用轻量深度学习自动识别越南10种木材，准确率达99.3%
Deep Learning for Automated Identification of Vietnamese Timber Species: A Tool for Ecological Monitoring and Conservation
- 用5种CNN模型对比测试，选ShuffleNetV2最佳
- 20次独立实验平均准确率99.29%，F1-score达99.35%
- 适合野外资源有限环境的实时物种鉴定
准确识别木材物种对生态监测、生物多样性保护和可持续森林管理至关重要。传统依赖宏观与显微观察的分类方法耗时且需专业知识。本研究探索深度学习在自动化分类越南常见10种木材中的应用。构建了基于实地采集样本的定制图像数据集，评估了五种先进卷积神经网络架构：ResNet50、EfficientNet、MobileViT、MobileNetV3 和 ShuffleNetV2。其中，ShuffleNetV2 在分类性能与计算效率之间取得最佳平衡，20次独立运行中平均准确率达99.29%，F1-score为99.35%。结果表明，轻量级深度学习模型可在资源受限环境下实现高精度、实时的物种识别。本工作为生态信息学领域提供了可扩展的图像驱动自动化木材分类与森林生物多样性评估方案。
原文摘要 · Abstract (English)
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.
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