In India, Agriculture is of great importance. Diseases affecting crops is a major challenge faced by Agricultural industry. Hence earlier detection of crop disease has greater importance in agricultural field. In recent years, the number of diseases on plants and degree of harm caused has increased due to the variation in the varieties of microorganisms, inadequate cultivation methods and inappropriate plant protection techniques. In the present study, Farmers spend so much money on disease management, often without adequate technical support, resulting in poor disease control, pollution and harmful results. In addition, plant disease can destroy natural ecosystems, adding environmental problems caused by poor management of crops. A symptom of plant disease is a visible effect of disease on the plant. Symptoms may include a visible change in colour, shape or function of the plant as it responds to the pathogen. A faster method for detection of crop diseases is done with the help of IoT. A sensor network can be created in the farm land using Raspberry Pi 4 model. The images will be captured by the sensor cameras and send to the cloud server via Raspberry pi 4 model. The proposed model is a theoretical model. In this proposed methodology, various image processing techniques will be applied on acquired images for classification of crop diseases using fuzzy c-means clustering algorithm. This paper will also shows the method of image processing techniques such as image acquisition, image pre-processing, image segmentation and feature extraction for classification of crop diseases. Farmers can produce quality crops and thus healthy food can be obtained by this proposed methodology and make more profit.