Predicting Some Quality Properties of Different White Rice Varieties by Image Processing Technology

Document Type : Research Article


1 condedat Ph.D university of mohaghegh ardabili.

2 Associate Professor, Department of Biosystems Engineering, University of Mohaghegh Ardabili,

3 University of Mohaghegh Ardabili


Rice is one of the most common and most consumed foods in the world. Quality properties are among the most important factors in determining the cooking and processing characteristics of rice. One of the major problems in the food industry is predicting quality properties. Therefore, in this study, it is possible to predict amylose content (AC), gelatinization temperature (GT), gel consistency (GC), protein content (PC), minimum viscosity (MV), peak viscosity (PV), final viscosity (FV), breakdown viscosity (BDV) and setback viscosity (SBV) of 100 single grain rice samples of Hashemi, Khazar and Dorefak using image processing technology in three exposure treatments including high exposure with LED lamp, high exposure with LED lamp and fluorescent and Anti-light exposure. Calibration models were developed by multivariate linear least squares (PLS) regression. Calibration coefficients of calibration coefficients of variables AC, GT, GC, PC, MV, PV, FV, BDV and SBV in Hashemi, Khazar and Darfak varieties for all treatments were R2cal ≥ 0.89, R2cal ≥ 0.95, and R2cal ≥ 0.92 respectively. prediction coefficients were obtained with R2pre ≥ 0.88, R2pre ≥ 0.94 and R2pre ≥ 0.90, respectively. The results of PLS regression showed that the variables derived from the shape and size characteristics and color variables R,, G, B, L, a and b of image processing were able to predict the rice quality parameters with good accuracy. As a result, using low-cost, non-destructive image processing technology can predict some of the qualitative properties of rice.

Graphical Abstract

Predicting Some Quality Properties of Different White Rice Varieties by Image Processing Technology


  • All existing methods for measuring the rice quality indexes are labor-expensive or time-consuming.
  • Predicting some of the rice quality properties is done low-cost and non-destructive via image processing technology.
  • Area and area covered are the most influential variables for predicting qualitye properties of rice.
  • The best treatment for predicting quality properties is lighting by the treatment LED and fluorescence lamps with a prediction accuracy of above 0.94.


Main Subjects

[1] Vithu, P., Tech, M., & Moses, J. A. (2016). Machine vision system for food grain quality evalution: A review. Journal of Trends In Food Science & Technology, 56, 13-20.
[2] Tomlins, K., Manful, J., Gayin, J., Kudjawu, B., & Tamakloe, I. (2007). Study of sensory evaluation, consumer acceptability, affordability and market price of rice. J. Sci.Food Agric., 87, 1564–1575.
[3] Kuchekar, N. A., & Yerigeri, V. V. (2018). Rice Grain Quality Grading Using Digital Image Processing Techniques. IOSR Journal of Electronics and Communication Engineering (IOSR-JECE), 13 (3), 84-88.
[4] Wang, N. N., Sun, D. W., Yang, Y. C., Pu, H., & Zhu, Z. (2016). Recent Advances in the Application of Hyper spectral Imaging for Evaluating Fruit Quality. Food Analytical Methods, 9, 178-191.
[5] Maheshwari, C.V, Jain, K. R, & Modi, C.K. (2012). Non-destructive quality analysis of Indian Basmati Oryza sativa SSP indica (Rice) using image processing, In: Int. Conf. on Communication Systems and Network Technologies (CSNT), (pp. 189-193), 10-14 May, Rajkot, India.
[6] Mousavirad, S.J., Tab, F.A., & Mollazade K. (2012). Design of an Expert System for Rice Kernel Identification Using Optimal Morphological Features and Back Propagation Neural Network. International Journal of Applied Information Systems, 3(2), 33-37.
[7] Vidya, P., & Malemath, V. S. (2015). Quality Analysis and Grading Of Rice Grain Images. International Journal of Innovative Research in Computer and Communication Engineering, 3(6), 5672-5678.
[8] Mittal, S., Dutta, M. K., & Issac, A. (2019). Non-destructive image processing based system for assessment of rice quality and defects for classification according to inferred commercial value. Measurement, 148, 1-8.
[9] Nalladurai, K., Alagusundaram, K., & Gayathri, P. (2003). Effects of variety and moisture content on the engineering properties of paddy and rice. The Ameican Medical A ssociation (AMA), 34(2), 47-52.
[10] Juliano, B. (1971). A simplified assay for milled rice amylose. Cereal Science Today, 16, 334–360.
[11] Champagne, E., Bett, K., Vinyard, B., Mcclung, A., Barton, F., Moldenhauer, K., Linscombe, S., & Mckenzie, K. (1999). Correlation between cooked rice texture and Rapid Visco Analyses measurements. Cereal Chemistry, 76, 764-771.
[12] Cagampang, G. (1973). A gel consistency test for eating quality of rice. Journal Sci. Food and Agric., 24(12), 1589-94.
[13]  Xu, Y. L., Xiong, S. B., Li, Y. B., & Zhao, S. M. (2008). Study on creep properties of indica rice gel. Journal of Food Engineering, 86, 10–16.
[14] Kesarwani, A., Chiang, P., & Chen, S. (2016). Rapid Visco Analyzer Measurements of japonica Rice Cultivars to Study Interrelationship between Pasting Properties and Farming System. International Journal of Agronomy, 3595326, 1-6.
[15] Patel, K., Kar, A., Jha, S., & Khan, M. (2012). Machine vision system: a tool for quality inspection of food and agricultural products. J. Food Sci Technol., 49(2), 123–141.
[16] Chen, J., Miao, Y., Sato, S., & Zhang, H. (2008). Near infrared spectroscopy for determination of the protein composition of rice flour. J. Food Science Technology Research, 14(2), 132–138.
[17] Siriphollakul, P., Nakano, K., Kanlayanarat, S., Ohashi, S., Sakai, R., Rittiron, R., & Maniwara, P. (2017). Eating quality evaluation of KhaoDawk Mali 105 rice using near infrared spectroscopy. LWT - Food Science and Technol., 79, 70-77.
[18] Nicolai, B., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, K., & Lammertyn, J. (2007). Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review. Postharvest Biology and Technology, 46, 99-118
[19] Heidarbeigi, K., Mohtasebi, S. S., Foroughirad, A., Ghasemi-Varnamkhasti, M., Rafiee, S., & Rezaei, K. (2015). Detection of adulteration in saffron samples using electronic nose. International Journal of Food Properties, 18(7), 1391-1401.
[20] Mabood, F., Hussain, J., Jabeen, F., Abbas, G., Allaham, B. A., Albroumi, M., & Haq, Q. M. (2018). Applications of FT-NIRS combined with PLS multivariate methods for the detection & quantification of saccharin adulteration incommercial fruit juices. Food Addit Contam., 35(6), 1052-1060.