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Aiming to solve the problem of difficulty in disease recognition and low application rate of recognition model in complex field environment, a corn disease recognition method based on improved CNN was proposed. The influence of data set size and quality on the performance of disease recognition model was discussed. The complicated background images were used and preprocessed by using augmentation, background removal, local fine segmentation and normalized processing methods. Then the CNN structure was designed by using five convolutional layers, four pooling layers and two fully connected layers. The L2 regularization and Dropout strategy were utilized to optimize the network. The results showed that the optimized model achieved an average precision of 97.10% implemented in nine kinds of diseases images with complicated background, which was increased by 9.02 percentage points than that of unimproved CNN method. Compared with the model with original sample, the average precision of the model trained with augmented data was increased by 28.17 percentage points. The removal of complex background can eliminate the influence of environmental noise on the model, thus the performance can be further enhanced, which reached an average precision of 97.96%. After the local fine segmentation of data set, the average precision was raised up to 99.12%. These indicated that CNN needed a large number of representative and high quality training data to identify the target feature. On the basis of improved CNN, a corn field disease recognition system based on mobile was developed, which achieved 83.33% recognition accuracy when verified by experiment in the field. The research could provide guidance for disease recognition and precise prevention and control in corn field.