## 4 Conclusions  
The MS-SC-CNN model was proposed to realize the non-destructive measurement of moisture and nitrogen content in leaves of _Pinus massoniana_ seedlings. The model replaced the traditional convolutional blocks with one-dimensional multi-scale convolutional blocks, which improved the resistance to over-fitting the model and its accuracy. The short cut structure helped limit the number of output channels, improved the available depth of the model, and meant the target layer changed from crossing a single layer to crossing to the end, ultimately crossing a multi-layer network. It also retained the ability for deep gradient transmission, and prevented the model from over-fitting to the training data. By combining MS-CNN with the short cut method, the moisture and nitrogen content of _Pinus massoniana_ leaves was measured quickly, non-destructively, and accurately.  
Although the small number of training samples used in this study may limit the predictive ability of some models, the multi-scale short cut neural network can still exhibit good prediction performance when trained with a small number of samples. In this study, the correlation coefficient was 0.998, and the RMSE was 0.087 for the prediction of moisture content, and the correlation coefficient and RMSE was 0.906 and 0.061 for the prediction of nitrogen content, respectively. Compared with the results of several other neural network models, the model we proposed had the best performance in predicting moisture and nitrogen content in leaves of _Pinus massoniana_ seedlings, which can provide an accurate data basis for the cultivation and management of _Pinus massoniana_ seedlings.  
In addition, this study also provided some inspiration for our future work. For example, we can access the performance of the MS-SC-CNN method in another open dataset for other problems or can analyze the generation and complexity of this method and operation time cost.  
\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 PLSR & 0.898 & 0.677 & 0.771 & 0.127 \\ SVR & 0.902 & 0.705 & 0.748 & 0.132 \\ ANN & 0.932 & 0.556 & 0.768 & 0.127 \\ MS-SC-CNN & 0.977 & 0.242 & 0.906 & 0.061 \\ \hline \hline \end{tabular} Notes: For moisture content measurement, PLSR, SVR, and ANN models are taken from the literature [21], and the PLSR, SVR, and ANN models for nitrogen content measurement are taken from the literature [5].  
\end{table}
Table 4: Results of the moisture and nitrogen content in _Pinus massoniana_ leaves using different regression models.  
**Author Contributions:** All authors designed this work; [PERSON], [PERSON] and [PERSON] contributed equally to this work. [PERSON] and [PERSON] carried out the experiments and validation of this work; [PERSON], and [PERSON] wrote original draft preparation; [PERSON] and [PERSON] reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Program of China, grant number 31570714, and the 2019 Jiangsu Province Key Research and Development Plan by the Jiangsu Province Science and Technology Agency, grant number BE2019112.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.