsu method offers improved accuracy and speed for TPD diagnosis.
This approach can enhance the early detection and management of tapping panel dryness in rubber plantations.