Advanced wood species identification based on multiple anatomical sections and using deep feature transfer and fusion

• URL: http://arxiv.org/abs/2404.08585v1
• Date: Fri, 12 Apr 2024 16:30:15 GMT
• Title: Advanced wood species identification based on multiple anatomical sections and using deep feature transfer and fusion
• Authors: Kallil M. Zielinski, Leonardo Scabini, Lucas C. Ribas, Núbia R. da Silva, Hans Beeckman, Jan Verwaeren, Odemir M. Bruno, Bernard De Baets,
• Abstract summary: 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. 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. In this paper, we apply two transfer learning techniques with Convolutional Neural Networks to a multi-view Congolese wood species dataset. Our results indicate superior accuracy on diverse datasets and anatomical sections, surpassing the results of other methods.
• Score: 8.844437603161198
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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