Paddy crops must be protected through early disease identification. In the past, diagnosing diseases required observation or laboratory examination. Visual observation requires expertise, and it may differ for each person, leading to inaccuracy. Laboratory tests take longer and might not be capable to deliver results right away. To solve this problem, a machine learning approach based on visual processing is utilized to identify diseases and categorize them. Diseases affecting rice (Oryza sativa) were our main concern. The photographs include diseased leaves and stems that were gathered from paddy fields. The dataset includes four healthy leaves and three diseases from three different disease classes: (1) Bacterial Leaf Blight (2) Sheath Blight and (3) Rice blast. Farmers will be able to boost their produce with the help of early disease identification.