Classifying Arecanuts and Detecting Disease using HSV Colour Model in Image Processing

India is the place that is known for agribusiness and Arecanut having the scientific name Areca catechu L. is one of the significant commercial crop in India. In this paper computer vision strategies are created utilizing picture handling procedures to recognize contaminated and non-contaminated nuts and classify non- contaminated nuts into boilable nuts (BN) and non-boilable nuts (NBN). Farmers experiences a few handling methods before it is prepared for commercial use. Generally the post gathering forms like diagnosing of arecanut and after that grouping of non-tainted into boilable and non-boilable class is done physically. Farmers face numerous issues amid these post-gathering preparing of arecanut as these manual procedures devour a great deal of time and furthermore human resource. The proposed methodology uses image Processing to diagnose and classify the arecanuts into boilable and non-boilable class. The methodology includes six steps: 1. Image Filtering and enhancement 2. Segmentation 3. RGB to HSV conversion 4. Masking 5. Disease detection 6. Classification. All these processes are implemented using Raspberry pi module.