Rice Leaf Diseases Detector Based on AlexNet

Abstract

Rice leaf disease detection is critical for the agriculture sector since rice feeds approximately half of the world’s population. Many researchers worked on this subject, and their results varied depending on the methodologies they used. A deep learning classification architecture, known as AlexNet, is used in this paper to detect three common rice leaf diseases: bacterial leaf blight (BLB), brown spot (BS), and leaf smut (LS), along with healthy leaves (HL). Compared to prior efforts, this work yields an outperforming result with an accuracy of 99.71%.