Abstract
Rice is a principal dietary component for most of the world’s population and comprises many breeds and qualities. Qualitative grading of rice is essential to determine its cost. This grading process is currently conducted via physical and chemical invasive processes. Automated, non-invasive grading processes are pivotal in reducing the drawbacks of invasive processes. This research involves the construction of a method for automated, non-invasive, qualitative grading of milled rice. Percentage of broken rice is one of the factors which governs the grading of rice. The method developed is useful in predicting the percentage of broken rice from the image of a given sample of rice. Color images of rice were acquired using cellphone camera. The images were processed by a foreground detector program. Statistical analysis was implemented to extract features for the formation of a regression model. The entire method is executed in MATLAB. The method involves simple regression models and hence requires lesser runtime (4.0567 s) than existing methods of calculating percentage of broken rice. The process produces low root mean square error (0.69 and 0.977) and high r squared (0.999 and 0.999) values for overlapping and non-overlapping grains’ dataset respectively.
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This research was supported by Vishwakarma Institute of Technology, Pune, India.
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Wyawahare, M., Kulkarni, P., Dixit, A., Marathe, P. (2020). Statistical Model for Qualitative Grading of Milled Rice. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T., Valentino, G. (eds) Advances in Computing and Data Sciences. ICACDS 2020. Communications in Computer and Information Science, vol 1244. Springer, Singapore. https://doi.org/10.1007/978-981-15-6634-9_22
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