Accuracy Enhancement in Apple Leaf Diseases Detection and Classification Using VGG16

In the modern world, predicting the plant diseases is an important job for several reasons which includes the early detection and intervention of loss of crop and ensures to provide heathy fruits and vegetables. This prevents the further more spread of the diseases and intimates the farmers to make strategical and managemental decisions or the necessary actions that are to be taken. The idea of using Machine learning models is that it provides various processing techniques and ensemble models that improves the accuracy and efficiency of the analysis of the diseased plant. The research uses Convolutional Neural Network (CNN) for image recognition, Visual Geometry Group (VGG) for improved performance measures. This model increases the efficiency where it consists of several convolutional layers which are fully connected. This research utilizes the apple and maize plant's dataset to visualize and categorize the plant leaf diseases. This research presents the comparison of the proposed system and all other existing models mainly to highlight the significance of the proposed model.