Leaf Disease Identification through Transfer Learning: Unveiling the Potential of a Deep Neural Network Model

Abstract

Grape is one of the world's most crucial and widely consumed crops. The yield of grapes varies depending on the method of fertilisation. Hence, some other factors also impact crop production and quality. One of the major elements affecting crop quality output is leaf disease. Therefore, it is necessary to diagnose and classify diseases earlier. Diseases impact grape production in many ways. It could reduce the disease's impact on grapevines if the disease were identified earlier, which would result in higher crop output. There has been a lot of experimentation with new approaches to diagnosing and classifying diseases. This endeavour aims to assist farmers in accurately analysing and informing themselves about illnesses in their early stages. The Convolutional Neural Network (CNN) is an effective method for identifying and categorising grape diseases. A dataset of 3297 images of grape leaves affected by four distinct diseases and a healthy leaf was used to conduct the entire experiment using python and orange software. Here's a rundown of the whole procedure: Before the actual segmentation of the images begins, input images are first pre-processed. The images are then subjected to the second processing round using several CNN hyper-parameters. Finally, CNN analyses images for details such as colour, texture, and edges, among other things. According to the results, the proposed model's predictions are 99.3% correct.