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Radial distribution of vascular bundle morphology in Chinese bamboos: machine learning methodology for rapid sampling and classification

  • Jing Li , Haocheng Xu , Ying Zhang , Tuhua Zhong , Katherine Semple , Vahid Nasir , Hankun Wang EMAIL logo and Chunping Dai ORCID logo EMAIL logo
Published/Copyright: May 15, 2023
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Holzforschung
From the journal Holzforschung Volume 77 Issue 6

Abstract

Variation in anatomical features of the culm wall namely the shape and size distributions of vascular bundles between different genera and species of bamboo is not well understood due to the cumbersome task of manual measurements. Using machine learning methodology, this work presents a universal vascular bundle detection model for rapid, reliable, and automatic characterization of vascular bundles in culm cross sections of 213 species across 23 genera of Chinese bamboos. The number of vascular bundles and the fiber sheath area have positive linear correlations with the outer circumference and the wall thickness, respectively. The distribution density of vascular bundles has a decay exponential correlation with the outer circumference and the wall thickness. The average fiber volume fraction was 35.2 % ± 7 % with relatively small variation between species. Bamboo species could be grouped into three categories based the endodermis to epidermis distribution pattern of radial and tangential length of vascular bundles, two categories of radial-to-tangential ratio and four categories of fiber sheath area distribution pattern. Implications on bamboo classification, structural and pulp/paper applications were discussed. The findings from this study provide groundwork for the establishment of a unified, authoritative and objective bamboo classification system based on the vascular tissue morphology.

Keywords: bamboo; cross section; object detection; radial distribution; transfer learning; vascular bundle
Corresponding authors: Hankun Wang, Institute of New Bamboo and Rattan Based Biomaterials, International Center for Bamboo and Rattan, NFGA/Beijing Key Lab for Bamboo & Rattan Science and Technology, Beijing 100102, China, E-mail: ; and Chunping Dai, Department of Wood Science, Faculty of Forestry, The University of British Columbia, Vancouver V6T 1Z4, Canada, E-mail:

Funding source: Basic Scientific Research Funds of the International Center for Bamboo and Rattan

Award Identifier / Grant number: 1632022016

Funding source: National Natural Science Foundation of China

Award Identifier / Grant number: 32071855

  1. Author contributions: Jing Li: writing: original draft, investigation. Haocheng Xu: formal analysis, software. Ying Zhang: visualization, data curation. Katherine Semple and Vahid Nasir: writing – drafting, review and editing. Tuhua Zhong and Chunping Dai: conceptualization, resources. Hankun Wang: conceptualization, funding acquisition, project administration.

  2. Research funding: This work was supported by the Basic Scientific Research Funds of the International Center for Bamboo and Rattan (grant no. 1632022016) and the National Natural Science Foundation of China (grant no. 32071855).

  3. Conflict of interest statement: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/hf-2022-0165).

Received: 2022-10-28
Accepted: 2023-03-21
Published Online: 2023-05-15
Published in Print: 2023-06-27

© 2023 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/hf-2022-0165
Keywords for this article
bamboo; cross section; object detection; radial distribution; transfer learning; vascular bundle

Articles in the same Issue

  1. Frontmatter
  2. Wood Physics/Mechanical Properties
  3. Effects of seismic strain rates on the perpendicular-to-grain compression behaviour of Dahurian larch, Mongolian pine and Chinese poplar: tests and stress-strain model
  4. The effect of the growth ring orientation on spring-back and set-recovery in surface-densified wood
  5. Synergistic improvement to dimensional stability of Populus cathay ana via hemicellulose removal/alkali lignin impregnation
  6. Adding gaseous ammonia with heat treatment to improve the mechanical properties of spruce wood
  7. Wood as a hydrothermally stimulated shape-memory material: mechanisms of shape-memory effect and molecular assembly structure networks
  8. Wood Chemistry
  9. Structural comparison of different isolated eucalyptus lignins and analysis of their interaction mechanism with bovine serum albumin solution under QCM-D
  10. Role of α-Fe2O3 nano-particles in protecting wood from ultraviolet light degradation
  11. Wood Science – Non-Tree Plants
  12. Radial distribution of vascular bundle morphology in Chinese bamboos: machine learning methodology for rapid sampling and classification
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