Wood is an indispensable non-renewable resource and plays an important role in our life.
So, it is a necessity to apply accurate classification techniques. In this paper, a novel model …

Effective development and utilization of wood resources is critical. Wood modification
research has become an integral dimension of wood science research, however, the …

With the rapid development of deep learning techniques, convolutional neural networks
have been widely used in the field of spectroscopy. In this paper, a bilinear branching …

Previous reports have shown that wood species identification result based on near-infrared
(NIR) spectroscopy was intimately entwined with spectra preprocessing. However, there is …

Pine nuts are not only the important agent of pine reproduction and afforestation, but also
the commonly consumed nut with high nutritive values. However, it is difficult to distinguish …

The swift and non-destructive classification of wood species holds crucial significance for the
utilization and trade of wood resources. Portable near-infrared (NIR) spectrometers have the …

Rapid determination of the mechanical performance of coniferous wood has great
importance for wood processing and utilization. Near-infrared spectroscopy (NIRS) is widely …

In our previous study, we investigated the use of ensemble models based on LeNet and
MiniVGGNet to classify the images of transverse and longitudinal surfaces of five Korean …

To address the issue of limited expressive ability and performance degradation of the model
caused by the limited depth and width of the CNN because of low computational overhead …

Lumber species identification is an important issue for the wood industry. In this study, three
types of neural networks (artificial neural network (ANN), deep neural network (DNN), and …