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

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.