Rice is the major sustenance in the globe. However, the quantity of rice is being hindered by different sort of paddy ailments. The peculiar ailment of paddy is the leaf illness. It is actually tedious and relentless for agriculturalist in the remote territories to distinguish the paddy leaf illnesses because of the unavailability of the specialists. Despite of the authorities available in specific locales, identify the ailments by unaided eye which may be inaccurate on some occasions. Therefore, a robotized system can confine these issues. In this paper, a robotized structure is proposed for discovering four essential paddy leaf illnesses (Brown spot, Leaf blast, leaf streak and Bacterial blight) and pesticides or conceivably composts are recommended based on the severeness of the ailments. K-means is utilized for isolating the influenced region of paddy leaf image. Visual substance (colour and texture) are utilized as highlights for grouping of the ailments. The kind of paddy leaf sicknesses is perceived by Support Vector Machine (SVM) classifier. After identification, the prescient cure is suggested based on the severity that can help the horticulture related individuals and associations to take suitable activities against these ailments.