deep learning-based methods for WoodID on multiple datasets with varying magnification levels. Several popular Convolutional Neural Networks, including DenseNet, ResNet50, and MobileNet, were examined to identify the best network and magnification levels. The experiments were conducted on five datasets with different magnifications, including a self-collected dataset and four existing ones. The results demonstrate that the DenseNet121 network achieved superior accuracy and F1-Score on the 20X dataset. The findings of this study provide useful insights into the development of automatic WoodID systems for practical applications.