A Robust and Accurate Potato Leaf Disease Detection System Using Modified AlexNet Model

This research addresses the economic and ecological losses caused by diseases that damage potato leaves, such as Late Blight, Early Blight, Septoria leaf spots, curly leaves, and bacterial wilt. The study utilizes machine learning and deep learning techniques to swiftly and accurately identify these diseases at an early stage, reducing damage and losses to farmers. The research focuses on four categorization groups, including three leaf illnesses and one healthy leaf, with the main goal of providing early detection of disease. The study employs three deep learning models, VGGNet16, RenNet101, and modified AlexNet, with modified AlexNet proving to be the most accurate, achieving a training accuracy of 99.97% and a testing accuracy of 61%.