A Novel Framework of Apple Leaf Disease Detection using Convolutional Neural Network

Apple become an important part of our staple diet. The farmers are facing severe problems due to disease caused on its leaf. Lot of crops have been wasted due to these phenomena. Even though there are lot of other diseases Scab, Black rot and Cedar are three important type of disease which cause severe damage to the leaf of apple. For the development of this particular industry it is important to reduce the infection which is spread on this apple leaf. It is important to detect the disease of leaf early so that the above process can be accomplished. In the current scenario there is no such technology which guarantee high recognition rate in the case of disease caused on leaf of apple. In this paper author tries to deliver an approach which ensures all issues mentioned above and it is achieved by using Deep Convolutional Neural Network (DCNN). Here in this model apple leaf disease is detected by using Deep Convolutional Neural Network using Resnet50V2 and before giving it to DCNN, this model generates pathological image related to input. In this model author employed a dataset which contains 9714 images. By this approach which is mentioned in this paper achieves an accuracy of about 98% percentage.