Abstract
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.
Abstract (translated)
近年来，我们在木种鉴定方面看到了许多进展。像DNA分析、近红外（NIR）光谱和实时直接分析（DART）质谱法等方法补充了长期确立的木解剖学评估。然而，大多数这些方法都有一些局限性，比如高成本，数据解释需要熟练专家，以及缺乏专业参考数据的缺乏。因此，大多数这些方法和木解剖学评估可能会从基于人工智能的工具中受益。在本文中，我们将两个迁移学习技术——卷积神经网络（CNNs）应用于包括不同方向和以不同显微镜放大倍数的摩氏 Congolese 木种数据集中的截面。我们详细探讨了两种特征提取方法，即全局平均池化（GAP）和聚集深度激活映射（RADAM）的随机编码。我们的结果表明，在多样数据集和木解剖学截面中具有卓越的准确性和精度，超过了其他方法的成果。我们的建议在木种鉴定方面是一个显著的进步，为支持森林生态系统的保护和促进可持续森林经营提供了一个有力的工具。
URL
https://arxiv.org/abs/2404.08585
