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

TL;DR

This paper demonstrates that lightweight deep learning models, especially ShuffleNetV2, can accurately and efficiently identify Vietnamese timber species from images, aiding ecological monitoring and conservation efforts.

Contribution

It introduces a novel application of deep learning for automated wood species identification in Vietnam, with a focus on model efficiency and high accuracy.

Findings

ShuffleNetV2 achieved 99.29% accuracy

Deep learning models are effective for real-time species identification

The approach supports ecological monitoring in resource-limited settings

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.