Abstract: Wood anatomical features are crucial in forestry science, traditionally relying on manual inspection of wood cross-sections. This conventional method is time-consuming, subjective, and dependent on expert experience. Recent advancements in deep learning offer high accuracy but often operate as black-box models, lacking interpretability and struggling with out-of-distribution challenges under real-world variations. To address these limitations, we propose a two-stage framework combining deep-learning-based image classification and explicit anatomical feature analysis, directly extracting expert-recognized morphological attributes such as pore size, frequency, and spatial arrangement from macroscopic images. By quantifying these anatomical descriptors, our framework yields transparent, OOD-robust features that can be directly fed into downstream species-identification models, thereby enhancing future classification accuracy while preserving interpretability. An end-to-end implementation integrates data acquisition, automated feature extraction, and interactive visualization, making the methodology practically applicable in both laboratory and field settings.

Khanh Nguyen-Trong and Thanh Nhan Nguyen-Thi. “Automated Anatomical Analysis of Wood Cross Sections Using Macroscopic Images”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.7 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0160784

@article{Nguyen-Trong2025,
title = {Automated Anatomical Analysis of Wood Cross Sections Using Macroscopic Images},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160784},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160784},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {7},
author = {Khanh Nguyen-Trong and Thanh Nhan Nguyen-Thi}
}