Performance of convolutional neural network (CNN) and performance influencing factors for wood species classification of Lepidobalanus growing in Korea
Author:
Funder
National Research Foundation of Korea
Korea Forestry Promotion Institute
Publisher
Springer Science and Business Media LLC
Link
https://www.nature.com/articles/s41598-024-69281-y.pdf
Reference54 articles.
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2. United Nations Office on Drugs and Crime (UNODC) Best practice guide for forensic timber identification. (United Nations Office on Drugs and Crime, 2016).
3. Kim, J. H. et al. Comparison of anatomical features in the three Syzygium species. BioResources 16(2), 3631–3642. https://doi.org/10.15376/biores.16.2.3631-3642 (2021).
4. Purusatama, B. D., Febrianto, F., Lee, S. H. & Kim, N. H. Hardness and fracture morphology of reaction wood from Pinus merkusii and Agathis loranthifolia. Wood Sci. Technol. 56(5), 1331–1351. https://doi.org/10.1007/s00226-022-01413-x (2022).
5. Savero, A. M. et al. Characterization of anatomical and non-anatomical properties for the identification of six commercial wood species from Vietnamese plantation forests. Forests 14(3), 496. https://doi.org/10.3390/f14030496 (2023).
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