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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 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.

SUBMITTER: Shugar AN

PROVIDER: S-EPMC8413463 | biostudies-literature |

REPOSITORIES: biostudies-literature

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