Identification of chicken egg fertility using SVM classifier based on first-order statistical feature extraction


Shoffan Saifullah(1*); Andiko Putro Suryotomo(2);

(1) Universitas Pembangunan Nasional Veteran Yogyakarta
(2) Universitas Pembangunan Nasional Veteran Yogyakarta
(*) Corresponding Author

  

Abstract


This study aims to identify chicken eggs fertility using the support vector machine (SVM) classifier method. The classification basis used the first-order statistical (FOS) parameters as feature extraction in the identification process. This research was developed based on the processs identification process, which is still manual (conventional). Although currently there are many technologies in the identification process, they still need development. Thus, this research is one of the developments in the field of image processing technology. The sample data uses datasets from previous studies with a total of 100 egg images. The egg object in the image is a single object. From these data, the classification of each fertile and infertile egg is 50 image data. Chicken egg image data became input in image processing, with the initial process is segmentation. This initial segmentation aims to get the cropped image according to the object. The cropped image is repaired using image preprocessing with grayscaling and image enhancement methods. This method (image enhancement) used two combination methods: contrast limited adaptive histogram equalization (CLAHE) and histogram equalization (HE). The improved image becomes the input for feature extraction using the FOS method. The FOS uses five parameters, namely mean, entropy, variance, skewness, and kurtosis. The five parameters entered into the SVM classifier method to identify the fertility of chicken eggs. The results of these experiments, the method proposed in the identification process has a success percentage of 84.57%. Thus, the implementation of this method can be used as a reference for future research improvements. In addition, it may be possible to use a second-order feature extraction method to improve its accuracy and improve supervised learning for classification.

Keywords


Egg Fertility; Feature Extraction; Identification; Image Processing; Machine Learning

  
  

Full Text:

PDF
  

Article Metrics

Abstract view: 780 times
PDF view: 215 times 		     

Digital Object Identifier

doi  https://doi.org/10.33096/ilkom.v13i3.937.285-293 		  

Cite

How to cite item

References


T. T. Nkukwana, Global poultry production: Current impact and future outlook on the South African poultry industry, S. Afr. J. Anim. Sci., vol. 48, no. 5, p. 869, Jan. 2019, doi: 10.4314/sajas.v48i5.7.

G. R. Gowane, A. Kumar, and C. Nimbkar, Challenges and opportunities to livestock breeding programmes in India, J. Anim. Breed. Genet., vol. 136, no. 5, pp. 329338, Sep. 2019, doi: 10.1111/jbg.12391.

J. Dong et al., Identification of unfertilized duck eggs before hatching using visible/near infrared transmittance spectroscopy, Comput. Electron. Agric., vol. 157, pp. 471478, Feb. 2019, doi: 10.1016/j.compag.2019.01.021.

N. A. Fadchar and J. C. Dela Cruz, Prediction Model for Chicken Egg Fertility Using Artificial Neural Network, in 2020 IEEE 7th International Conference on Industrial Engineering and Applications (ICIEA), Apr. 2020, pp. 916920, doi: 10.1109/ICIEA49774.2020.9101966.

A. Salamon, Fertility and Hatchability in Goose Eggs: A Review, Int. J. Poult. Sci., vol. 19, no. 2, pp. 5165, Jan. 2020, doi: 10.3923/ijps.2020.51.65.

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 V. A. Permadi, Comparison of Egg Fertility Identification based on GLCM Feature Extraction using Backpropagation and K-means Clustering Algorithms, in 2019 5th International Conference on Science in Information Technology (ICSITech), 2019, pp. 140145.

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. 91519156, 2018, doi: 10.1166/asl.2018.12115.

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. 200208, 2017, doi: 10.11591/ijece.v7i1.pp200-208.

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 and A. P. Suryotomo, Thresholding and hybrid CLAHE-HE for chicken egg embryo segmentation, Int. Conf. Commun. Inf. Technol., 2021.

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. 5360, Mar. 2020, doi: 10.29080/systemic.v5i2.798.

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.

J. Dong, B. Lu, K. He, B. Li, B. Zhao, and X. Tang, Assessment of hatching properties for identifying multiple duck eggs on the hatching tray using machine vision technique, Comput. Electron. Agric., vol. 184, p. 106076, May 2021, doi: 10.1016/j.compag.2021.106076.

A. O. Adegbenjo, L. Liu, and M. O. Ngadi, Non-Destructive Assessment of Chicken Egg Fertility, Sensors, vol. 20, no. 19, p. 5546, Sep. 2020, doi: 10.3390/s20195546.

T. Intarakumthornchai and R. Kesvarakul, Double yolk eggs detection using fuzzy logic, PLoS One, vol. 15, no. 11, p. e0241888, Nov. 2020, doi: 10.1371/journal.pone.0241888.

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. 140145, doi: 10.1109/ICSITech46713.2019.8987496.

S. Saifullah, K-Means Clustering for Egg Embryos Detection Based-on Statistical Feature Extraction Approach of Candling Eggs Image, SINERGI, vol. 25, no. 1, pp. 4350, 2020, doi: 10.22441/sinergi.2021.1.006.

S. Saifullah, Segmentation for embryonated Egg Images Detection using the K-Means Algorithm in Image Processing, 2020 Fifth Int. Conf. Informatics Comput., pp. 17, Nov. 2020, doi: 10.1109/ICIC50835.2020.9288648.

R. Jamal, K. Manaa, M. Rabeea, and L. Khalaf, Traffic control by digital imaging cameras?, Emerg. Trends Image Process. Comput. Vis. Pattern Recognit., pp. 231247, 2015, doi: 10.1016/B978-0-12-802045-6.00015-6.

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. 15, 2018.

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. 1013, doi: 10.1109/EIConCIT.2018.8878517.

S. Das, S. Sengupta, V. B. Shambhu, and D. P. Ray, Defect detection of jute fabric using image processing, Econ. Aff., vol. 61, no. 2, p. 273, 2016, doi: 10.5958/0976-4666.2016.00035.8.

A. Kadir, Dasar Pengolahan Citra dengan Delphi. Yogyakarta: Andi, 2013.

M. Zhou, K. Jin, S. Wang, J. Ye, and D. Qian, Color Retinal Image Enhancement Based on Luminosity and Contrast Adjustment, IEEE Trans. Biomed. Eng., vol. 65, no. 3, pp. 521527, Mar. 2018, doi: 10.1109/TBME.2017.2700627.

A. O. Salau and S. Jain, Feature Extraction: A Survey of the Types, Techniques, Applications, 2019 Int. Conf. Signal Process. Commun., pp. 158164, Mar. 2019, doi: 10.1109/ICSC45622.2019.8938371.

S. G. Mougiakakou, S. Golemati, I. Gousias, A. N. Nicolaides, and K. S. Nikita, Computer-aided diagnosis of carotid atherosclerosis based on ultrasound image statistics, Laws texture and neural networks, Ultrasound Med. Biol., vol. 33, no. 1, pp. 2636, Jan. 2007, doi: 10.1016/j.ultrasmedbio.2006.07.032.

A. H. Mohammed, B. Jameel, and F. Ali, Bootstrap technique for image detection, Period. Eng. Nat. Sci., vol. 8, no. 3, pp. 12801287, 2020, doi: 10.21533/pen.v8i3.1440.

N. Nabizadeh and M. Kubat, Brain tumors detection and segmentation in MR images: Gabor wavelet vs. statistical features, Comput. Electr. Eng., vol. 45, pp. 286301, Jul. 2015, doi: 10.1016/j.compeleceng.2015.02.007.

M. R. Ismael and I. Abdel-Qader, Brain Tumor Classification via Statistical Features and Back-Propagation Neural Network, 2018 IEEE Int. Conf. Electro/Information Technol., pp. 02520257, May 2018, doi: 10.1109/EIT.2018.8500308.

S. J. Russell and P. Norvig, Artificial Intelligence A Modern Approach. New Jersey: Pearson Education, Inc., 2010.

S. Dhakshina Kumar, S. Esakkirajan, S. Bama, and B. Keerthiveena, A microcontroller based machine vision approach for tomato grading and sorting using SVM classifier, Microprocess. Microsyst., vol. 76, p. 103090, Jul. 2020, doi: 10.1016/j.micpro.2020.103090.

S. Saifullah, Y. Fauziah, and A. S. Aribowo, Comparison of Machine Learning for Sentiment Analysis in Detecting Anxiety Based on Social Media Data, Jan. 2021, [Online]. Available: http://arxiv.org/abs/2101.06353.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2021 Shoffan Saifullah, Andiko P Suryotomo

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.