id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
brj-23254	Ma, Te; Kimura, Fumiya; Tsuchikawa, Satoru; Kojima, Miho; Inagaki, Tetsuya	Validation Study on the Practical Accuracy of Wood Species Identification via Deep Learning from Visible Microscopic Images	2024	14	.pdf	application/pdf	5804	288	49	DOI: 10.15376/biores.19.3.4838-4851 Keywords: Wood species identification; Microscopic cross-sectional images; Convolutional neural networks (CNN); Practical accuracy; Interactive platform; Web-based identification Contact information: a: Graduate School of Bioagricultural Sciences, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan; b: Forestry and Forest Products Research Institute, Matsunosato, In the context of addressing illegal logging practices and conducting comprehensive wood property analyses, there is a pressing contemporary requirement for the advancement and implementation of a machine learning- based systems dedicated to wood species identification.	cache/brj-23254.pdf	txt/brj-23254.txt