Identification of Maize Leaf Diseases based on Convolutional Neural Network

Abstract

Abstract The identification and diagnosis of crop leaf disease is of great significance to improve the quality of crop cultivation. Compared with the traditional manual diagnosis method, the automatic identification of crop leaf disease based on computer vision technology has high efficiency and no subjective judgment error. But the traditional image processing technology is affected by different illumination conditions, cross shading. The algorithm’s robustness is affected. Because deep learning dose not need to set learning features manually, which greatly improves the recognition efficiency. In this paper, the two-channel Convolutional Neural Network was constructed based on VGG and ResNet. Taking the maize leaf diseases as research objects, the maize leaf disease data set has been constructed and preprocessed. And the structure and characteristics of AlexNet, VGG and ResNet are introduced respectively. By adjusting the parameters of the two-channel Convolutional Neural Network, the accuracy of identifying the maize leaf disease type in the validation set can reach 98.33%, while the VGG model can reach 93.33%. The classification results on three types of maize leaf diseases show that the two-channel Convolutional Neural Network has a better performance than the single AlexNet model. Three kinds of leaf disease data sets (big spot, gray leaf spot and rust ) are downloaded from the “kaggle”platform.