An Interpretable Deep Learning for Early Detection and Diagnosis of Wheat Leaf Diseases

Abstract

Wheat is a strategic crop that ensures food security and sustainability. Due to climate change, Egypt faces many challenges in the context of wheat disease. This led the government to set goals to achieve sustainable development, represented by Egypt’s Vision 2030. Automated systems using artificial intelligence can help in the early identification of wheat diseases and reduce effort and time as well. In this paper, we introduce a novel deep learning model (Mobile-DNN-Net) to identify wheat diseases. The mobile-DNN-Net model is evaluated using a wheat disease dataset between 15 different classes. Our model (Mobile-DNN-Net) is a hybrid between MobileNet and Deep convolution neural network (DCNN). Also, Grad-Cam techniques into a convolutional neural network (CNN) defect detection model to enhance its transparency and comprehensibility. Grad-CAM precisely identifies the precise regions of the input image that exert the greatest influence on the model's prediction, thereby enhancing the clarity and comprehension of the detection process. The mobile-DNN-Net model is compared to other DL models such as Xception, MobileNet, InceptionV3, and VGG19. The proposed model shows superior results compared to other models.