Classification of Egg Fertility on the Image of Kampong Chicken Egg Using the Frequency Distribution Feature and Naive Bayes Classifier Algorithm’s

Main Article Content

Aris Diantoro Irwan Budi Santoso

Abstract

Losses in hatching chicken eggs make breeders income decreased. The main cause of this because it is less effective and efficient in distinguishing the fertilities state in eggs. Detection of fertile and infertile eggs will automatically facilitate the selection and removal of fertile eggs and infertile eggs. This will bring more benefits to farms such as time efficiency and more selling power. Infertile eggs will provide the selling price if known as early as possible so as not to fail hatched. A Fuzzy C Means method and the Naive Bayes Classifier are designed to identify the fertility state of the egg. By putting eggs near a light source as well as a black background in dark spaces, then take the image with a high quality camera. From the resulting image the camera extracted features or distinctive features that distinguish between fertile and infertile eggs. The total amount of data used in this study is 350 eggs from the field survey. Training data used 250 data, 125 fertile egg image data and 125 infertile egg image data. While for data testing using 100 data, 50 data fertilized egg image and 50 data image infertile egg. Based on the results of the training data trials obtained the best accuracy is 93.2% at interval 4 with RGB feature, 50% at interval 3 on Grayscale feature. Accuracy results in the data testing test obtained by 87% on RGB features, 35% on Grayscale features.

Article Details

How to Cite
DIANTORO, Aris; SANTOSO, Irwan Budi. Classification of Egg Fertility on the Image of Kampong Chicken Egg Using the Frequency Distribution Feature and Naive Bayes Classifier Algorithm’s. Proceedings of the International Conference on Green Technology, [S.l.], v. 8, n. 1, p. 446-453, nov. 2017. ISSN 2580-7099. Available at: <http://conferences.uin-malang.ac.id/index.php/ICGT/article/view/659>. Date accessed: 29 mar. 2024. doi: https://doi.org/10.18860/icgt.v8i1.659.
Section
Technology Information

References

[1] Supartono W, Yunus M, Yuliando H. “Analisis Kelayakan Finansial Usaha Pemotongan Ayam Tradisional di Daerah Istimewa Yogyakarta” Teknik Industri Pertanian Universitas Gadjah Mada. 2000.
[2] Susanto E, Suliswanto. “Pengaruh Berat Telur terhadap Daya Tetas Telur Ayam Kampung”. Program Studi Peternakan Fakultas Peternakan. ISSN 2086 – 5201
[3] Zhu Z, Ma Meihu. “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(12), pp. 2699-2704, 18 June, 2011.
[4] D. P Smith et al. “Detection of fertility and early development of hatching eggs with hyperspectral imaging”. USDA, ARS, Russell Research Center, 950 College Station Road, Athens, GA 30605, USA. 2010
[5] Liu L, Ngadi M. O. “Detection of Chicken Egg Fertility and Early Embryo Development Using Hyperspectral Imaging”. Department of Bioresource Engineering, McGill University, Macdonald Campus.
[6] Wang Qiaohu et al. “egg freshness detection based on digital image technology”. College of Engineering and Technology, Huazhong Agricultural University, Wuhan, 430070, P.R. China. 2009
[7] Nawawi M. Z., dkk. “Klasifikasi Telur Fertil dan Infertil menggunakan Jaringan Syaraf Tiruan Multilayer Perceptron berdasarkan Ekstraksi Fitur Warna dan Bentuk”. Jurnal Teknologi dan Informasi. Univesitas Sumatera Utara. Vol 4 No.2, Desember 2015:100-109
[8] Santoso, Irwan Budi, “Membangun Gaussian Classifier dalam Mengenali Objek dalam Bentuk Image,” Matics, vol. 1, pp. 1–5, 2014.
[9] R. Webb and K. D. Copsey, Statistical Pattern Recognition, 3rd ed., Mathematics and Data Analysis Consultancy Malvern, United Kingdom: John Wiley & Sons Ltd., 2011.
[10] Santoso, Irwan Budi. Deteksi Non-RTH(Ruang Terbuka Hijau) Kota Malang Berbasis Citra Google Earth dengan Menggunakan Naïve Bayes Classifier. MATICS : Jurnal Ilmu Komputer dan Teknologi. Universitas Islam Maulana Malik Ibrahim Malang. 2015
[11] Hasan, M. Iqbal. 2001. Pokok-pokok Materi Statistik I (Statistik Deskriptif), Bumi Aksara. Jakarta.)
[12] Santoso, Irwan Budi. “Statistika I untuk Teknik Informatika”. UIN-MALIKI PRESS. Malang. 2013.