Research on potato disease identification based on RegNet network

In order to overcome the problems of solidified structure of traditional network model and low recognition rate of potato diseases, five types of potato diseases in PlantVillage dataset are taken as the research object, and the images are randomly zoomed in and out, horizontally flipped, vertically flipped and so on for data enhancement. Then a RegNet network model with a high degree of flexibility is designed using a network-based design space idea, and the PoLy loss function is used to improve RegNet and the attention mechanism is added to predict the potato disease images after data enhancement, and the traditional network models are compared with AlexNet and GoogLeNet. The experimental results show that the improved RegNetX has good performance in potato recognition, the highest accuracy can reach 99.8%, and the model accuracy is higher than AlexNet and GoogLeNet, which can be used as a reference for potato disease recognition.