Geometric and Topological Bases of a New Classification of Wood Vascular Tissues, Part 2: Classification of Vessels According to Their Grouping

• Published in: Sustainability 2022-02, Vol.14 (4), p.2031
• Main Authors: Bardarov, Nikolai, Christoff, Nicole, Todorov, Vladislav
• Format: Article
• Language: English
• Subjects: Algorithms; Classification; Computer industry; Computer vision; Discriminant analysis; Indicators; Machine learning; Neural networks; Plant species; Principal components analysis; Quantitative analysis; Recognition; Vascular tissue; Wood
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su14042031

The arrangement of vessels and their grouping is unique in most tree species. When observing tiny, microscopic samples of wood, the arrangement of the wood vessels forms a characteristic and repetitive pattern, which is largely determined by the tree species, but it is also influenced by the site conditions as well as its location in the tree. The present study is part of a project aimed at applying computer vision and computer recognition methods to present a more general and comprehensive group classification of wood vessels. Quantitative descriptions of the grouping of vessels, as a rule, have so far been used mainly to reveal characteristic deviations from the typical structure of wood, for example, due to extreme site conditions. Therefore, they are applicable but not sufficient for the present study and need in-depth revision. A classification of vessels is presented depending on their mutual position, and more precisely, the groups of adjacent vessels are determined using quantitative methods. The quantitative indicators used for this purpose are based on the diameter and other quantitative indicators of the vessels’ arrangements. The proposed classification, although based on a long-known classification scheme in structural wood science, allows for the more precise definition of the classes of a grouping of adjacent vessels in a cross-section as a necessary step towards the wider use of the methods of machine recognition of wood.