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Predicting the air-dry density of black walnut based on NIR analysis.

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Title: Predicting the air-dry density of black walnut based on NIR analysis.
Authors: Ren, Zi-Rui1 (AUTHOR) renzirui0676@163.com, Luo, Li1 (AUTHOR) luoli0044@163.com, Na, Bin1 (AUTHOR) nabin8691@126.com
Source: Holzforschung: International Journal of the Biology, Chemistry, Physics, & Technology of Wood. Oct2023, Vol. 77 Issue 10, p784-792. 9p.
Subjects: Machine learning, Walnut, Nondestructive testing, Computer engineering, Density, Near infrared radiation
Abstract: The combination of computer technology and non-destructive testing technology can facilitate the development of forestry in a more intelligent direction. In this paper, a Shapley additive explanations (SHAP)-based method is used to analyse the importance of band features in the near-infrared spectrum of black walnut wood, which ranges from 900 to 1650 nm. The spectral data from the SHAP analysis are fed into an integrated framework of machine learning algorithms based on four different theories. In the comparison tests, three different pre-processed NIR spectral data are entered into the integrated framework. The result of the SHAP analysis shows that the wavelengths that are positively correlated with the air-dry density of black walnut are 1354.59, 1400.23, 1341.51, 1426.26, 1413.25 nm. The model predictions show that the SHAP-treated spectral data outperformed the other two treatments for each model. For the SHAP-treated spectral data, the KNN model gives the best results with an R2 of 0.947 and an MSE of 0.0010. [ABSTRACT FROM AUTHOR]
Copyright of Holzforschung: International Journal of the Biology, Chemistry, Physics, & Technology of Wood is the property of De Gruyter and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 172849685
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Predicting the air-dry density of black walnut based on NIR analysis.
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  Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Walnut%22">Walnut</searchLink><br /><searchLink fieldCode="DE" term="%22Nondestructive+testing%22">Nondestructive testing</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+engineering%22">Computer engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Density%22">Density</searchLink><br /><searchLink fieldCode="DE" term="%22Near+infrared+radiation%22">Near infrared radiation</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The combination of computer technology and non-destructive testing technology can facilitate the development of forestry in a more intelligent direction. In this paper, a Shapley additive explanations (SHAP)-based method is used to analyse the importance of band features in the near-infrared spectrum of black walnut wood, which ranges from 900 to 1650 nm. The spectral data from the SHAP analysis are fed into an integrated framework of machine learning algorithms based on four different theories. In the comparison tests, three different pre-processed NIR spectral data are entered into the integrated framework. The result of the SHAP analysis shows that the wavelengths that are positively correlated with the air-dry density of black walnut are 1354.59, 1400.23, 1341.51, 1426.26, 1413.25 nm. The model predictions show that the SHAP-treated spectral data outperformed the other two treatments for each model. For the SHAP-treated spectral data, the KNN model gives the best results with an R2 of 0.947 and an MSE of 0.0010. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Holzforschung: International Journal of the Biology, Chemistry, Physics, & Technology of Wood is the property of De Gruyter and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1515/hf-2023-0036
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 9
        StartPage: 784
    Subjects:
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Walnut
        Type: general
      – SubjectFull: Nondestructive testing
        Type: general
      – SubjectFull: Computer engineering
        Type: general
      – SubjectFull: Density
        Type: general
      – SubjectFull: Near infrared radiation
        Type: general
    Titles:
      – TitleFull: Predicting the air-dry density of black walnut based on NIR analysis.
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          Name:
            NameFull: Ren, Zi-Rui
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            NameFull: Luo, Li
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          Name:
            NameFull: Na, Bin
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          Dates:
            – D: 01
              M: 10
              Text: Oct2023
              Type: published
              Y: 2023
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              Value: 77
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              Value: 10
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            – TitleFull: Holzforschung: International Journal of the Biology, Chemistry, Physics, & Technology of Wood
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