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In this study, a differential amplification convolutional neural network (DACNN) was proposed and used in the identification of wheat leaf disease images with ideal accuracy. The branches added between the deep convolutional layers can amplify small differences between the real output and the expected output, which made the weight updating more sensitive to the light errors return in the backpropagation pass and significantly improved the fitting capability. Firstly, since there is no large-scale wheat leaf disease images dataset at present, the wheat leaf disease dataset was constructed which included eight kinds of wheat leaf images, and five kinds of data augmentation methods were used to expand the dataset. Secondly, DACNN combined four classifiers: Softmax, support vector machine (SVM), K-nearest neighbor (KNN) and Random Forest to evaluate the wheat leaf disease dataset. Finally, the DACNN was compared with the models: LeNet-5, AlexNet, ZFNet and Inception V3. The extensive results demonstrate that DACNN is better than other models. The average recognition accuracy obtained on the wheat leaf disease dataset is 95.18%.

Novel method for identifying wheat leaf disease images based on differential amplification convolutional neural network