Evaluation of Wood Species Identification Using CNN-Based Networks at Different Magnification Levels

Wood species identification (WoodID) is a crucial task in many industries, including forestry, construction, and furniture manufacturing. However, this process currently requires highly trained individuals and is time-consuming. With the recent advances in machine learning and computer vision techniques, automatic WoodID using macro-images of cross-section wood has gained attention. Nevertheless, existing works have been evaluated on ad-hoc datasets with pre-fixed magnification levels. To address this issue, this paper proposes an evaluation of 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.

@article{Nguyen-Trong2023,
  title     = {Evaluation of Wood Species Identification Using CNN-Based Networks at Different Magnification Levels},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {4},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Khanh Nguyen-Trong},
  doi       = {10.14569/IJACSA.2023.0140487},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140487}
}