Deep learning for automated identification of Vietnamese timber species: A tool for ecological monitoring and conservation
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| Titel: | Deep learning for automated identification of Vietnamese timber species: A tool for ecological monitoring and conservation |
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| Autoren: | Tianyu Song, Doan Van Duong, Phuong Thi Le, Ton Viet Ta |
| Quelle: | Ecological Informatics, Vol 92, Iss , Pp 103518- (2025) |
| Verlagsinformationen: | Elsevier, 2025. |
| Publikationsjahr: | 2025 |
| Bestand: | LCC:Information technology LCC:Ecology |
| Schlagwörter: | Wood species classification, Deep learning, Convolutional neural network, Lightweight models, Ecological monitoring, Vietnamese timber species, Information technology, T58.5-58.64, Ecology, QH540-549.5 |
| Beschreibung: | 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. |
| Publikationsart: | article |
| Dateibeschreibung: | electronic resource |
| Sprache: | English |
| ISSN: | 1574-9541 |
| Relation: | http://www.sciencedirect.com/science/article/pii/S1574954125005278; https://doaj.org/toc/1574-9541 |
| DOI: | 10.1016/j.ecoinf.2025.103518 |
| Zugangs-URL: | https://doaj.org/article/4257125923264743af8d87a76fca9152 |
| Dokumentencode: | edsdoj.4257125923264743af8d87a76fca9152 |
| Datenbank: | Directory of Open Access Journals |
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Nájsť tento článok vo Web of Science | 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. |
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| ISSN: | 15749541 |
| DOI: | 10.1016/j.ecoinf.2025.103518 |