# Construction of Apple Leaf Diseases Identification Networks Based on Xception Fused by SE Module
## 1 Introduction
Their experimental results showed that compared with using laboratory background as the training set, using images with complex background as the training set could greatly improve the disease identification accuracy of the model (about 14%). CNN networks have a global receptive field when extracting features, giving more attention to the principle features of a given task could improve the model performance. This is one of the major studies in the field of computer vision research [25]. The study shows that the spatial attention of the models can be improved by integrating the learning mechanism into CNNs, so as to improve the performance of the CNNs [26; 27; 28; 29; 30]. [PERSON] et al. proposed the SE module [25] to add channel attentions to CNNs and it has been widely used. The SE module has a simple structure and only a few parameters. It can be used as an additional module of any CNN network to introduce channel attention to the network, thus improving the network feature extraction ability. There are a few works that use an SE module to add channel attentions to CNN models to improve network feature extraction ability. In 2020, [PERSON] et al. [31] proposed the SE-Inception model by integrating the SE (Squeeze and Excitation) module with the Inception to classify Solanaceae diseases with an average recognition accuracy of 98.29%. [PERSON] et al. [32] integrated the SE module [25] into ShuffleNet [33] and carried out disease identification on grape disease from the PlantVillage dataset, with an average recognition rate of 99.14%. The above studies have proved that CNNs are effective in solving the plant disease identification task, and the integration of the SE module into the CNN networks can improve the disease identification ability of the networks. The SE module can be added to any network to add channel attention to the network. More possible ways to add an SE module to existing CNN networks can be explored beyond the four approaches suggested by the authors of the SE module. With the popularity of mobile devices, CNN networks can be embedded into mobile applications to provide growers with the ability of disease identification anytime and anywhere. The mobile application requires the network to have a small parameter number, low memory usage, low computation, and faster recognition speed. Existing CNN networks with large quantity of parameters are hard to deploy into the mobile devices. Therefore, we need to look for (or design) lightweight networks with high recognition accuracy. On the one hand, we can verify the performance of existing lightweight networks on the apple disease identification task. On the other hand, the network compression experiment can also be carried out on the existing CNN network with a good recognition rate in the apple disease recognition task. The main contributions of this paper are summarized as follows:  
* In order to meet the application requirements of apple leaf disease identification in a natural growing scenario, we built a dataset with 2975 images of five apple leaf diseases and healthy leaves, where images with different backgrounds were shot in different time periods. * We propose a new method called SE-DEEP to deeply fuse the SE module with a depth-wise separable convolution layer, where the SE module is inserted between the depth-wise convolution and the pointwise convolution of the depth-wise separable convolution block, therefore, the feature channels from the lower layers can be directly weighted. This makes the model more sensitive to the principal features that are useful for the classification task. Then, we used SE-DEEP to integrate the SE module into Xception network to propose the SE_Xception network. * To investigate the influence of depth and width on network performance, we designed an experiment to compress the proposed SE_Xception network to get the lightweight network, named SE_miniXception. In the compression experiment we compared two networks by either compressing the depth or compressing the width.