Automated Anatomical Analysis of Wood Cross Sections Using Macroscopic Images

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.