Rice is the most significant cultivated harvests everywhere throughout the world, specifically in Asian nations. Nowadays the evaluation of rice quality has a great impact on the market due to adulteration by plastic rice and stones. The assessment of rice is prepared physically by experienced a rancher which is a tedious and monotonous errand and in particular it is a dangerous strategy where the rice might be pulverized by growth pollution. In this paper, a quick, programmed and non-destructive assessment practice is endeavored to measure the nature of rice based on deep learning neural system model. The pre-processing is started by the Median channel to expel noises from the input pictures. By utilizing Fuzzy c-means clustering, the edges of the rice pictures are appropriately depicted. The features like contour, edge, and region are chosen with the assistance of a genetic algorithm. A probabilistic neural system is created to characterize the portioned rice picture. The presentation of PNN model is introduced to show its viability with regards to exactness, accuracy, f-score, and review, and the outcomes are compared with the existing SVM method.