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. 2024 Mar 21;19(3):e0297284.
doi: 10.1371/journal.pone.0297284. eCollection 2024.

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

Zhenjing Xie  1 , Jinran Wu  1 , Weirui Tang  1 , Yongna Liu  1
Affiliations

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

Zhenjing Xie et al. PLoS One. .

Abstract

Addressing the profound impact of Tapping Panel Dryness (TPD) on yield and quality in the global rubber industry, this study introduces a cutting-edge Otsu threshold segmentation technique, enhanced by Dung Beetle Optimization (DBO-Otsu). This innovative approach optimizes the segmentation threshold combination by accelerating convergence and diversifying search methodologies. Following initial segmentation, TPD severity levels are meticulously assessed using morphological characteristics, enabling precise determination of optimal thresholds for final segmentation. The efficacy of DBO-Otsu is rigorously evaluated against mainstream benchmarks like Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM), and compared with six contemporary swarm intelligence algorithms. The findings reveal that DBO-Otsu substantially surpasses its counterparts in image segmentation quality and processing speed. Further empirical analysis on a dataset comprising TPD cases from level 1 to 5 underscores the algorithm's practical utility, achieving an impressive 80% accuracy in severity level identification and underscoring its potential for TPD image segmentation and recognition tasks.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. The improvement and application.
Fig 2
Fig 2. The conceptual model of boundary selection strategy.
Fig 3
Fig 3. The beetle proportion chart.
Fig 4
Fig 4. Original image.
Fig 5
Fig 5. Grayscale histogram.
Fig 6
Fig 6. Segmentation image.
Fig 7
Fig 7. DBO-Otsu flowchart.
Fig 8
Fig 8. Low-level epidermis disease Fig 1.
Fig 9
Fig 9. Low-level epidermis disease Fig 2.
Fig 10
Fig 10. Low-level epidermis disease Fig 3.
Fig 11
Fig 11. High-level epidermis disease Fig 1.
Fig 12
Fig 12. High-level epidermis disease Fig 2.
Fig 13
Fig 13. High-level epidermis disease Fig 3.
Fig 14
Fig 14. High-level epidermis disease Latex Fig 1.
Fig 15
Fig 15. High-level epidermis disease Latex Fig 2.
Fig 16
Fig 16. High-level epidermis disease Latex Fig 3.
Fig 17
Fig 17. Iterative comparison figure.
Fig 18
Fig 18. Detail analysis of original image 1.
Fig 19
Fig 19. Detail analysis of original image 2.
Fig 20
Fig 20. Detail analysis of original image 3.
Fig 21
Fig 21. Comparison of detail analysis.
Fig 22
Fig 22. Application evaluation of the original image.
Fig 23
Fig 23. Application evaluation of the incision image.
Fig 24
Fig 24. Application evaluation of the latex image.
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References

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Grants and funding

This research was supported by the Hainan Provincial Natural Science Foundation of China, grant number 20163140. This grant primarily funded the construction of the dataset and the research into the segmentation algorithm.