## 4 Results and Discussion
### Modeling Results
#### 4.2.3 Numerical Regression Results of the Mechanical Strength  
Finally, a quantitative regression model of mechanical strength values was established, and PLSR, SVR, ELM, DenseNet, and SE-DenseNet algorithms were used for comparison. The number of PLS factors selected by PLSR was 3; SVR penalty factor C was 1.5, and sigmoid kernel function was selected with a kernel function coefficient gamma of 0.001; the number of ELM hidden nodes was 9; the number of layers in the SE-DenseNet model is 42; and dimension transformation rate \(\beta\) is 8. The cross-validation set and test set prediction results of the SE-DenseNet model are shown in Figure 17 below. Table 8 provides a comparison of various modeling techniques and the modeling results for various tree species.  
As shown in Figure 17, the SE-DenseNet model has achieved good results on the cross-validation and test set of three coniferous woods. According to Table 8, ELM is slightly superior to PLSR and SVR in traditional methods. The DenseNet model was comparable to the ELM model in larch and hemlock but showed a big improvement in monogolica. After the introduction of the SE module, the SE-Densenet model was established to obtain the optimal prediction performance for the three species. The RP of larch, hemlock, and monogolica were 0.9144, 0.8957, and 0.8950, respectively. Larch had a relatively better predictive performance, which may be due to the spectral pretreatment for larch. The above  
Figure 17: SE-DenseNet for three coniferous wood’s mechanical strength regression: (**a**) Cross validation and prediction results for larch; (**b**) Cross validation and prediction results for hemlock; (**c**) Cross validation and prediction results for monogolica.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Tree Species**} & \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Cross-Validation Set**} & \multicolumn{2}{c}{**Test Set**} \\ \cline{3-6}  & & **RMSECV: MPa** & **RCV** & **RMSEP: MPa** & **RP** \\ \hline \multirow{4}{*}{larch} & WT + MSC, CARS + LLE, PLSR & 1.6031 & 0.8523 & 1.6658 & 0.8498 \\  & WT + MSC, CARS + LLE, SVR & 1.4019 & 0.8765 & 1.4165 & 0.8733 \\  & WT + MSC, CARS + LLE, ELM & 1.4302 & 0.8722 & 1.4387 & 0.8679 \\  & WT + MSC, DenseNet & 1.3371 & 0.8859 & 1.3582 & 0.8768 \\  & **WT + MSC, SE-DenseNet** & **1.2636** & **0.9107** & **1.2389** & **0.9144** \\ \hline \multirow{4}{*}{hemlock} & WT + MSC, CARS + LLE, PLSR & 1.6215 & 0.8485 & 1.6659 & 0.8368 \\  & WT + MSC, CARS + LLE, SVR & 1.4348 & 0.8542 & 1.5274 & 0.8495 \\  & WT + MSC, CARS + LLE, ELM & 1.3852 & 0.8659 & 1.3518 & 0.8705 \\  & WT + MSC, DenseNet & 1.3055 & 0.8726 & 1.3243 & 0.8717 \\  & **WT + MSC, SE-DenseNet** & **1.1975** & **0.9117** & **1.2293** & **0.8957** \\ \hline \multirow{4}{*}{mongolica} & WT + MSC, CARS + LLE, PLSR & 1.4898 & 0.8577 & 1.5546 & 0.8465 \\  & WT + MSC, CARS + LLE, SVR & 1.5364 & 0.8469 & 1.4966 & 0.8541 \\ \cline{1-1}  & WT + MSC, CARS + LLE, ELM & 1.2895 & 0.8698 & 1.2991 & 0.8684 \\ \cline{1-1}  & WT + MSC, DenseNet & 1.2128 & 0.9015 & 1.2376 & 0.8874 \\ \cline{1-1}  & **WT + MSC, SE-DenseNet** & **1.1664** & **0.9207** & **1.2244** & **0.8950** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of results of numerical regression methods.  
results indicate that NIRS combined with SE-DenseNet can be utilized for the accurate determination of coniferous wood's compressive strength, which is of great significance to the rational processing and utilization of wood.