Work

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

Year: 2025

Type: article

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.

Source: Ecological Informatics

DOI: 10.1016/j.ecoinf.2025.103518

Authors: Tianyu Song, Doan Van Duong, Phuong Thi Le, Ton Viet Ta

Institutions: Kyushu University, Thai Nguyen University, Thai Nguyen University of Agriculture and Forestry

Language: English

Topic: Wood and Agarwood Research

Subfield: Organic Chemistry

Field: Chemistry

Domain: Physical Sciences

SDG: Life on land

Open Access status: gold

Estimated APC paid: $2,770