Narrow Results

The early detection of plant diseases by means of precise or automated detection techniques can improve the quality of food production and reduce economic losses. In this study, a deep convolutional neural network (Deep CNN) was devised to automatically and accurately identify leaf diseases in Cucumis Melo L. The deep CNN model used 1.776 sample images of healthy Cucumis Melo L. leaves and leaves infected with anthracnose, downy mildew, and powdery mildew for training and 198 random test images for the identification accuracy. We also conducted experiments comparing the identification results on our self-designed CNN model with three standard AlexNet, VGG16, and VGG19 models based on the same image dataset. The training experiment results with and without data augmentation while keeping the same parameters of the simulation showed that the accuracy of the self-designed CNN, AlexNet, VGG16, and VGG19 models were 93.58%, 90.26%, 92.86%, and 92.50%, respectively. The results confirmed the feasibility and effectiveness of our proposed CNN model for automatic leaf disease detection in the smart farming of Cucumis Melo L.