Full text available in english in Adobe Acrobat format:https://www.forestry.actapol.net/issue2/volume/12_2_2025.pdf
original articleIssue 24 (2) 2025 pp. 113–122
Volker Haag¹, Kilian Dremel2, Tim Lewandrowski1, Anne Reisenbach1, Jannik Stebani2 3, Simon Zabler4, Valentina Zemke¹ and Gerald Koch¹
1Thuenen Institute of Wood Research, Hamburg, Germany
2Fraunhofer Development Center X-ray Technology EZRT, Würzburg, Germany
3Experimentelle Physik
5, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
4Deggendorf Institute of Technology (THD), Faculty of Computer Science, Germany
2Fraunhofer Development Center X-ray Technology EZRT, Würzburg, Germany
3Experimentelle Physik
5, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
4Deggendorf Institute of Technology (THD), Faculty of Computer Science, Germany
IDENTIFICATION OF WOOD AND CHARCOAL USING 3D-MICROSCOPY, μCT VOLUMETRIC IMAGING, AND MACHINE-LEARNING APPROACHES: A REVIEW
Abstract
In an age of increasing surveillance and regulation of markets and trade flows, reliable methods for identifying the species and origin of commercial timbers and other woody plant products are essential. In highly processed and composite materials – such as charcoal, engineered wood products, and paper – DNA and chemical signatures are often degraded or modified. In contrast, anatomical structures are typically preserved, making structural identification the only reliable method. This review compares the principal anatomical methods used for wood and charcoal identification. It summarizes the strengths and limitations of traditional techniques and highlights recent advances in digital and AI-driven approaches, emphasizing innovations from the last ten years.
Keywords: wood identification, charcoal analysis, wood anatomy, artificial intelligence, digital image analysis, species identification, trade control
Full text available in english in Adobe Acrobat format:https://www.forestry.actapol.net/issue2/volume/12_2_2025.pdf
https://doi.org/10.17306/J.AFW.2025.2.7
For citation:
| MLA | Haag¹, Volker, et al. "IDENTIFICATION OF WOOD AND CHARCOAL USING 3D-MICROSCOPY, μCT VOLUMETRIC IMAGING, AND MACHINE-LEARNING APPROACHES: A REVIEW." Acta Sci.Pol. Silv. 24.2 (2025): . https://doi.org/10.17306/J.AFW.2025.2.7 |
| APA | (2025). IDENTIFICATION OF WOOD AND CHARCOAL USING 3D-MICROSCOPY, μCT VOLUMETRIC IMAGING, AND MACHINE-LEARNING APPROACHES: A REVIEW. Acta Sci.Pol. Silv. 24 (2), https://doi.org/10.17306/J.AFW.2025.2.7 |
| ISO 690 | HAAG¹, Volker, et al. IDENTIFICATION OF WOOD AND CHARCOAL USING 3D-MICROSCOPY, μCT VOLUMETRIC IMAGING, AND MACHINE-LEARNING APPROACHES: A REVIEW. Acta Sci.Pol. Silv., 2025, 24.2: . https://doi.org/10.17306/J.AFW.2025.2.7 |
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