Abstract
Due to the damage that various tree diseases cause to agricultural fields, there are always different methods to deal with them. This paper proposes a new algorithm to identify three common grapevine diseases: Downey Mildew, Anthracnose, and Powdery Mildew. This new algorithm is based on the Faster Region-based Convolutional Neural Networks (R-CNN) with an Enhanced VGG16 model. The proposed Enhanced VGG16 model can diagnose better these three types of diseases. Experimental results show that the proposed algorithm has 0.53%, 0.912%, 2.759%, and 7.268% improvement in the mean Average Precision (mAP) criterion compared to ResNet50, VGG16, GoogLeNet, and AlexNet networks, respectively. The precision of the proposed method is better than other methods, and the number of layers used in it is acceptable compared to other methods.
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