Measuring the Weight of Egg with Image Processing and ANFIS Model
It is clear that the egg is very important in human food basket. But a problem in food processing manufactures is measuring the weight of eggs as real time and it’s difficult. One solution can be by using the camera. In this research we tried to measure the width and length of egg by real time image processing and then design and optimize an ANFIS model to find best relation between image processing outputs and the weight of egg. The correlation coefficient of experimental value for weight of egg and predicted value by ANFIS model is 0.9942. The result is very interesting and this idea is cheap, novel and practical.
Keywordsegg weight defect grading adaptive neuro-fuzzy inference system
Unable to display preview. Download preview PDF.
- 1.United States Department of Agriculture. Egg-Grading Manual. Agriculture Handbook No.75. Agriculture Marketing Service, USDA (2000)Google Scholar
- 3.Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice Hall Inc. (2004)Google Scholar
- 5.Pourreza, H.R., Pourreza, R.S., Fazeli, S., Taghizadeh, B.: Automatic Detection of Eggshell Defects Based on Machine Vision. Journal of Animal and Veterinary Advances 7(10), 1200–1203 (2008)Google Scholar
- 6.Dehrouyeh, M.H., Omid, M., Ahmadi, H., Mohtasebi, S.S., Jamzad, M.: Grading and Quality Inspection of Defected Eggs Using Machine Vision. International Journal of Advanced Science and Technology 17, 23–31 (2010)Google Scholar
- 10.Rashidi, M., Malekiyan, M., Gholami, M.: Egg Volume Determination by Spheroid Approximation and Image Processing. World Applied Sciences Journal 3(4), 590–596 (2008)Google Scholar
- 12.Kiralakis, L., Tsourveloudis, N.C.: Modeling and Optimization of Olive Stone Drying process. In: WSEAS International Conference on Dynamical Systems and Control, Venice, Italy, November 2-4, pp. 240–246 (2005)Google Scholar
- 20.Wang, Q.-H., Wen, Y.-X.: Research on the Grading Method of Egg’s Weight Based on BP Neural Network. Hubei Agricultural Sciences (1), 97–99 (2005)Google Scholar
- 21.Long, M.-S., He, D.-J., Ning, J.-F.: An integrated apple grading system based on genetic neural network. J. Northwest Sci-Tech University of Agriculture and Foresty 12(6), 1–4 (2001)Google Scholar
- 22.Ma, H.M.: Application of BP neural networks in plant taxonomy identification. Agriculture Network Information (12), 1–3 (2006) Google Scholar
- 23.Tu, K., Ren, K., Pan, L., Li, H.: A Study of Broccoli Grading System Based on Machine Vision and Neural Networks. In: Proceedings of the 2007 IEEE International Conference on Mechatronics and Automation, Harbin, China, August 5-8 (2007)Google Scholar
- 28.Jang, J.S.R.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man. Cybern., 665–685 (1993)Google Scholar