Deep convolutional neural network model for classifying common bean leaf diseases - Dagne Walle Girmaw; Tsehay Wasihun Muluneh (Discover Artificial Intelligence)
Abstract Common bean is one of the most important crops used by Ethiopian farmers for export and local consumption. However, the quality and quantity of this crop are heavily affected by different leaf diseases and affect crop growth. Currently, common bean disease detection is performed through expert visual observation. Disease detection through observation is costly, time-consuming, and inaccurate. As a result, in this paper, a novel deep convolutional neural network model is proposed for the automatic identification of common bean leaf diseases. This research mainly focuses on the identification of three common bean leaf diseases, such as common bean brown leaf spot, common bean leaf rust, and common bean leaf beetle. The proposed model was trained using a total of 1766 images of diseased and healthy common bean leaves. The proposed model has 12 convolutional layers. The classes of the diseases were classified using a softmax classifier. The proposed model achieved a training accuracy of 98%, a validation accuracy of 97.2%, and a testing accuracy of 96%. We retrained the pre-trained models (AlexNet, GoogleNet, VGG16) in a similar simulation environment to make a comparison with the proposed model. Therefore, the proposed model achieved better performance than the pre-trained models.