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Diseases of grape leaves are destructive because they spread very quickly. Therefore, they should be detected and tackled fast. This study proposes a novel Enhanced VGG16 model for detecting, identifying, and classifying three widespread diseases in grape leaves. These diseases include Downey Mildew, Anthracnose, and Powdery Mildew. The data are automatically gathered with the assistance of photos taken with a quadcopter from the grapevine garden. The main central system analyzes the images received from the quadcopter to identify diseases. If it finds any of the three diseases, it sends the command to the hexacopter to spray pesticide into the grapevine garden location where the disease is found. The proposed method using the Enhanced VGG16 model with Faster Region-based Convolutional Neural Networks (R-CNN) is compared with different networks, including VGG16, GoogLeNet, ResNet50, and AlexNet. The experimental results on the grape leaf diseases demonstrated that the proposed method achieves the mean Average Precision) mAP (criterion improvements of 0.53%, 0.912%, 2.759%, and 7.268% compared with the ResNet50, VGG16, GoogLeNet, and AlexNet networks, respectively. Also, the average accuracy of the proposed Enhanced VGG16 model is 99.6%, which is 0.437–1.91% higher than other models. The Enhanced VGG16 has the best precision, and the number of layers is acceptable, although it is not less.
Mousavi et al. (Sat,) studied this question.