Application of Variant Transfer Learning in Wood Recognition

Penggui Huang, Fan Zhao, Zheng Zhu, Yanfeng Zhang, Xiaoping Li, Zhangkang Wu


Wood is a material commonly found in nature and is widely used in all professions and industries. Because wood has varied growth cycles and physical properties, there are large differences in its usage and commercial price. In addition, some woods are nationally protected species. Therefore, it is of great importance to accurately identify the type of wood. Traditional wood recognition methods rely on experts and specialized equipment. To facilitate wood recognition, this paper proposes an approach for wood recognition using images. Next, a transfer learning technology was used to extract the textural features of wood, and a global average pooling (GAP) layer was used to reduce the number of features. Finally, the extreme learning machine (ELM) was used for classification. The recognition accuracy of this approach for the Wood Species Dataset was 93.07%, which was higher than the method used by the data provider. This approach had a higher recognition accuracy and a more stable recognition performance than previous approaches.


Wood recognition; Transfer learning; Extreme learning machine

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Welcome to BioResources! This online, peer-reviewed journal is devoted to the science and engineering of biomaterials and chemicals from lignocellulosic sources for new end uses and new capabilities. The editors of BioResources would be very happy to assist you during the process of submitting or reviewing articles. Please note that logging in is required in order to submit or review articles. Martin A. Hubbe, (919) 513-3022,; Lucian A. Lucia, (919) 515-7707, URLs:; ISSN: 1930-2126