Sesame plant is the most important types of a plant under the categories of oil crop. Different literature shows that the manual and experimental diagnosis of plant disease the result prone to biased and time and cost consumed. An intelligent diagnosis of plant disease gives a great effort for agricultural industries related to productivity. In this study, an intelligent model that diagnosis sesame plant disease that uses an integration of image processing and machine learning is studied. The main objective of the proposed system is to classify the captured image either infected or healthy plant using extracted features. In this study, the difference between healthy and diseased sesame plants was considered and investigating four types of disease these are Powdery Mildew, Bacterial blight, Cercospora leaf spot and Alternaria leaf spot with a total of five classes including healthy leaf.