Detection of Chicken Egg Embryos using BW Image Segmentation and Edge Detection Methods
Abstract
This study aims to identify chicken egg embryos with the concept of image processing. This concept uses input and output in images. Thus the identification process, which was originally carried out using manual observation, was developed by computerization. Digital images are applied in identification by various image preprocessing, image segmentation, and edge detection methods. Based on these three methods, image processing has three processes: image grayscaling (convert to a grayscale image), image adjustment, and image enhancement. Image adjustment aims to clarify the image based on color correction. Meanwhile, image enhancement improves image quality, using histogram equalization (HE) and Contrast Limited Adaptive Histogram Equalization methods (CLAHE). Specifically for the image enhancement method, the CLAHE-HE combination is used for the improvement process. At the end of the process, the method used is edge detection. In this method, there is a comparison of various edge detection operators such as Roberts, Prewitt, Sobel, and canny. The results of edge detection using these four methods have the SSIM value respectively 0.9403; 0.9392; 0.9394; 0.9402. These results indicate that the SSIM values of the four operators have the same or nearly the same value. Thus, the edge detection method can provide good edge detection results and be implemented because the SSIM value is close to 1.00 (more than 0.93). Image segmentation detected object (egg and embryo), and the continued process by edge detection showed clearly edge of egg and embryo.
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References
J. Dong et al., “Prediction of infertile chicken eggs before hatching by the Naïve-Bayes method combined with visible near infrared transmission spectroscopy,” Spectrosc. Lett., pp. 1–10, Apr. 2020, doi: 10.1080/00387010.2020.1748061.
L. Huang, A. He, M. Zhai, Y. Wang, R. Bai, and X. Nie, “A Multi-Feature Fusion Based on Transfer Learning for Chicken Embryo Eggs Classification,” Symmetry (Basel)., vol. 11, no. 5, p. 606, May 2019, doi: 10.3390/sym11050606.
S. Saifullah and A. P. Suryotomo, “Thresholding and Hybrid CLAHE-HE for Chicken Egg Embryo Segmentation,” in 2021 International Conference on Communication & Information Technology (ICICT), Jun. 2021, pp. 268–273, doi: 10.1109/ICICT52195.2021.9568444.
E. H. Rachmawanto et al., “Eggs Classification based on Egg Shell Image using K-Nearest Neighbors Classifier,” in 2020 International Seminar on Application for Technology of Information and Communication (iSemantic), Sep. 2020, pp. 50–54, doi: 10.1109/iSemantic50169.2020.9234305.
K. Zhang et al., “Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography,” Cell, vol. 181, no. 6, pp. 1423-1433.e11, Jun. 2020, doi: 10.1016/j.cell.2020.04.045.
L. Geng, Y. Hu, Z. Xiao, and J. Xi, “Fertility Detection of Hatching Eggs Based on a Convolutional Neural Network,” Appl. Sci., vol. 9, no. 7, p. 1408, Apr. 2019, doi: 10.3390/app9071408.
S. Saifullah and Andiko Putro Suryotomo, “Chicken Egg Fertility Identification using FOS and BP-Neural Networks on Image Processing,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 5, pp. 919–926, Oct. 2021, doi: 10.29207/resti.v5i5.3431.
Sunardi, A. Yudhana, and S. Saifullah, “Identification of Egg Fertility Using Gray Level Co-Occurrence Matrix and Backpropagation,” Adv. Sci. Lett., vol. 24, no. 12, pp. 9151–9156, 2018, doi: 10.1166/asl.2018.12115.
W. Lumchanow and S. Udomsiri, “Combination of GLCM and KNN Classification for Chicken Embryo Development Recognition,” in 2019 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT-NCON), Jan. 2019, pp. 322–325, doi: 10.1109/ECTI-NCON.2019.8692272.
S. Saifullah, “K-Means Clustering for Egg Embryo’s Detection Based-on Statistical Feature Extraction Approach of Candling Eggs Image,” SINERGI, vol. 25, no. 1, pp. 43–50, 2020, doi: 10.22441/sinergi.2021.1.006.
Q. Tong et al., “Detection of embryo mortality and hatch using thermal differences among incubated chicken eggs,” Livest. Sci., vol. 183, pp. 19–23, Jan. 2016, doi: 10.1016/j.livsci.2015.11.004.
L. Hai-ling, C. Jian-rong, S. Li, Y. Lei-ming, and L. Meng-lei, “Research on the Discrimination of Hatching Eggs Activity Based on Thermal Imaging: A Food Nondestructive Testing Practice,” Int. J. Smart Home, vol. 10, no. 2, pp. 175–186, Feb. 2016, doi: 10.14257/ijsh.2016.10.2.17.
Sunardi, A. Yudhana, and S. Saifullah, “Identity analysis of egg based on digital and thermal imaging: Image processing and counting object concept,” Int. J. Electr. Comput. Eng., vol. 7, no. 1, pp. 200–208, 2017, doi: 10.11591/ijece.v7i1.pp200-208.
Sunardi, A. Yudhana, and S. Saifullah, “Thermal Imaging Untuk Identifikasi Telur,” in Prosiding Konferensi Nasional Ke-4 Prosiding Konferensi Nasional Ke- 4 Asosiasi Program Pascasarjana Perguruan Tinggi Muhammadiyah (APPPTM), May 2016, no. May, p. 157.
S. Saifullah and V. A. Permadi, “Comparison of Egg Fertility Identification based on GLCM Feature Extraction using Backpropagation and K-means Clustering Algorithms,” in Proceeding - 2019 5th International Conference on Science in Information Technology: Embracing Industry 4.0: Towards Innovation in Cyber Physical System, ICSITech 2019, Oct. 2019, pp. 140–145, doi: 10.1109/ICSITech46713.2019.8987496.
S. Saifullah, “K-means Segmentation Based-on Lab Color Space for Embryo Egg Detection,” arXiv Prepr. arXiv2103.02288, Mar. 2021, [Online]. Available: http://arxiv.org/abs/2103.02288.
A. Yudhana, Sunardi, and S. Saifullah, “Segmentation comparing eggs watermarking image and original image,” Bull. Electr. Eng. Informatics, vol. 6, no. 1, pp. 47–53, 2017, doi: 10.11591/eei.v6i1.595.
D. Indra, T. Hasanuddin, R. Satra, and N. R. Wibowo, “Eggs Detection Using Otsu Thresholding Method,” in 2018 2nd East Indonesia Conference on Computer and Information Technology (EIConCIT), Nov. 2018, pp. 10–13, doi: 10.1109/EIConCIT.2018.8878517.
J. G. C. Rancapan, E. R. Arboleda, J. L. Dioses, and R. M. Dellosa, “Egg fertility detection using image processing and fuzzy logic,” Int. J. Sci. Technol. Res., vol. 8, no. 10, pp. 3228–3230, Oct. 2019.
S. Saifullah, “Segmentasi Citra Menggunakan Metode Watershed Transform Berdasarkan Image Enhancement Dalam Mendeteksi Embrio Telur,” Syst. Inf. Syst. Informatics J., vol. 5, no. 2, pp. 53–60, 2019, doi: 10.29080/systemic.v5i2.798.
L. Liu and M. O. Ngadi, “Detecting Fertility and Early Embryo Development of Chicken Eggs Using Near-Infrared Hyperspectral Imaging,” Food Bioprocess Technol., vol. 6, no. 9, pp. 2503–2513, Sep. 2013, doi: 10.1007/s11947-012-0933-3.
S. Yang, X. Han, and Y. Chen, “Three-Dimensional Embryonic Image Segmentation and Registration Based on Shape Index and Ellipsoid-Fitting Method,” J. Comput. Biol., vol. 26, no. 2, pp. 128–142, Feb. 2019, doi: 10.1089/cmb.2018.0165.
A. A. Budiarto, M. B. Fatkhurrozi, and M. I. Setyowati, “Implementasi Operator Canny untuk Identifikasi Fertilitas Telur Ayam Buras,” Theta Omega J. Electr. Eng. Comput. Inf. Technol., vol. 1, no. 2, pp. 1–7, 2020, doi: 10.31002/jeecit.v1i2.3547.
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,” Comput. Electron. Agric. Comput. Electron. Agric., vol. 91, pp. 94–105, 2012.
W. Qiaohua, F. Dandan, M. Meihu, and Z. Tao, “Differentiating between fertilized and unfertilized eggs prior to incubation based on oxygen flux measurement,” Int. J. Agric. Biol. Eng., vol. 10, no. 4, pp. 243–251, 2017, doi: 10.25165/j.ijabe.20171004.2606.
W. Koodtalang, T. Sangsuwan, and A. Rerkratn, “Non-destructive Fertility Detection of Multiple Chicken Eggs Using Image Processing and Convolutional Neural Network,” in IOP Conference Series: Materials Science and Engineering, Jul. 2020, vol. 895, no. 1, doi: 10.1088/1757-899X/895/1/012013.
S. Saifullah, Sunardi, and A. Yudhana, “Analisis Perbandingan Pengolahan Citra Asli dan Hasil Croping Untuk Identifikasi Telur,” J. Tek. Inform. dan Sist. Inf., vol. 2, no. 3, pp. 341–350, 2016.
S. Saifullah, “Segmentation for embryonated Egg Images Detection using the K-Means Algorithm in Image Processing,” 2020 5th Int. Conf. Informatics Comput. ICIC 2020, Nov. 2020, doi: 10.1109/ICIC50835.2020.9288648.
S. Saifullah and A. P. Suryotomo, “Thresholding and hybrid CLAHE-HE for chicken egg embryo segmentation,” Int. Conf. Commun. Inf. Technol., 2021.
S. Saifullah, “Analisis Perbandingan HE dan CLAHE pada Image Enhancement dalam Proses Segmentasi Citra untuk Deteksi Fertilitas Telur,” J. Nas. Pendidik. Tek. Inform. JANAPATI, vol. 9, no. 1, 2020.
S. Saifullah, A. P. Suryotomo, and B. Yuwono, “Fish Detection Using Morphological Approach Based-on K-Means Segmentation,” Compiler, vol. 10, no. 1, Jan. 2021, doi: 10.28989/compiler.v10i1.946.
A. Muhammed and A. R. Pais, “A Novel Fingerprint Image Enhancement based on Super Resolution,” in 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), 2020, pp. 165–170.
S. Saifullah, “Segmentasi Citra Menggunakan Metode Watershed Transform Berdasarkan Image Enhancement Dalam Mendeteksi Embrio Telur,” Syst. Inf. Syst. Informatics J., vol. 5, no. 2, pp. 53–60, Mar. 2020, doi: 10.29080/systemic.v5i2.798.
Y. Xie, L. Ning, M. Wang, and C. Li, “Image Enhancement Based on Histogram Equalization,” J. Phys. Conf. Ser., vol. 1314, p. 012161, Oct. 2019, doi: 10.1088/1742-6596/1314/1/012161.
K. G. Dhal, A. Das, S. Ray, J. Gálvez, and S. Das, “Histogram Equalization Variants as Optimization Problems: A Review,” Arch. Comput. Methods Eng., Apr. 2020, doi: 10.1007/s11831-020-09425-1.
E. Park, S. Lohumi, and B.-K. Cho, “Line-scan imaging analysis for rapid viability evaluation of white-fertilized-egg embryos,” Sensors Actuators B Chem., vol. 281, pp. 204–211, Feb. 2019, doi: 10.1016/j.snb.2018.10.109.
H. Yeganeh, A. Ziaei, and A. Rezaie, “A novel approach for contrast enhancement based on Histogram Equalization,” 2008 Int. Conf. Comput. Commun. Eng., pp. 256–260, May 2008, doi: 10.1109/ICCCE.2008.4580607.
H. Y. Yang, J. X. Zhao, G. H. Xu, and S. Liu, “A Survey of Color Image Segmentation Methods,” Softw. Guid., vol. 17, no. 4, pp. 1–5, 2018.
S. Saifullah, S. Sunardi, and A. Yudhana, “Perbandingan segmentasi pada citra asli dan citra kompresi wavelet untuk identifikasi telur,” Ilk. J. Ilm., vol. 8, no. 3, pp. 190–196, Dec. 2016, doi: 10.33096/ilkom.v8i3.75.190-196.
S. Saifullah, “Segmentation for embryonated Egg Images Detection using the K-Means Algorithm in Image Processing,” 2020 Fifth Int. Conf. Informatics Comput., pp. 1–7, Nov. 2020, doi: 10.1109/ICIC50835.2020.9288648.
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