In recent years, we have seen many advancements in wood species identification. Methods like DNA analysis, Near Infrared (NIR) spectroscopy, and Direct Analysis in Real Time (DART) mass spectrometry complement the long-established wood anatomical assessment of cell and tissue morphology. However, most of these methods have some limitations such as high costs, the need for skilled experts for data interpretation, and the lack of good datasets for professional reference. Therefore, most of these methods, and certainly the wood anatomical assessment, may benefit from tools based on Artificial Intelligence. In this paper, we apply two transfer learning techniques with Convolutional Neural Networks (CNNs) to a multi-view Congolese wood species dataset including sections from different orientations and viewed at different microscopic magnifications. We explore two feature extraction methods in detail, namely Global Average Pooling (GAP) and Random Encoding of Aggregated Deep Activation Maps (RADAM), for efficient and accurate wood species identification. Our results indicate superior accuracy on diverse datasets and anatomical sections, surpassing the results of other methods. Our proposal represents a significant advancement in wood species identification, offering a robust tool to support the conservation of forest ecosystems and promote sustainable forestry practices.

翻译：近年来，木材树种识别领域取得了诸多进展。DNA分析、近红外光谱、实时直接分析质谱等方法，补充了基于细胞与组织形态学的传统木材解剖评估方法。然而，这些方法大多存在成本高昂、数据解读需资深专家、缺乏优质专业参考数据集等局限性。因此，上述方法（尤其是木材解剖评估）有望受益于基于人工智能的工具。本文针对包含不同切面方向及不同显微放大倍率的刚果木材树种多视角数据集，应用两种基于卷积神经网络的迁移学习技术。我们详细探究了全局平均池化和聚合深度激活图随机编码两种特征提取方法，以实现高效准确的木材树种识别。结果表明，本方法在不同数据集和解剖切面上均展现出卓越精度，优于其他方法。本研究成果标志着木材树种识别领域的重大突破，可为保护森林生态系统、促进可持续林业实践提供有力工具。