# Construction of Apple Leaf Diseases Identification Networks Based on Xception Fused by SE Module
## 1 Introduction  
Automatic identification of apple leaf diseases is extremely helpful to improve the management of apple orchard and provides a good indication for the early warning and prevention of apple diseases. Traditional expert diagnosis on apple leaf disease takes high cost and has subjective misjudgment risk. Therefore, with the development of computer vision technology, scholars start to sue image processing technology to carry out researches on plant disease identification based on image feature extraction and modelling. However, image feature extraction used in image processing mainly relies on manual selection and design [1, 2, 3, 4], which is time-consuming and laborious and the accuracy of image recognition needs to be improved. In addition, models for such methods are designed and trained with a specific dataset so it is hard to transfer to new tasks. In recent years, deep learning technology has developed rapidly. Especially, Convolutional Neural Networks (CNN) has been widely applied to plant leaf disease recognition [5, 6, 7, 8, 9, 10]. Its end-to-end propertyexempts it from the need for complex image preprocessing and manual feature selection, thus it is more efficient than traditional methods and has better generalization ability. Classic CNN models have been trained and fine-tuned on large dataset, so some studies used transfer learning for plant disease identification. Some of them applied minor modification on the classical CNN network structure. For example, [PERSON] et al. [21] improved the AlexNet and collected an experimental data set composed by PlantVillage dataset [22] and some images collected from the Internet. Their dataset was divided into a training set and a test set after data augmentation. The average classification accuracy on the test set is 99.56%. [PERSON] et al. [23] trained an AlexNet model for tomato disease classification based on transfer learning. The model could quickly and accurately classify 10 categories of tomato leaf diseases, with an average classification accuracy of 95.62% on the validation set. In 2018, [PERSON] et al. [13] trained AlexNet and GoogleNet networks by using transfer learning technology for Camellia oleifera diseases identification. On the dataset with four kinds of diseases and healthy leaves, the recognition rate was 96.53%, and their results indicated that transfer learning could accelerate the network convergence and improve the classification performance. [PERSON] [11] compared the existing CNN to conduct experiments on the extended version of PlantVillage dataset by using transfer learning. Experiment results proved that VGG had the best classification and recognition accuracy, up to 99.53%. In addition, the experimental results showed that the recognition rate of model trained with images of laboratory background was 32.42% lower than those trained by images with natural growing background on the test set with both natural growing background and laboratory background images. There are also some studies that improve the CNN models for plant leaf disease identification. For example, [PERSON] et al. [12] proposed a CNN model based on AlexNet to identify four common apple leaf diseases and healthy leaves in 2018, and the model could achieve an average recognition accuracy of 97.62%. In 2019, [PERSON] et al. [14] designed a convolutional neural network structure based on LeNet-5 architecture, which was used to identify three kinds of apple leaf diseases and healthy leaves. The recognition rate reached 98.54%. In 2019, [PERSON] et al. [18] proposed a network for real-time detection of apple leaf diseases, and the disease recognition rate of the proposed VGG-INCEP model reached 97.14%. In 2020, [PERSON] et al. [24] proposed a CNN model by fusing dense blocks of Densenet into Xception, the model could identify five apple leaf diseases and healthy leaves, and the average recognition rate was 98.35%.