LAUSR

Crop diseases are a major threat to food security. Identifying the diseases rapidly is still a difficult task in many parts of the world due to the lack of the necessary infrastructure. The accurate identification of crop diseases is highly desired in the field of agricultural information. In this study, we propose a deep convolutional neural network (CNN)-based architecture (modified LeNet) for maize leaf disease classification. The experimentation is carried out using maize leaf images from the PlantVillage dataset. The proposed CNNs are trained to identify four different classes (three diseases and one healthy class). The learned model achieves an accuracy of 97.89%. The simulation results for the classification of maize leaf disease show the potential efficiency of the proposed method.