Accurate identification of wood species is a challenging \textit{Fine-Grained Visual Classification (FGVC)}, playing a crucial role in supply chain management and in combating illegal logging. Conventional Convolutional Neural Networks (CNNs) often fail to capture subtle morphological details due to feature compression (global pooling), even though macro-images inherently contain both global structural context and fine-grained cues. To overcome this limitation, we propose \textbf{FA-Net (Fine-Anatomical Network)}, a novel dual-branch architecture that employs a \textit{global branch} to capture global structural context (e.g. porosity types and vessel distribution) and a \textit{local branch} to preserve local morphological details (e.g. parenchyma patterns and vessel/ray sizes) from macro-scale images. Both branches are enhanced with channel–spatial attention mechanisms and are adaptively fused through a pyramid self-attention module, yielding a highly discriminative representation. Comprehensive experiments across five benchmark datasets demonstrate that FA-Net achieves state-of-the-art accuracy, reaching up to 99.32\%—outperforming the DenseNet121 baseline by 4.0\%—while maintaining near-real-time inference speed. Interpretability analysis via EigenCAM further confirms that FA-Net successfully attends to critical anatomical traits (such as porosity types and parenchyma patterns). FA-Net provides an efficient, transparent and deployment-ready solution for practical applications in forestry and customs inspection.