Food Security : Grading The Quality Of Variety Of Rice Grains Through Digital Image Processing Rajlakshmi Ghatkamble Research Scholar at School of Research and Innovation Department of Computer Science , C. M. R University, Bangalore Karnataka, India Abstract or Introduction Conclusion Results   Rice is the staple food of India. Adulteration of rice is a major issue and has distinct effect on its yield. Proper inspection and grading of rice is very important. In present grain-handling system, grain type and quality, are rapidly assessed by visual inspection which is, however, tedious and time consuming. The decision-making capabilities of grain inspector can be seriously affected by his/her physical condition, mental state caused by bias and work pressure. Working conditions such as improper lighting, climate, etc can also affect the results of visual inspection. To prevent fraudulent mislabelling of rice grains, an automated imaging system that can extract the visual features of rice grains and classify those using imaging techniques will simplify this task.   In the present work a digital imaging approach has been devised in order to investigate different types of characteristics to identify the rice varieties. Two different common rice varieties were used in tests for defining. These include existing standards for rice length, area and aspect ratio features of rice. It successfully shows the effectiveness of compactness as its features. When the data base of this work can recognize the rice’s, which has been trained the data in number of time; and hence it has been identified. With proper selection of software tools, we can design a low cost tool for quality analysis of rice grains which provides all relevant parameters about rice grains by image analysis. Hence we aim for the accurate classification of micro calcification of different types of rice varieties for using in the testing and producing the accurate result. Objective In the present work a digital imaging approach has been devised in order to investigate different types of characteristics to identify the rice varieties. Two different common rice varieties were used in tests for defining. Existing and Proposed System Testing Table for Performance Analysis Testing Rice Type File Name Rice Count Actual Type Classified Type Accuracy Total Rice Accuracy 3 Sona Masuri Sona Masuri_1 44 90.90% 91.16% 4 Sona Masuri_2 35 91.42% 5 Big Rice Big Rice_1 Big Rice 95.34% 90.72% 6 Big Rice_2 86.11% 7 Indraini Rice_1 50% 40% 8 Rice_2 30% 9 Basmati 100% 10 Total Project Accuracy 84.376% References In the present grain-handling system, grain type and quality are rapidly assessed by visual inspection. This evaluation process is however tedious and time consuming. The decision-making capabilities of human-inspectors are subjected to external influences such as fatigue, vengeance, bias etc. [1]. Rajlakshmi Ghatkamble, “Matching And Grading Of Different Varieites Of Rice Grains Through Digital Image Processing”, International Journal of Computer Science Trends and Technology (IJCST) – Volume X Issue X, Year 2016, C.M.R University, Bangalore, Karnataka, India [2]. http://www.arabnews.com/news/463708 [3]. http://www.bangkokpost.com/learning/learning-from-news/360470/rice-tricks-innovation-in-corruption [4]. https://www.google.co.in/webhp?sourceid=chrome-instant&ion=1&espv=2&ie=UTF8#q=global%20food%20security%20logo I Image Acquisition Preprocessing Thresholding Feature Selection and Extraction Counting the number of grains Image Segmentation Neural Network Decision and Classification