Abstract: In order to solve the problems of strong subjective feature extraction and high misrecognition rate in traditional corn disease identification methods, a convolutional neural network is proposed to identify corn diseases. The corn images with 5 types of corn disease images and healthy images were taken as the research objects, and the LeNet model was used for experiments. First, select a training set and a test set for each corn disease image in the ratio of 8∶2. Then, the effects of different convolutional neural network structure settings on accuracy are compared through experimental combination and comparative analysis, and the best parameters are selected. In addition, the Adam algorithm is used instead of the SGD algorithm to optimize the model, the learning rate is adjusted by the exponential decay method, the L2 regular term is added to the cross entropy function, and the Dropout strategy and ReLU excitation function are selected. Finally, a 10-layer CNN network structure is determined. The experimental results show that the maize mosaic disease, gray leaf spot, rust, leaf spot disease and corn health recognition rates are 95.83%, 90.57%, 100%, 93.75%, 100%, and the average recognition rate is 96%. Compared with experimental results, the recognition effect of this model is significantly higher than traditional methods, which provides technical support for the prevention and treatment of corn diseases.

Key words: convolutional neural network, corn image, LeNet model, disease recognition