Precision farming is a need of today's world. In last few decades image processing with IOT domain is rapidly increasing. In IOT domain we can easily use to multiple sensors for sensing unreadable format data. By using image processing domain we can easily work on complicated image to process efficiently. The plant disease detection is based on food security detection. The food security is based on food effect as well as environment. If food environment is not good then automatically food quality will loss. So we have to implement plant disease detection system using IOT and image processing. In this paper we have used four different sensors. 1. PH Sensor 2. Temperature sensor 3. Humidity sensor 4. Soil Moisture Sensor. We can collect data through Raspberry pies well as collect plant images. The main goal of the proposed work is to monitor the plant leaf, detect and classify them according to the diseases using the data mining and image processing techniques. Geological condition is extraordinary for farming in light of the fact that it gives numerous good conditions. By collecting, the information from various types of sensors predicts the diseases that can affect the leaf. We have implemented the classification and clustering algorithm to sort out good quality and bad quality plant detection. Our segmentation approach and utilization of support vector machine demonstrate disease classification over 300images with an accuracy of 90%.