Pine wood species identification based on random forest transformer and near infrared spectroscopy

In the wood trade market, inferior timber is frequently mislabeled as a superior timber for higher profits. To effectively avoid this and circumvent unnecessary economical losses, this study proposes a pine species identification based on Random Forest Transformer and near infrared spectroscopy to identify the three kinds of pine wood, namely, Pinus koraiensis (red pine), Pinus sylvestris (scotch pine) and Larix gmelinii (larch). Preprocessing methods were applied to the spectra data of the pine wood samples. Feature extraction of the near infrared spectroscopy was done with Random Forest algorithm. Then the identification models of pine woods were developed based on the Random Forest Transformer, Linear Discriminant Analysis (LDA), Support Vector Machines (SVM) and Convolution Neural Networks (CNN). Results showed that the method proposed for pine species identification could quickly and effectively determine the category of pine wood with discrimination accuracy of 98.39 %, which was improved by 3.23 %, 1.62 % and 1.62 % compared with the results of LDA, SVM, and CNN, respectively. Furthermore, the proposed method holds considerable scientific significance and reference value for non-destructive testing applications, not only in the identification of other wood species but also in fields such as traditional Chinese medicine, food science, and agriculture.