Improvement of the Efficiency of Grape Leaf Disease Classification Using VGG12 with Wide Convolution Layer

This article presents a model improvement to increase the efficiency of grape leaf disease classification using the VGG12 (Visual Geometry Group with 12 Layers) with Wide Layer model. It is a new model based on the concept of VGG16 (Visual Geometry Group with 16 Layers) and InceptionV3 Block. It aims to present a small convolutional neural network model. It reduces the number of parameters and computational costs but increases the efficiency of grape leaf disease classification. By reducing the number of layers of the VGG16 model from 16 to 12, horizontal feature maps are forwarded instead of hierarchical feature maps between the second to eighth layers. Feature maps are combined to forward the feature maps hierarchically to the next layer. In addition, the filter size was changed from 3×3 to 1×3 and 3×1 to reduce the number of parameters and help the model process faster. Multiple Dilated Convolution was used to obtain more feature maps and did not increase the parameters. The model’s results were evaluated by experimenting with a four-class grape leaf dataset from the PlantVillage dataset, consisting of three classes of diseased grape leaves and one class of healthy grape leaves. The results showed that the proposed model gave an accuracy of 99.95% in classifying grape leaf disease and comparing the proposed model with the models ResNet50, VGG16, InceptionV3, DenseNet121, and MobileNetV2, whose classification accuracies were 64.14%, 91.16%, 97.92%, 97.71%, and 99.69%, respectively. It was found that the model proposed has the highest accuracy, and it uses 1,571,839 parameters.