## 3 Results  
The number of parameters of a network model determines the complexity of its computation. The huge amount of computation leads to the existing mature deep learning models cannot be directly applied to mobile. Therefore, researching lightweight neural network structures is the first task in the goal of reducing the number of model parameters. If some modules of the deep neural network model are directly reduced, it will definitely affect the accuracy of potato leaf image feature extraction and lead to the decrease of potato disease recognition model accuracy. Based on the above analysis of the residual network structure and the depth-separable convolutional network model, the number of model parameters is reduced by using the depth-separable convolutional technique for the characteristics of potato disease leaf images. New lightweight methods such as the point-by-point convolution technique and inverse residual network structure are used to design and adjust the number of ascending and descending channel multiples, the number of convolution kernels, and the number of module repetitions, which improve the model recognition accuracy and significantly reduce the number of model parameters and computational complexity. The overfitting problem is also solved by adding a dropout layer, regularization process, and data enhancement. Given the combined with the characteristics of the algorithms proposed in this paper, an Intel i9-9900 k CPU 3.6 GHz processor, memory of 32 GBRAM, and graphics card NVIDIA GeForce RTX 2080 Ti with 11 GB of video memory were selected for the training experimental equipment. Based on the performance characteristics of different algorithms, it is also possible to train a sufficiently good prediction model in this single GPU environment using CUDA technology. In this paper, several deep learning convolutional neural network models have been trained using the same dataset to identify different types of potato leaf diseases. We first load the original model using Python language to build a pre-trained model without classifier. Then we add a global average pooling layer and a fully connected layer. Finally, a SoftMax function based classifier is added according to the disease leaf type. The training process uses the weights pre-trained on ImageNet for network parameter updates. The loss function of the loss layer uses the cross-entropy loss function. The optimizer is chosen to update the weights and biases using the Adam (Adaptive Moment Estimation) optimization algorithm [27]. Adam is a method for calculating the adaptive learning rate for each parameter or weight, and it is an extension of the gradient descent optimization algorithm, gaining the advantages of both the AdaGrad and RMSProp algorithms. After tuning the hyper parameters such as learning rate of each model several times, the experimental results are statistically shown in Table 1. As can be seen from the table, the number of parameters of MobileNet series models is significantly smaller than that of residual networks and other deep learning networks, and its Top-1 accuracy is over 93%. MobileNetV2 is very similar to the original MobileNet, except that it uses inverted residual blocks with bottleneck features. It has a drastically lower parameter count than the original MobileNet. In this paper, the trained models are converted to H5 files. The model deployment module is built to save the trained neural network models into the Django framework. Django is an open source web application framework written in Python that adopts the framework structure pattern of MTV-Model M, View V, and Template T. While transferring the leaf images, the model also adds category labels to the images with category labels. The leaf disease category is identified and then the returned parameters are entered into the views function, the POST port number and the HOST local address of the settings function are set, and finally a static page with that image label is generated, thus realizing a potato early leaf disease identification system.