## 4 Discussion  
The incidence of crop pests and diseases tends to follow a specific pattern in terms of both location and timing, however, it also exhibits a degree of disorder, displaying variations in both space and time, including regional disparities and unpredictable, sudden, and unstable occurrences. Accurate identification and appropriate control measures for crop pests and diseases can not only ensure crop production, but also effectively reduce the amount of pesticides used and the various hazards caused by pesticides. Current pest and disease data monitoring is an important part of the agricultural monitoring system. Research on crop pest and disease problems has mostly used image processing techniques and machine learning algorithms for effective detection and has achieved more and better results to realize effective identification and extraction of various types of crop information. However, it is very difficult to collect images of crop pests and diseases in actual production. The crop pest images collected in the field are interfered by various factors, including complex backgrounds, changing lighting conditions, and being overlapped and obscured, especially when the foreground target and background leaf color are extremely similar, which brings great difficulties to the multi-class species target recognition. Traditional machine learning algorithms cannot extract the image recognition features related to pests and diseases from the complex and variable background environment, and the established crop pest and disease recognition models are not scalable. Some recognition models can only recognize a single crop pest target, and such models can hardly meet the actual production needs. Moreover, the existing deep learning recognition models for crop pests and diseases have a huge number of parameters, and it is difficult to implement the application technology development on the mobile APP with the heavy computation and the huge model volume. Therefore, it becomes extremely important to optimize the model of convolutional neural network while improving the scale and representativeness of the number of crop pest and disease image samples. Potatoes are annual plants, usually planted in spring and harvested in fall/autumn. We collected images of the leaves mainly in the summer. The data acquisition and preprocessing are actually the most time consuming and important. It is essential to collect image data of different potato diseases and their leaves at different periods of lesions. Due \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **Size** & **Top-1 Accuracy** & **Top-2 Accuracy** & **Depth** \\ \hline Xception & 88 MB & 0.92 & 0.945 & 126 \\ VGG16 & 528 MB & 0.901 & 0.92 & 23 \\ VGG19 & 549 MB & 0.91 & 0.923 & 26 \\ ResNet50 & 98 MB & 0.937 & 0.943 & - \\ ResNet101 & 171 MB & 0.932 & 0.936 & - \\ ResNet152 & 232 MB & 0.938 & 0.940 & - \\ ResNet50V2 & 98 MB & 0.93 & 0.94 & - \\ ResNet101V2 & 171 MB & 0.941 & 0.95 & - \\ ResNet152V2 & 232 MB & 0.945 & 0.953 & - \\ InceptionV3 & 92 MB & 0.932 & 0.948 & 159 \\ InceptionResNetV2 & 215 MB & 0.934 & 0.955 & 572 \\ MobileNet & 16 MB & 0.93 & 0.949 & 88 \\ MobileNetV2 & 14 MB & 0.94 & 0.953 & 88 \\ DenseNet121 & 33 MB & 0.93 & 0.936 & 121 \\ DenseNet169 & 57 MB & 0.915 & 0.942 & 169 \\ DenseNet201 & 80 MB & 0.92 & 0.938 & 201 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Identification statistics of different deep learning models. The Top-1 and Top-2 accuracy refers to the performance of the model on the validation dataset. This includes activation layers, batch normalization layers, etc. Depth refers to the topological depth of the network, which is the number of layers with parameters. to the widespread use of pesticides, it is very difficult to collect images of different types of potato diseases at different times of the year in a sufficiently size quantity and at the same time in each plantation area.