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Vol. 10, No. 2, May 2025

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Bamboo Diameter Detection System Based on Image Processing as a Pre-Assessment for an Automated Bamboo Splitting Technology

https://doi.org/10.22219/kinetik.v10i2.2170
Sinta Uri El Hakim
Universitas Gadjah Mada
Rokhmat Arifianto
National Research and Innovation Agency
Sugiyanto
Universitas Gadjah Mada
Ilham Ayu Putri Pratiwi
Universitas Gadjah Mada
Galuh Bahari
Universitas Gadjah Mada
Radhian Krisnaputra
Universitas Gadjah Mada

Corresponding Author(s) : Sugiyanto

sugiyanto_t@ugm.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 10, No. 2, May 2025

Abstract

Bamboo is recognized for its eco-friendly attributes and rapid growth, serves as a promising sustainable alternative to wood. However, the high production cost of laminated bamboo remains a major challenge due to labor-intensive processes, particularly manual splitting, which affects efficiency and labor costs. To overcome this issue, this study presents an automated bamboo diameter measurement system that leverages Canny Edge Detection and Hough Transform to ensure precise and uniform slat dimensions. A dataset of 100 bamboo images with diameters ranging from 11 - 13 cm was utilized for training and testing. The system achieved a high accuracy, with a coefficient of determination (R²) of 0.973, demonstrating strong predictive reliability. Furthermore, Bayesian Optimization was applied to fine-tune parameters, resulting in an optimized configuration for both Canny Edge Detection and Hough Transform. The proposed system reduces dependence on manual labor, thereby lowering production costs and improving overall manufacturing efficiency. Automation in the bamboo splitting process ensures consistent and precise slat dimensions, supporting scalability and enhancing the economic feasibility of laminated bamboo production. The findings of this study provide a practical and sustainable solution to optimize production, making laminated bamboo a more viable and competitive material in the industry.

Keywords

Hough Transform Canny Edge Detection Bamboo Laminated Bamboo Radius Pre-assesment
Hakim, S. U. E., Arifianto, R. ., Sugiyanto, Pratiwi, I. A. P., Bahari, G., & Krisnaputra, R. (2025). Bamboo Diameter Detection System Based on Image Processing as a Pre-Assessment for an Automated Bamboo Splitting Technology. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 10(2). https://doi.org/10.22219/kinetik.v10i2.2170
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References
  1. P. M. Forster et al., “Indicators of Global Climate Change 2022: Annual update of large-scale indicators of the state of the climate system and human influence,” Earth Syst. Sci. Data, vol. 15, no. 6, pp. 2295–2327, 2023, doi: 10.5194/essd-15-2295-2023.
  2. J. Bredenoord, “Bamboo as a Sustainable Building Material for Innovative, Low-Cost Housing Construction,” Sustain. , vol. 16, no. 6, 2024, doi: 10.3390/su16062347.
  3. D. Behera, S. S. Pattnaik, D. Nanda, P. P. Mishra, S. Manna, and A. K. Behera, “A review on bamboo fiber reinforced composites and their potential applications,” Emergent Mater., 2024, doi: 10.1007/s42247-024-00832-9.
  4. A. S. Devi and K. S. Singh, “Carbon storage and sequestration potential in aboveground biomass of bamboos in North East India,” Sci. Rep., vol. 11, no. 1, pp. 1–8, 2021, doi: 10.1038/s41598-020-80887-w.
  5. J. Q. Yuen, T. Fung, and A. D. Ziegler, “Carbon stocks in bamboo ecosystems worldwide: Estimates and uncertainties,” For. Ecol. Manage., vol. 393, pp. 113–138, 2017, doi: https://doi.org/10.1016/j.foreco.2017.01.017.
  6. N. Nugroho and N. Ando, “Development of structural composite products made from bamboo II: fundamental properties of laminated bamboo lumber,” J. Wood Sci., vol. 47, no. 3, pp. 237–242, 2001, doi: 10.1007/BF01171228.
  7. E. S. Bakar, M. N. M. Nazip, R. Anokye, and L. Seng Hua, “Comparison of three processing methods for laminated bamboo timber production,” J. For. Res., vol. 30, no. 1, pp. 363–369, 2019, doi: 10.1007/s11676-018-0629-2.
  8. J. Tao and H. Chen, “A Pupil Diameter Measurement System Based on Image Processing,” in 2024 Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC), 2024, pp. 1–6. doi: 10.1109/IPEC61310.2024.00011.
  9. R. Yamaguchi, R. Watanabe, N. Fujii, D. Kokuryo, T. Kaihara, and Y. Sunami, “Automatic Measurement of Timber Diameter Using Image Processing,” Procedia CIRP, vol. 126, pp. 44–47, 2024, doi: https://doi.org/10.1016/j.procir.2024.08.259.
  10. Z. Qi, W. Hua, Z. Zhang, X. Deng, T. Yuan, and W. Zhang, “A novel method for tomato stem diameter measurement based on improved YOLOv8-seg and RGB-D data,” Comput. Electron. Agric., vol. 226, p. 109387, 2024, doi: https://doi.org/10.1016/j.compag.2024.109387.
  11. A. G. Poyraz, M. Kaçmaz, H. Gürkan, and A. E. Dirik, “Sub-Pixel counting based diameter measurement algorithm for industrial Machine vision,” Measurement, vol. 225, p. 114063, 2024, doi: https://doi.org/10.1016/j.measurement.2023.114063.
  12. Z. Lu et al., “A Deep Learning Method for Log Diameter Measurement Using Wood Images Based on Yolov3 and DeepLabv3+,” Forests, vol. 15, no. 5, 2024, doi: 10.3390/f15050755.
  13. L. Pu, X. Zhang, J. Shi, S. Wei, T. Zhang, and X. Zhan, “Precise RCS Extrapolation via Nearfield 3-D Imaging With Adaptive Parameter Optimization Bayesian Learning,” IEEE Trans. Antennas Propag., vol. 70, no. 5, pp. 3656–3671, 2022, doi: 10.1109/TAP.2021.3137212.
  14. M. Nikolic, E. Tuba, and M. Tuba, “Edge detection in medical ultrasound images using adjusted Canny edge detection algorithm,” 24th Telecommun. Forum, TELFOR 2016, pp. 1–4, 2017, doi: 10.1109/TELFOR.2016.7818878.
  15. J. Canny, “A Computational Approach to Edge Detection,” IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-8, no. 6, pp. 679–698, 1986, doi: 10.1109/TPAMI.1986.4767851.
  16. L. Wang, X. Ma, and H. Wang, “Hybrid Image Edge Detection Algorithm Based on Fractional Differential and Canny Operator,” in 2018 11th International Symposium on Computational Intelligence and Design (ISCID), 2018, pp. 210–213. doi: 10.1109/ISCID.2018.10149.
  17. L. Yuan and X. Xu, “Adaptive Image Edge Detection Algorithm Based on Canny Operator,” Proc. - 2015 4th Int. Conf. Adv. Inf. Technol. Sens. Appl. AITS 2015, no. 2, pp. 28–31, 2016, doi: 10.1109/AITS.2015.14.
  18. D. Ji, Y. Liu, and C. Wang, “Research on Image Edge Detection Based on Improved Canny Operator,” in 2022 3rd International Conference on Information Science, Parallel and Distributed Systems (ISPDS), 2022, pp. 229–232. doi: 10.1109/ISPDS56360.2022.9874064.
  19. Y. Li and B. Liu, “Improved edge detection algorithm for canny operator,” IEEE Jt. Int. Inf. Technol. Artif. Intell. Conf., vol. 2022-June, pp. 1–5, 2022, doi: 10.1109/ITAIC54216.2022.9836608.
  20. P. V. C. Hough, “Method and means for recognition complex patterns,” Us3069654, p. 6, 1962, [Online]. Available: https://www.researchgate.net/publication/236519005_Method_and_Means_for_Recognizing_Complex_Patterns
  21. R. O. Duda and P. E. Hart, “Use of the Hough Transformation to Detect Lines and Curves in Pictures,” Commun. ACM, vol. 15, no. 1, pp. 11–15, 1972, doi: 10.1145/361237.361242.
  22. SUTARNO, ABDURAHMAN, R. PASSARELLA, Y. PRIHANTO, and R. A. . GULTOM, “Mathematical Implementation of Circle Hough Transformation Theorem Model Using C# For Calculation Attribute of Circle,” vol. 172, no. Siconian 2019, pp. 454–458, 2020, doi: 10.2991/aisr.k.200424.070.
  23. Y. Mao, S. Wang, L. Chen, F. Han, M. Pang, and H. Li, “Plastic Optical Fiber Dimension Measurement Based on Canny Edge Detection and Hough Line Detection,” 2024 IEEE 7th Inf. Technol. Networking, Electron. Autom. Control Conf., vol. 7, pp. 375–379, 2024, doi: 10.1109/ITNEC60942.2024.10733135.
  24. J. Snoek, H. Larochelle, and R. P. Adams, “Practical Bayesian optimization of machine learning algorithms,” Adv. Neural Inf. Process. Syst., vol. 4, pp. 2951–2959, 2012.
  25. B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, and N. De Freitas, “Taking the human out of the loop: A review of Bayesian optimization,” Proc. IEEE, vol. 104, no. 1, pp. 148–175, 2016, doi: 10.1109/JPROC.2015.2494218.
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