Kombinasi Fitur Bentuk, Warna dan Tekstur untuk Identifikasi Kesuburan Telur Ayam Kampung Sebelum Inkubasi

Rohman Dijaya, Nanik Suciati, Darlis Herumurti


Abstract. In the chicken nursery industry (doc) hatching efficiency is obtained by observing the eggs through candling before the incubation process. To sort out infertile eggs the use of fertility image identification thought egg candling is needed before incubation. The focus of this study is to combine the features of shape, texture and color to the area and egg yolk to determine the most dominant features in the image representing firtile egg candling. Features used in this study are the feature of forms: roundness, elongation, Index, Ellips Varriance and Circularity Ratio, moment invariant texture features of the area and the egg yolk, and features HSI color in egg yolks area. The test results show that the highest accuracy is on the features of the new forms of egg yolk with an accuracy of 76.67%. The second highest is shown by the combination of form features (Circularity Ratio, Ellips Varriance) and texture features in the area moment yolk color features HSI with 81.67% accuracy using SVM classification method.

Keywords: Egg candling imagery, fertile, infertile, incubation


Abstrak. Pada industri pembibitan ayam (doc) efisiensi penetasan telur ayam didapatkan dengan melakukan candling (peneropongan telur) sebelum proses inkubasi menggunakan mesin tetas. Untuk mengklasifikasikan telur fertile dan infertile dibutuhkan identifikasi kesuburan telur menggunakan citra candling sebelum inkubasi. Fokus dari penelitian ini adalah mengkombinasikan fitur bentuk, tekstur dan warna pada area kuning telur dan telur untuk mengetahui fitur yang paling dominan dalam merepresentasikan citra candling telur ayam kampung. Fitur yang digunakan dalam penelitian ini adalah fitur bentuk (Roundness, Elongation, Index, Ellips Varriance dan Circularity Ratio), fitur tektur moment invarian dari area telur dan kuning telur dan fitur warna HSI pada area kuning telur. Hasil pengujian menunjukkan akurasi tertinggi pada fitur bentuk kuning telur baru dengan akurasi 76,67% dan kombinasi fitur bentuk (Circularity Ratio, Ellips Varriance), fitur tekstur moment pada area kuning telur dengan fitur warna HSI dengan akurasi 81,67 % menggunakan metode klasifikasi SVM.

Kata Kunci: Citra candling telur, fertile, infertile, inkubasi.

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. Arivazhagan, S., Shebiah, R. N., Sudharsan, H., Kannan, R. R., & Ramesh, R. 2013. External and Internal Defect Detection of Egg using Machine Vision. Journal of Emerging Trends in Computing and Information Sciences, 4(3), 257-262.

. Bamelis, F. R., Tona, K., De Baerdemaeker, J. G., & Decuypere, E. M. 2002. Detection of early embryonic development in chicken eggs using visible light transmission. British poultry science, 43(2), 204-212.

. Das, K., & Evans, M. D. 1992a. Detecting fertility of hatching eggs using machine vision I. Histogram characterization method. Transactions of the ASAE, 35(4), 1335-1341.

. Das, K., & Evans, M. D. 1992b. Detecting fertility of hatching eggs using machine vision II: Neural network classifiers. Transactions of the ASAE, 35(6), 2035-2041.

. Gonzalez, R. C., Woods, R. E., & Eddins, S. L. 2004. Digital image processing using MATLAB. Pearson Prentice Hall.

. Lawrence, K. C., Smith, D. P., Windham, W. R., Heitschmidt, G. W., & Park, B. 2006. Egg embryo development detection with hyperspectral imaging. In Optics East 2006 (pp. 63810T-63810T). International Society for Optics and Photonics.

. Park, F. 2011. Shape Descriptor/Feature Extraction Techniques. (Online), (http://www.math.uci.edu/icamp/summer/research_11/park/shape_descriptors_survey.pdf)

. Patel, V. C., McClendon, R. W., & Goodrum, J. W. 1998. Development and evaluation of an expert system for egg sorting. Computers and Electronics in Agriculture, 20(2), 97-116.

. Ramteke, R. J. 2010. Invariant moments based feature extraction for handwritten devanagari vowels recognition. International Journal of Computer Applications, 1(18), 1-5.

. Shah Rizam, M. S. B., Farah Yasmin, A. R., Ahmad Ihsan, M. Y., & Shazana, K. 2009. Non- destructive watermelon ripeness determination using image processing and artificial neural network (ANN). International Journal of Electrical and Computer Engineering, 4(6).

. Shan, B. 2010. Fertility Detection of Middle-stage Hatching Egg in Vaccine Production Using Machine Vision. In Education Technology and Computer Science (ETCS), 2010 Second International Workshop on (Vol. 3, pp. 95-98). IEEE.

. Taniguchi, R. 2007. U.S. Patent No. 7,167,579. Washington, DC: U.S. Patent and Trademark Office.

. Wang, Q., Deng, X., Ren, Y., Ding, Y., Xiong, L., Ping, Z., & Wang, S. 2009. Egg freshness detection based on digital image technology. Scientific Research and Essay, 4(10), 1073-1079.

. Yang, M., Kpalma, K., & Ronsin, J. 2008. A survey of shape feature extraction techniques. Pattern recognition, 43-90.

. Youxian, W. Q. R. Y. W. 2006. Study on Non-destructive Detection Method for Fresh Degree of Eggs Based on BP Neural Network [J]. Transactions of the Chinese Society for Agricultural Machinery, 1, 029.

. Zhu, Z., & Ma, M. 2011. The identification of white fertile eggs prior to incubation based on machine vision and least square support vector machine. African Journal of Agricultural Research, 6(12), 2699-2704.

DOI: https://doi.org/10.24002/jbi.v7i3.659


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