Determination of Coniferous Wood’s Compressive Strength by SE-DenseNet Model Combined with Near-Infrared Spectroscopy

Abstract

Rapid determination of the mechanical performance of coniferous wood has great importance for wood processing and utilization. Near-infrared spectroscopy (NIRS) is widely used in various production fields because of its high efficiency and non-destructive characteristics, however, the traditional NIR spectroscopy analysis techniques mainly focus on the spectral pretreatment and dimension reduction methods, which are difficult to maximize use of effective spectral information and are time consuming and laborious. Deep learning methods can automatically extract features; data-driven artificial intelligence technology can discover the internal correlation between data and realize many detection tasks in life and production. In this paper, we propose a SE-DenseNet model, which can realize end-to-end prediction without complex spectral dimension reduction compared with traditional modeling methods. The experimental results show that the proposed SE-DenseNet model achieved classification accuracy and F1 values of 88.89% and 0.8831 on the larch’s test set, respectively. The proposed SE-DenseNet model achieved correlation coefficients (R) and root mean square errors (RMSE) of 0.9144 and 1.2389 MPa on the larch’s test set, respectively. Implementation of this study demonstrates that SE-DenseNet can realize automatic extraction of spectral features and the accurate determination of wood mechanical properties.

Keywords

• near-infrared spectroscopy
• coniferous wood
• compressive strength
• deep learning
• SE-DenseNet