# Construction of Apple Leaf Diseases Identification Networks Based on Xception Fused by SE Module
## 6 Conclusions and Future Works  
In order to add channel attentions to the CNN network, we proposed a deep fusion scheme of SE module into Xception network, and constructed the SE_Xception network, where SE module is inserted between depth-wise convolution and point-wise convolution of a depth-wise separable convolution layer, and feature channels from the lower level are weighted. The proposed SE_Xception network is better than other classical deep convolutional neural networks in terms of classification accuracy, robustness, and convergence performance on the ATLDD dataset. On the premise of maintaining the network classification performance, we propose a lightweight model SE_miniXception by compressing the width and depth of SE_Xception. Compared with MobileNet and ShuffleNet, which are two popular lightweight models, the number of parameters and FLOPs of SE_miniXception are greatly reduced, so that the model complexity and training time have minimized, and disease prediction speed has been significantly improved, while the classification accuracy of SE_miniXception on ATLDD dataset is still higher than those of MobileNet and ShuffleNet. The experimental results prove that the proposed SE-DEEP block can improve the model's performance on apple leaf disease classification through feature channel adaptive weighting. The SE_miniXception model provides technical support for the design of mobile terminal disease identification system. The scheme of SE-DEEP block and the conclusion of light-weight experiment could provide some reference value for the identification of other crop diseases.  
In the future, we will extend and improve the existing data set, balance the number of images of different disease types in the dataset, and add more images with natural growing background, so as to improve the model performance on disease identification in the actual planting scenario. Furthermore, we plan to add spatial attentions and channel attentions together to the CNN networks to improve their identification accuracy.  
Figure 7: Effects of data augmentation and transfer learning on model convergence performance and recognition accuracy: (**a**) The effect of data augmentation technology on network convergence and classification accuracy without transfer learning; (**b**) The effect of transfer learning on network convergence and classification accuracy with no data augmentation technology.  
**Author Contributions:** Conceptualization, D.H. and X.C.; methodology, X.C. and X.H.; software, X.C. and X.H.; validation, X.C. and X.H.; formal analysis, X.C.; investigation, X.C. and M.W.; resources, X.C. and M.W.; data curation, J.F. and Z.Z.; writing--original draft preparation, X.C. and X.H.; writing--review and editing, X.C. and D.H.; visualization, J.F., Z.Z. and M.W.; supervision, D.H.; project administration, X.C.; funding acquisition, D.H. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by Key Research and Development Program of Shaanxi Province of China (2019 NY-167), and the Undergraduate Training Program for Innovation and Entrepreneurship of Northwest A&F University(X202010712368).  
Data Availability Statement: Data sharing not applicable.  
Appreciations to [PERSON] and [PERSON] for their inspired discussions, to [PERSON] for the sharing of apple leaf disease images, and to [PERSON] for providing apple leaf disease diagnosis standard.  
The authors declare no conflict of interest.