Deep Learning for Automated Identification of Vietnamese Timber Species: A Tool for Ecological Monitoring and Conservation

• URL: http://arxiv.org/abs/2508.10938v1
• Date: Wed, 13 Aug 2025 02:54:58 GMT
• Title: Deep Learning for Automated Identification of Vietnamese Timber Species: A Tool for Ecological Monitoring and Conservation
• Authors: Tianyu Song, Van-Doan Duong, Thi-Phuong Le, Ton Viet Ta,
• Abstract summary: In this study, we explore the application of deep learning to automate the classification of ten wood species commonly found in Vietnam.<n>A custom image dataset was constructed from field-collected wood samples, and five state-of-the-art convolutional neural network architectures were evaluated.<n> ShuffleNetV2 achieved the best balance between classification performance and computational efficiency, with an average accuracy of 99.29% and F1-score of 99.35% over 20 independent runs.
• Score: 2.1466764570532004
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate identification of wood species plays a critical role in ecological monitoring, biodiversity conservation, and sustainable forest management. Traditional classification approaches relying on macroscopic and microscopic inspection are labor-intensive and require expert knowledge. In this study, we explore the application of deep learning to automate the classification of ten wood species commonly found in Vietnam. A custom image dataset was constructed from field-collected wood samples, and five state-of-the-art convolutional neural network architectures--ResNet50, EfficientNet, MobileViT, MobileNetV3, and ShuffleNetV2--were evaluated. Among these, ShuffleNetV2 achieved the best balance between classification performance and computational efficiency, with an average accuracy of 99.29\% and F1-score of 99.35\% over 20 independent runs. These results demonstrate the potential of lightweight deep learning models for real-time, high-accuracy species identification in resource-constrained environments. Our work contributes to the growing field of ecological informatics by providing scalable, image-based solutions for automated wood classification and forest biodiversity assessment.

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