Indian Journal of Animal Research

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Research Article

Indian Journal of Animal Research, volume 51 issue 2 (april 2017) : 322-326

A study regarding the fertility discrimination of eggs by using ultrasound

Eray Önler1, Ilker H Çelen1, Timur Gulhan2, Banur Boynukara3*
1<p>Department of Microbiology, Faculty of Veterinary Medicine,&nbsp;Namik Kemal University, Tekirdag, Turkey.</p>
Cite article:- Önler1 Eray, Çelen1 H Ilker, Gulhan2 Timur, Boynukara3* Banur (2016). A study regarding the fertility discrimination of eggs by using ultrasound . Indian Journal of Animal Research. 51(2): 322-326. doi: 10.18805/ijar.v0iOF.4561.

ABSTRACT

The aim of this research was to track the growth of chicken eggs, and make a decision as to whether the egg was fertilized or not. A digital imaging system has been developed in order to take an image from six different points without damaging the egg shell. All the images were transferred to a PC and turned into binary images. All the images were reduced to 1024 pixels and fed directly into the classification algorithm. The logistic regression method was used to discriminate the fertility of the eggs. Python programming language and the scikit-learn machine learning library was used to carry out the classifications. True positive, true negative, wrong positive, and wrong negative detection numbers in the trials were 350, 344, 56, and 50, respectively. Negative indicates the egg was infertile, and positive indicated that the egg was fertilized. The model accuracy was measured as 0.8675.

REFERENCES


  1. Andrewartha, S.J., Tazawa, H. and Burggren,W.W. (2011). Embryonic control of heart rate: Examining developmental patterns and temperature and oxygenation influences using embryonic avian models.Resp. Physiol.Neurobi. 178: 84-96.

  2. Bamelis, F.R., Tona, K. De Baerdemaeker, J.G. and Decuypere, E.M. (2002). Detection of early embryonic development in chicken eggs using visible light transmission. Br. Poult. Sci.43: 204-212.

  3. Bartels, T., Fischer, B., Krüger, P., Koch, E., Ryll, M. andKrautwald-Junghanns, M.E. (2008). 3D-X-ray microcomputer tomography and optical coherence tomography as methods for the localization of the blastoderm in the newly laid unincubated chicken egg.Dtsch. Tierarztl.Wochenschr.115: 182-188.

  4. Burkhardt, A., Meister, S., Bergmann, R.and Koch, E. (2011). Influence of storage on the position of the germinal disc in the fertilized unincubated chicken egg. Poultry Sci.90: 2169-2173.

  5. Chalker, B.A. and Hutchins, J.E. (2003).Methods and apparatus for non-invasively identifying conditions of eggs via multi-wavelength spectral comparison.Embrex, Inc., assignee. US Pat. No. 6:535,277.

  6. Chue, J. and Smith.C.A.(2011).Sex determination and sexual differentiation in the avian model.FEBS J.278: 1027-1034.

  7. Coucke, P.M., Room, G.M., Decuypere E.M. and De Baerdemaeker, J.G. (1997).Monitoring embryo development in chicken eggs using acoustic resonance analysis.Biotechnol.Progr.13: 474-478.

  8. Cox, D.R. (1958). The regression analysis of binary sequences (with discussion). J. Roy. Stat. Soc. B. 20: 215-242. 

  9. Das, K. and Evans, M.D.(1992a). Detecting fertility of hatching eggs using machine vision: I. Histogram characterization method. Trans. ASAE. 35: 1335-1341.

  10. Das, K. and Evans, M.D. (1992b). Detecting fertility of hatching eggs using machine vision: II.Neuralnetwork classifiers. Trans. ASAE.35: 2035-2041.

  11. Freedman, D.A. (2009). Statistical Models: Theory and Practice. Cambridge University Press. p. 128. 

  12. Klein, S., Rokitta, M., Baulain, U., Thielebein, A., Haase, A. and Ellendorf, F. (2002). Localization of the fertilized germinal disc in the chicken egg before incubation. Poultry Sci. 81: 529-536.

  13. Liu, L. and Ngadi, M.O.(2013).Detection of fertility and early embryo development of chicken eggs using near-    infared hyperspectral imaging. Food Bioprocess Tech. 6: 2503-2513.

  14. Morinha, F., Cabral, J.A. and Bastos, E.(2012).Molecular sexing of birds: A comparative review of polymerase chain reaction (PCR)-based methods. Theriogenology.78: 703-714.

  15. Oosterbaan, A.M.(2012). Hemodynamics and vascular development in the chicken embryo and the effects of homocysteine and folic acid treatment, PhD Thesis, Erasmus University Rotterdam, The Netherlands.

  16. Patel, V.C., McClendon, R.W. and Goodrum, J.W. (1996). Detection of blood spots and dirt stains in eggs using computer vision and neural networks. Appl. Eng. Agric.12: 253-258.

  17. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E. (2011). Scikit-    learn: Machine Learning in Python. The Journal of Machine Learning Research (JMLR)(12): 2825-2830

  18. Phelps, P., Bhutada, A., Bryan, S., Chalker, A., Ferbell, B., Neuman, S., Ricks, C., Tran, H. and Butt, T. (2003). Automated identification of male layerchicks prior to hatch.World.Poultry. Sci. J.59:33-38.

  19. Smith, D.P., Mauldin, J.M., Lawrence, K.C., Park B. and Heitschmidt, G.W. (2005). Detection of fertilityand early development of hatching eggs with hyperspectralimaging. Proc. 11thEurope.Symp. Qual. Eggs and Egg Prod. Doorwerth,The Netherlands.

  20. Smith, D.P. Lawrence K.C. and Heitschmidt, G.W. (2008). Fertility and embryo development of broiler hatching eggs evaluated with a hyperspectralimaging and predictive modeling system. Int. J. Poultry Sci.7: 1001-1004.

  21. Walker, S.H. and Duncan, D.B. (1967). Estimation of the probability of an event as a function of several independent variables. Biometrika 54: 167-178.

     
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