Measuring the Weight of Egg with Image Processing and ANFIS Model

  • Payam Javadikia
  • Mohammad Hadi Dehrouyeh
  • Leila Naderloo
  • Hekmat Rabbani
  • Ali Nejat Lorestani
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7076)


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.


egg weight defect grading adaptive neuro-fuzzy inference system 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    United States Department of Agriculture. Egg-Grading Manual. Agriculture Handbook No.75. Agriculture Marketing Service, USDA (2000)Google Scholar
  2. 2.
    Goodrum, J.W., Elster, R.T.: Machine Vision for Crack Detection in Rotating Eggs. Transactions of the ASAE 35(4), 1323–1328 (1992)CrossRefGoogle Scholar
  3. 3.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice Hall Inc. (2004)Google Scholar
  4. 4.
    Kuchida, K., Fukaya, M., Miyoshi, S., Suzuki, M., Tsuruta, S.: Nondestructive Prediction Method for Yolk: Albumin Ratio in Chicken Eggs by Computer Image Analysis. Poultry Science 78, 909–913 (2000)CrossRefGoogle Scholar
  5. 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. 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
  7. 7.
    Narushin, V.G.: Egg Geometry Calculation Using the Measurements of Length and Breadth. Poultry Science 84, 482–484 (2005)CrossRefGoogle Scholar
  8. 8.
    Sabliov, C.M., Bolder, D., Keener, K.M., Farkas, B.E.: Image Processing Method to Determine Surface Area and Volume of Axisymmetric Agricultural Products. International Journal of Food Prop. 5, 641–653 (2002)CrossRefGoogle Scholar
  9. 9.
    Koc, B.: Determination of Watermelon Volume Using Ellipsoid Approximation and Image Processing. Journal of Postharvest Technology 45, 366–371 (2007)CrossRefGoogle Scholar
  10. 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
  11. 11.
    Fraile-Ardanuy, J., Zufiria, P.J.: Design and comparison of adaptive power system stabilizers based on neural fuzzy networks and genetic algorithms. Neurocomputing 70, 2902–2912 (2007)CrossRefGoogle Scholar
  12. 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
  13. 13.
    Singh, J., Singh, S.: Modelling for tensile strength of friction welded aluminium pipes by ANFIS. Int. J. Intelligent Engineering Informatics 1(1), 3–20 (2010)CrossRefGoogle Scholar
  14. 14.
    Avci, E.: Comparison of wavelet families for texture classification by using wavelet packet entropy adaptive network based fuzzy inference system. Applied Soft Computing 8, 225–231 (2008)CrossRefGoogle Scholar
  15. 15.
    Buragohain, M., Mahanta, C.: A novel approach for ANFIS modelling based on full factorial design. Applied Soft Computing 8, 609–625 (2008)CrossRefGoogle Scholar
  16. 16.
    Ross, T.J.: Fuzzy logic with engineering applications, 2nd edn. John Wiley and Sons Ltd., London (2004)zbMATHGoogle Scholar
  17. 17.
    Cheng, C.B., Cheng, C.J., Lee, E.S.: Neuro-Fuzzy and Genetic Algorithm in Multiple Response Optimization. Computers and Mathematics with Applications 44, 1503–1514 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Ertunc, M., Hosoz, H.: Comparative analysis of an evaporative condenser using artificial neural network and adaptive neuro-fuzzy inference system. International Journal of Refrigeration 3(1), 1426–1436 (2008)CrossRefGoogle Scholar
  19. 19.
    Patel, V.C., Mc Clendon, R.W., Goodrum, W.: Color Computer Vision and Artificial Neural Networks for the Detection of Defects in Poultry Eggs. Artificial Intelligence Review 12, 163–176 (1998)CrossRefzbMATHGoogle Scholar
  20. 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. 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. 22.
    Ma, H.M.: Application of BP neural networks in plant taxonomy identification. Agriculture Network Information (12), 1–3 (2006) Google Scholar
  23. 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
  24. 24.
    Ying, L.C., Pan, M.C.: Using adaptive network based fuzzy inference system to forecast regional electricity loads. Energy Conversation and Management 49, 205–211 (2008)CrossRefGoogle Scholar
  25. 25.
    Sengur, A.: Wavelet transform and adaptive neuro-fuzzy inference system for color texture classification. Expert Systems with Applications 34, 2120–2128 (2008)CrossRefGoogle Scholar
  26. 26.
    Übeyli, E.D.: Adaptive neuro-fuzzy inference system employing wavelet coefficients for detection of ophthalmic arterial disorders. Expert Systems with Applications 34, 2201–2209 (2008)CrossRefGoogle Scholar
  27. 27.
    Singh, J., Singh, S.: Multi input single output fuzzy model to predict tensile strength of radial friction welded GI pipes. International Journal of Information and Systems Sciences, Institute for Scientific Computing and Information 4(3), 462–477 (2008)zbMATHGoogle Scholar
  28. 28.
    Jang, J.S.R.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man. Cybern., 665–685 (1993)Google Scholar
  29. 29.
    Rashidi, M., Gholami, M.: Prediction of egg mass based on geometrical attributes. Agriculture and Biology Journal of North America 2(4), 638–644 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Payam Javadikia
    • 1
  • Mohammad Hadi Dehrouyeh
    • 2
  • Leila Naderloo
    • 3
  • Hekmat Rabbani
    • 1
  • Ali Nejat Lorestani
    • 1
  1. 1.Department of Mechanical Engineering of Agricultural Machinery, Collage of Agriculture and Natural ResourcesRazi UniversityKermanshahIran
  2. 2.Department of Mechanical Engineering of Agricultural MachineryRoudehen Branch, Islamic Azad UniversityRoudehenIran
  3. 3.Department of Mechanical Engineering of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, Collage of Agriculture and Natural ResourcesUniversity of TehranTehranIran

Personalised recommendations