Rapid identification of wood species using XRF and neural network machine learning

Abstract An innovative approach for the rapid identification of wood species is presented. By combining X-ray fluorescence spectrometry with convolutional neural network machine learning, 48 different wood specimens were clearly differentiated and identified with a 99% accuracy. Wood species identif...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Aaron N. Shugar, B. Lee Drake, Greg Kelley
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/aef542a3a8534e4ba65b56f076949600
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:aef542a3a8534e4ba65b56f076949600
record_format	dspace
spelling	oai:doaj.org-article:aef542a3a8534e4ba65b56f0769496002021-12-02T19:09:30ZRapid identification of wood species using XRF and neural network machine learning10.1038/s41598-021-96850-22045-2322https://doaj.org/article/aef542a3a8534e4ba65b56f0769496002021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96850-2https://doaj.org/toc/2045-2322Abstract An innovative approach for the rapid identification of wood species is presented. By combining X-ray fluorescence spectrometry with convolutional neural network machine learning, 48 different wood specimens were clearly differentiated and identified with a 99% accuracy. Wood species identification is imperative to assess illegally logged and transported lumber. Alternative options for identification can be time consuming and require some level of sampling. This non-invasive technique offers a viable, cost-effective alternative to rapidly and accurately identify timber in efforts to support environmental protection laws and regulations.Aaron N. ShugarB. Lee DrakeGreg KelleyNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Aaron N. Shugar B. Lee Drake Greg Kelley Rapid identification of wood species using XRF and neural network machine learning
description	Abstract An innovative approach for the rapid identification of wood species is presented. By combining X-ray fluorescence spectrometry with convolutional neural network machine learning, 48 different wood specimens were clearly differentiated and identified with a 99% accuracy. Wood species identification is imperative to assess illegally logged and transported lumber. Alternative options for identification can be time consuming and require some level of sampling. This non-invasive technique offers a viable, cost-effective alternative to rapidly and accurately identify timber in efforts to support environmental protection laws and regulations.
format	article
author	Aaron N. Shugar B. Lee Drake Greg Kelley
author_facet	Aaron N. Shugar B. Lee Drake Greg Kelley
author_sort	Aaron N. Shugar
title	Rapid identification of wood species using XRF and neural network machine learning
title_short	Rapid identification of wood species using XRF and neural network machine learning
title_full	Rapid identification of wood species using XRF and neural network machine learning
title_fullStr	Rapid identification of wood species using XRF and neural network machine learning
title_full_unstemmed	Rapid identification of wood species using XRF and neural network machine learning
title_sort	rapid identification of wood species using xrf and neural network machine learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/aef542a3a8534e4ba65b56f076949600
work_keys_str_mv	AT aaronnshugar rapididentificationofwoodspeciesusingxrfandneuralnetworkmachinelearning AT bleedrake rapididentificationofwoodspeciesusingxrfandneuralnetworkmachinelearning AT gregkelley rapididentificationofwoodspeciesusingxrfandneuralnetworkmachinelearning
_version_	1718377109800353792

Rapid identification of wood species using XRF and neural network machine learning

Ejemplares similares