An optimized dense convolutional neural network model for disease recognition and classification in corn leaf

Abstract

An optimized dense convolutional neural network (CNN) architecture (DenseNet) for corn leaf disease recognition and classification is proposed in this paper. Corn is one of the most cultivated grain throughout the world. Corn crops are highly susceptible to certain leaf diseases such as corn common rust, corn gray leaf spot, and northern corn leaf blight are very common. Symptoms of these leaf diseases are not differentiable in their nascent stages. Hence, the current research presents a solution through deep learning so that crop health can be monitored and, it will lead to an increase in the quantity as well as the quality of crop production. The proposed optimized DenseNet model has achieved an accuracy of 98.06%. Besides, it uses significantly lesser parameters as compared to the various existing CNN such as EfficientNet, VGG19Net, NASNet, and Xception Net. The performance of the optimized DenseNet model has been contrasted with the current CNN architectures by considering two (time and accuracy) quality measures. This study indicates that the performance of the optimized DenseNet model is close to that of the established CNN architectures with far fewer parameters and computation time.