Design, development and performance evaluation of an automatic control system for rice whitening machine based on computer vision and fuzzy logic

Computers and Electronics in Agriculture

Volumn 124, Issue , 2016, Pages 14-22

  Zareiforoush, Hemad ^a   Minaei, Saeid ^b   Alizadeh, Mohammad Reza ^c   Banakar, Ahmad ^b   Samani, Bahram Hosseinzadeh ^d  

^a UNIVERSITY OF GUILAN   (Iran)

^b TARBIAT MODARES UNIVERSITY   (Iran)

^c RICE RESEARCH INSTITUTE OF IRAN   (Iran)

^d SHAHREKORD UNIVERSITY   (Iran)

Author keywords

Automatic adjustment; Computer vision; Fuzzy logic; Rice; Whitening machine

Indexed keywords

AUTOMATION; COMPUTER CIRCUITS; COMPUTER CONTROL SYSTEMS; COMPUTER HARDWARE; COMPUTER VISION; CONTROL SYSTEMS; DATA ACQUISITION; FUZZY LOGIC; IMAGE PROCESSING; PROCESS CONTROL; QUALITY CONTROL; RECONFIGURABLE HARDWARE;

AUTOMATIC ADJUSTMENT; DATA-ACQUISITION UNITS; DEGREE OF MILLING; DISCHARGE PRESSURES; FUZZY LOGIC TECHNIQUES; INTELLIGENT AUTOMATIC CONTROL; QUALITATIVE INDICES; RICE;

FUZZY INFERENCE;

COMPUTER VISION; CONTROL SYSTEM; DATA ACQUISITION; FUZZY MATHEMATICS; MACHINERY; RICE; SOFTWARE;

EID: 84962255686     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2016.01.024     Document Type: Article

References (22)

1. Alavi N. Quality determination of mozafati dates using mamdani fuzzy inference system. J. Saudi Soc. Agric. Sci. 2013, 12:137-142.
2. Davies E., Sun D. Computer vision for automatic sorting in the food industry. Comput. Vision Technol. Food Beverage Ind. 2012, 150.
3. Dehrouyeh M., Omid M., Ahmadi H., Mohtasebi S., Jamzad M. Grading and quality inspection of defected eggs using machine vision. Int. J. Adv. Sci. Technol. 2010, 17:23-31.
4. Fan F., Ma Q., Ge J., Peng Q., Riley W.W., Tang S. Prediction of texture characteristics from extrusion food surface images using a computer vision system and artificial neural networks. J. Food Eng. 2013, 118:426-433.
5. Fang C., Hu X., Sun C., Duan B., Xie L., Zhou P. Simultaneous determination of multi rice quality parameters using image analysis method. Food Anal. Methods 2014, 1-9.
6. FAO, 2015. FAOSTAT. <>. http://faostat3.fao.org.
7. Firouzi S., Minaei S., Payman S., Fotohi H. The effect of feed rate and outlet pressure on the breakage of rice kernels in a rice jet pearler. Iran. J. Sci. Food Ind. 2004, 1:31-44.
8. Jayas D., Ghosh P., Paliwal J., Karunakaran C. Quality Evaluation of Wheat. Computer Vision Technology for Food Quality Evaluation 2007, 351-376. Academic Press, New York.
9. Kondo N. Automation on fruit and vegetable grading system and food traceability. Trends Food Sci. Technol. 2010, 21:145-152.
10. Lee K.H. First Course on Fuzzy Theory and Applications 2006, Springer.
11. Omid M., Soltani M., Dehrouyeh M.H., Mohtasebi S.S., Ahmadi H. An expert egg grading system based on machine vision and artificial intelligence techniques. J. Food Eng. 2013, 118:70-77.
12. Patel K.K., Kar A., Jha S., Khan M. Machine vision system: a tool for quality inspection of food and agricultural products. J. Food Sci. Technol. 2012, 49:123-141.
13. Payman S., Bagheri I., Zareiforoush H. Milling characteristics of rice grains as affected by paddy mixture ratio and moisture content. Int. J. Biosci. 2014, 4:87-97.
14. Shewfelt R.L., Bruckner B. Fruit and Vegetable Quality: An Integrated View 2000, CRC Press.
15. Shiddiq, D.M., Nazaruddin, Y.Y., Muchtadi, F.I., Raharja, S., 2011. Estimation of rice milling degree using image processing and adaptive network based fuzzy inference system (ANFIS). In: Second International Conference on Instrumentation Control and Automation (ICA), IEEE, Bandung, Indonesia, pp. 98-103.
16. Siebenmorgen T., Saleh M., Bautista R. Milled rice fissure formation kinetics. Trans. ASAE 2009, 52:893-900.
17. Song L. Fuzzy control for food agricultural robotics of a degree. Adv. J. Food Sci. Technol. 2014, 6.
18. Wu D., Sun D.-W. Colour measurements by computer vision for food quality control - a review. Trends Food Sci. Technol. 2013, 29:5-20.
19. Zareiforoush H., Komarizadeh M., Alizadeh M. Effects of crop-machine variables on paddy grain damage during handling with an inclined screw auger. Biosyst. Eng. 2010, 106:234-242.
20. Zareiforoush H., Minaei S., Alizadeh M.R., Banakar A. Design and performance evaluation of a singulation device for effective positioning of rice kernels in a machine vision-based quality inspection system. Mech. Eng. 2014, 73:26539-26545.
21. Zareiforoush H., Minaei S., Alizadeh M.R., Banakar A. A hybrid intelligent approach based on computer vision and fuzzy logic for quality measurement of milled rice. Measurement 2015, 66:26-34.
22. Zareiforoush H., Minaei S., Alizadeh M.R., Banakar A. Potential applications of computer vision in quality inspection of rice: a review. Food Eng. Rev. 2015, 1-25.