## 3 Results and Discussion  
### Structural Performance Evaluation of MS-SC-CNN Model  
Firstly, a set of comparative experiments were designed to verify the rationality of the proposed structure of the MS-SC-CNN. In this group of experiments, three kinds of neural networks were set up: CNN, MS-CNN, and MS-SC-CNN. Table 3 shows the results for estimating moisture and nitrogen content in leaves of _Pinus massoniana_ seedlings using these three neural network models with different structures, among which the MS-SC-CNN model had the highest estimation accuracy. In contrast, the CNN model had the lowest accuracy. Specifically, with regard to moisture content estimation, the correlation coefficient between the estimated value and the real value of the MS-SC-CNN model was the highest, improved by 0.001 compared with the MS-CNN model, while the RMSE was reduced by 0.03. In nitrogen content estimation, the correlation coefficient of the MS-SC-CNN model was improved by 0.016 compared with the MS-CNN model, and the RMSE of the MS-SC-CNN model was reduced by 0.005, indicating that the short cut structure's generalization ability could be further improved, and the calculation accuracy of the model was improved by adding a short cut structure. Figure 4 shows the moisture content prediction effect of the three models on the calibration and prediction. All three methods accurately predicted the moisture content, but the prediction effect was different. Consistent with the results in Table 3, CNN was the method with the least ideal detection effect with the selected data set. In Figure 4a, it can be seen that most of the data points are far from the perfect correlation line, which indicates that the prediction result has a large error and was not highly accurate. The other two models' prediction results were similar, although more data points of the MS-SC-CNN were close to the perfect correlation line, which shows that the prediction error of this method is smaller, and it can better predict the moisture content of _Pinus massoniana_ seedlings. Therefore, the best model in terms of its predictive ability was the MS-SC-CNN, followed by the MS-CNN, while the CNN alone had a poor predictive ability. With respect to the moisture content test, the predicted values of the CNN, MS-CNN, and MS-SC-CNN matched the actual values better than the training results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Moisture Content**} & \multicolumn{2}{c}{**Nitrogen Content**} \\ \cline{2-5}  & \(R^{2}\) & _RMSE_ & \(R^{2}\) & _RMSE_ \\ \hline CNN & 0.965 & 0.336 & 0.836 & 0.084 \\ MS-CNN & 0.976 & 0.272 & 0.890 & 0.066 \\ MS-SC-CNN & 0.977 & 0.242 & 0.906 & 0.061 \\ \hline \hline \end{tabular} Notes: CNN is short for the convolutional neural network; MS-CNN is the multi-scale convolutional neural network; MS-SC-CNN is the multi-scale short cut convolutional neural network.  
\end{table} 
Table 3: Results of moisture and nitrogen content in _Pinus massoniana_ seeding leaves using different convolutional neural network models. Figure 5 shows the model estimation results for nitrogen content. In the nitrogen content test, the three prediction models' predictions were less accurate compared with the moisture content prediction, which indicates that the difficulty of the regression task is greater compared with the moisture content test. However, the estimated performance and moisture content of the three models are consistent. Figure 5 shows that a large number of data points deviate from the line of perfect correlation, and that the CNN model prediction is the worst. In contrast, the other two models had a better predictive ability, and when combined with the results in Table 3. It is clear that the prediction of the MS-SC-CNN model is better than that of the MS-CNN model.