Volume 14, Issue 3 (12-2017)                   JSDP 2017, 14(3): 97-112 | Back to browse issues page

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Hashemzadeh M. A Vision Machine for Detecting Fertile Eggs and Performance Evaluation of Neural Networks and Support Vector Machines in This Machine . JSDP. 2017; 14 (3) :97-112
URL: http://jsdp.rcisp.ac.ir/article-1-488-en.html
Azarbaijan Shahid Madani University
Abstract:   (1655 Views)

In this research, a system is proposed for detecting fertility of eggs. The system is composed of two parts: hardware and software. The fabricated hardware provides a platform to obtain accurate images from inner side of the eggs, without harming their embryos. The software part includes a set of image processing and machine vision processes, which is able to detect the fertility of eggs from captured images, without any sensitivities to different types of eggs (e.g. with different thickness of the eggshell). In order to classify the fertile and infertile eggs, two classifiers based on Artificial Neural Networks (ANN) and Support Vector Machines (SVM) are designed and tested. It means that, to have a fully automatic fertility detection machine, we design two machine learning approaches using SVMs and ANNs to classify fertile and infertile eggs. That is, instead of using a predefined threshold values for distinguishing fertile pixels of egg images from infertile ones, we try to train the machine to do the job automatically. After training the machine using both classification algorithms, the performance of them are accurately investigated and measured in order to select the appropriate one. To evaluate the system, an egg image dataset is provided including 1200 images captured from incubated eggs. Extensive experiments are performed using the provided dataset, which confirm the reliable performance of the system. Comparisons with other fertility detection approaches applying different methods and algorithms confirm that the proposed machine outperforms more complex systems. Performance evaluations of the two proposed classifiers confirm that the SVM based classifier, with average detection accuracy of 50.57% at day 1 of incubation, 83.67% at day 2, 94.20% at day 3, 98.03% at day 4, and 98.91% at day 5, performs better than ANN based classifier, and it is also less sensitive against the reductions in training samples, which can be a serious issue when we are not able to provide more training samples.

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Type of Study: Research | Subject: Paper
Received: 2016/02/14 | Accepted: 2017/03/5 | Published: 2018/01/29 | ePublished: 2018/01/29

1. [1] J. Pourreza, Theoretical and Practical Principles of Poultry: Isfahan University of Technology Press, 2000.
2. [2] A. Sadaghiyan, Poultry guide, Poultry industry for industrial, household, recreational and decorative poultries, 1980.
3. [3] M. KhojastehKey, Principles of Poultry Incubation: Marze Danesh Press, 2012.
4. [4] M. Moshiri, Aviculture: Eshrafi Press, 1982. [PMID]
5. [5] S. E. FroozanMehr, M. Habibollahi, S. N. Alavi, and S. E. FroozanMehr, "Investigation and selection of infertile eggs at incubation stages using machine vision in order to increase the efficiency of production of one-day-old chicks," presented at the 5th National Congress on Agricultural Machinery and Mechanization, Mashhad, Iran, 2008.
6. [6] M. Zardadi and N. Mehrshad, "A New Approach to Retinal Vessel Segmentation by Using Computational Model of Simple Cells in Primary Visual Cortex," Signal and Data Processing, vol. 13, pp. 127-138, 2016.
7. [7] M. A. Ghazavi, M. Mahmoudi, and M. Torfehneghad, "Identification of cracks and egg contamination by using image processing techniques," presented at the 5th National Congress on Agricultural Machinery and Mechanization, Mashahd, Iran, 2008.
8. [8] W. Fang and W. Youxian, "Detecting preserved eggshell crack using machine vision," in Information Technology, Computer Engineering and Management Sciences (ICM), 2011 International Conference on, 2011, pp. 62-65. [DOI:10.1109/ICM.2011.391]
9. [9] Y. Han, J. Gao, and S. Zhang, "Research on the automatic detection system for cracked egg based on LabVIEW," in Measuring Technology and Mechatronics Automation (ICMTMA), 2010 International Conference on, 2010, pp. 190-193. [DOI:10.1109/ICMTMA.2010.670]
10. [10] R. Ibrahim, Z. M. Zin, N. Nadzri, M. Shamsudin, and M. Zaunidin, "Egg's Grade Classification and Dirt Inspection Using Image Processing Techniques," in Proceedings of the World Congress on Engineering, 2012.
11. [11] P. Javadikia, M. Dehrouyeh, L. Naderloo, H. Rabbani, and A. Lorestani, "Measuring the Weight of Egg with Image Processing and ANFIS Model," in Swarm, Evolutionary, and Memetic Computing. vol. 7076, B. Panigrahi, P. Suganthan, S. Das, and S. Satapathy, Eds., ed: Springer Berlin Heidelberg, 2011, pp. 407-416. [DOI:10.1007/978-3-642-27172-4_50]
12. [12] L. Peng, K. Tu, Z. Wei, and P. L. Qing, "MSEAES: An egg non-destructive detecting expert system based on multi-sensor fusion," in Computer Application and System Modeling (ICCASM), 2010 International Conference on, 2010, pp. V4-269-V4-273.
13. [13] Y. Usui, K. Nakano, and Y. Motonaga, "A study of the development of non-destructive detection system for abnormal eggs," in EFITA Conference. Debrecen. Hungary, 2003.
14. [14] F. Bamelis, K. Tona, J. De Baerdemaeker, and E. Decuypere, "Detection of early embryonic development in chicken eggs using visible light transmission," British poultry science, vol. 43, pp. 204-212, 2002. [DOI:10.1080/00071660120121409] [PMID]
15. [15] K. C. Lawrence, D. P. Smith, W. R. Windham, G. W. Heitschmidt, and B. Park, "Egg embryo development detection with hyperspectral imaging," in Optics East 2006, 2006, pp. 63810T-63810T-8. [DOI:10.1117/12.686303]
16. [16] L. Liu and M. Ngadi, "Detecting fertility and early embryo development of chicken eggs using near-infrared hyperspectral imaging," Food and Bioprocess Technology, vol. 6, pp. 2503-2513, 2013. [DOI:10.1007/s11947-012-0933-3]
17. [17] D. Smith, K. Lawrence, and G. Heitschmidt, "Fertility and embryo development of broiler hatching eggs evaluated with a hyperspectral imaging and predictive modeling system," International journal of poultry science, vol. 7, pp. 1001-1004, 2008.
18. [18] V. C. Patel, R. W. McClendon, and J. W. Goodrum, "Development and evaluation of an expert system for egg sorting," Computers and Electronics in Agriculture, vol. 20, pp. 97-116, 7// 1998.
19. [19] X. Deng, Q. Wang, H. Chen, and H. Xie, "Eggshell crack detection using a wavelet-based support vector machine," Computers and Electronics in Agriculture, vol. 70, pp. 135-143, 1// 2010.
20. [20] K. Das and M. Evans, "Detecting fertility of hatching eggs using machine vision. I. Histogram characterization method," Transactions of the ASAE (USA), 1992.
21. [21] K. Das and M. Evans, "Detecting fertility of hatching eggs using machine vision. II. Neural network classifiers," Transactions of the ASAE (USA), 1992.
22. [22] Z. Zhu and M. Ma, "The identification of white fertile eggs prior to incubation based on machine vision and least square support vector machine," African Journal of Agricultural Research, vol. 6, pp. 2699-2704, 2011.
23. [23] C.-S. Lin, P. T. Yeh, D.-C. Chen, Y.-C. Chiou, and C.-H. Lee, "The identification and filtering of fertilized eggs with a thermal imaging system," Computers and Electronics in Agriculture, vol. 91, pp. 94-105, 2// 2013.
24. [24] K. Zuiderveld, "Contrast limited adaptive histogram equalization," in Graphics gems IV, 1994, pp. 474-485.
25. [25] R. C. Gonzalez and R. E. Woods, "Book on "Digital image processing"," ed: Prentice-Hall of India Pvt. Ltd, 2005.
26. [26] E. Alpaydin, Introduction to machine learning: MIT press, 2014.
27. [27] G. Bradski and A. Kaehler, Learning OpenCV: Computer vision with the OpenCV library: " O'Reilly Media, Inc.", 2008.
28. [28] OpenCV. (2014). Available: http://sourceforge.net/projects/opencvlibrary/
29. [29] L. Fausett, Fundamentals of neural networks: architectures, algorithms, and applications: Prentice-Hall, Inc., 1994.
30. [30] R. O. Duda, P. E. Hart, and D. G. Stork, Pattern classification: John Wiley & Sons, 2012.
31. [31] V. N. Vapnik and V. Vapnik, Statistical learning theory vol. 1: Wiley New York, 1998.

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