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...
Saved in:
Main Authors: | Aaron N. Shugar, B. Lee Drake, Greg Kelley |
---|---|
Format: | article |
Language: | EN |
Published: |
Nature Portfolio
2021
|
Subjects: | |
Online Access: | https://doaj.org/article/aef542a3a8534e4ba65b56f076949600 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Development of a rapid field testing method for metals in horizontal directional drilling residuals with XRF sensor
by: Hailin Zhang, et al.
Published: (2021) -
Identification of cinnabar existing in different objects using portable coupled XRF-XRD, laboratory-type XRD and micro-Raman spectroscopy: comparison of the techniques
by: Jingyi Shen, et al.
Published: (2021) -
XRF elemental analysis of inks in South American manuscripts from 1779 to 1825
by: Celina Luízar Obregón, et al.
Published: (2021) -
Using artificial neural networks in estimating wood resistance
by: Miguel,Eder Pereira, et al.
Published: (2018) -
Soil and Plant Nutrient Analysis with a Portable XRF Probe Using a Single Calibration
by: João Antonangelo, et al.
Published: (2021)