Abstract: Traditional image recognition methods have low accuracy in recognizing maize disease images,and convolutional neural networks have a good effect on image recognition.However,the network model has a large amount of calculation and a large amount of parameters,making it difficult to rely on mobile devices with limited computing power to popularize and use in small sample applications.Therefore,this paper aims to improve the accuracy of maize disease images,reduce network parameters and model size,and proposes a convolutional neural network model that combines migration learning and model compression for maize disease recognition.In order to improve the generalization of the model,the data set is enhanced and the structure of convolutional neural network based on transfer learning is constructed.In this paper,the migration recognition of common maize disease images is carried out by using the improved VGG16-Inception network model pre-trained on ImageNet through transfer learning.Experimental results show that the average recognition accuracy of maize disease images using transfer learning is 93.38% on ImageNet data set.After the migration,the model is compressed by channel pruning and knowledge distillation,and the compressed model is used to recognize the maize disease image by transfer learning.Experimental results show that the average recognition accuracy of corn disease image after compression reaches 92.40%,the accuracy is reduced by 0.98%,the model size is compressed from 73.90 MB to 9.45 MB,and the number of parameters is reduced by 87.80%.The proposed method can ensure the recognition accuracy in small sample scenarios and further realize the model lightweight.

Key words: Convolutional neural network, Maize disease image, Transfer learning, Image identification, Model compression