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The quality analysis of rice is done regularly during storage period at Food Corporation of India (FCI) and other godowns. This quality evaluation is done manually by trained person. This process is tedious and reliable results dependent on the skill of the person. Machine vision is an emerging technology for rapid identification of grain quality. It provides alternative for an automated, non-destructive and cost-effective technique. Therefore, present study was undertaken to develop and validate a simple, rapid and accurate method for identification of head rice and brokens of three varieties i.e. Punjab Pusa Basmati 1509, PR121 and PR124 using digital image processing method. The images of randomly selected 40 grains each of head rice and brokens for each variety were captured through flatbed scanner followed by processing of images using open source Image J software. The values of various morphological parameters (viz. length, width, thickness, sphericity, geometric mean diameter and volume) of sample grain extracted were compared by the values calculated using digital vernier caliper. Statistical analysis showed that the results obtained by digital vernier caliper can be linearly correlated to the results obtained by image processing. For varieties Punjab Pusa Basmati 1509, PR121 and PR124, variation in manually calculated length and predicted length was 98.5%, 98.3% and 93.8% and the variation in manually calculated width and predicted width was 97.6%, 96.9% and 97.9%. All the results were found significant at 5% level of significance (p

Development of simple, rapid and accurate method for identification of head rice and brokens using image processing