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

2025-08-12

AI Summary

This study addresses the expert-dependent, low-efficiency challenge in Vietnamese timber species identification by proposing a lightweight deep learning image classification framework for ecological monitoring and sustainable forest management. We introduce the first publicly available benchmark dataset of common Vietnamese timber images and systematically evaluate five architectures—ResNet50, EfficientNet, MobileViT, MobileNetV3, and ShuffleNetV2. Among them, ShuffleNetV2 achieves an average accuracy of 99.29% and F1-score of 99.35% across 20 independent trials, demonstrating superior accuracy-computation trade-offs. Key contributions include: (i) the first standardized, publicly accessible timber image dataset specifically curated for Vietnam; and (ii) empirical validation that lightweight models can attain industrial-grade recognition performance under resource-constrained conditions—enabling feasible real-time, field-deployable species monitoring.

Technology Category

• Computer Vision: Object Detection & Categorization
• Machine Learning: Multi-instance/Multi-view Learning
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

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.

Problem

• Automate Vietnamese timber species identification using deep learning
• Overcome labor-intensive traditional wood classification methods
• Enable real-time species identification in resource-limited settings

Innovation

• Deep learning automates Vietnamese timber species identification
• Custom image dataset with five CNN architectures evaluated
• ShuffleNetV2 achieves 99.29% accuracy efficiently