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Issue 21, 2019
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Cognitive spectroscopy for wood species identification: near infrared hyperspectral imaging combined with convolutional neural networks

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Abstract

From the viewpoint of combating illegal logging and examining wood properties, there is a contemporary demand for a wood species identification system. Several nondestructive automatic identification systems have been developed, but there is room for improvement to construct a highly reliable model. The present study proposes cognitive spectroscopy that combines near infrared hyperspectral imaging (NIR-HSI) with a deep convolutional neural network approach. We defined “cognitive spectroscopy” as a protocol that extracts features from complex spectroscopic data and presents the best results without human intervention. Overall, 120 samples representing 38 hardwood species were scanned using an NIR-HSI camera. A deep learning prediction model was built based on the principal component (PC) images obtained from the PC scores of hyperspectral images (wavelength range: 1000–2200 nm at approximately 6.2 nm interval). The results showed that the accuracy of wood species identification based on 6PC (PC1–PC6) images was 90.5%, which was considerably higher than the accuracy of 56.0% obtained with conventional visible images.

Graphical abstract: Cognitive spectroscopy for wood species identification: near infrared hyperspectral imaging combined with convolutional neural networks

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Publication details

The article was received on 27 Jun 2019, accepted on 28 Sep 2019 and first published on 02 Oct 2019


Article type: Paper
DOI: 10.1039/C9AN01180C
Analyst, 2019,144, 6438-6446

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    Cognitive spectroscopy for wood species identification: near infrared hyperspectral imaging combined with convolutional neural networks

    H. Kanayama, T. Ma, S. Tsuchikawa and T. Inagaki, Analyst, 2019, 144, 6438
    DOI: 10.1039/C9AN01180C

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