Deep learning for automated identification of Vietnamese timber species: A tool for ecological monitoring and conservation

Tianyu Song, Doan Van Duong, Phuong Thi Le, Ton Viet Ta,
Deep learning for automated identification of Vietnamese timber species: A tool for ecological monitoring and conservation,
Ecological Informatics,
Volume 92,
2025,
103518,
ISSN 1574-9541,
https://doi.org/10.1016/j.ecoinf.2025.103518.
(https://www.sciencedirect.com/science/article/pii/S1574954125005278)
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.
Keywords: Wood species classification; Deep learning; Convolutional neural network; Lightweight models; Ecological monitoring; Vietnamese timber species