Deep Learning for Automated Identification of Vietnamese Timber Species: A Tool for Ecological Monitoring and Conservation
This provides a scalable tool for ecological monitoring and conservation in resource-constrained environments, though it is incremental as it applies existing methods to a new dataset.
The study tackled the problem of automating wood species identification for ecological monitoring by applying deep learning to classify ten Vietnamese timber species from images, achieving up to 99.29% accuracy with ShuffleNetV2.
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.