Artificial Intelligence Based Determination of Cracks in Eggshell Using Sound Signals

Year 2022, Volume: 26 Issue: 3, 579 - 589, 30.06.2022

Zekeriya Balcı İsmail Yabanova

https://doi.org/10.16984/saufenbilder.848213

Abstract

Although the egg is a cheap food source, it is one of the valuable nutritional sources for people because of its rich nutritional values. It is also among the most consumed foods in daily nutrition. With the increase in egg production, it is very difficult to collect them with the human power in the egg production farms, to classify them according to their weights and to separate the defective (dirty and broken) eggs. Therefore, the mechanization has become a necessity in large capacity production farms. Cracks and fractures may occur in the egg shell as a result of exposure to external factors such as the transportation of eggs. The cracks or fractures that are formed leave the egg vulnerable to disease-causing micro-organisms. Before the egg sorting and packing, the broken and cracked eggs must be separated. This process is commonly carried out with manpower by which it is very difficult to obtain the necessary efficiency. In this study, the egg crack detection was performed by using Support Vector Machines (SVM) and Artificial Neural Network (ANN). As a result of the application of studied methods, the accuracy values of crack detection process were 0.99 for ANN and 1 for SVM. In addition, a data acquisition and processing program was developed in LABVIEW environment to detect cracks in real time.

Keywords

Cracked egg detection, artificial neural networks, support vector machines, LABVIEW

References

• [1] Y. Li, S. Dhakal, Y. Peng, “A machine vision system for identification of micro-crack In eggshell”, Journal of Food Engineering, vol. 109, pp. 127-134, 2012.
• [2] J. Strnková, Š. Nedomová, “Eggshell crack detection using dynamic frequency analysis”, MENDEL International Conference on Soft Computing, Brno, pp. 603-608, 2013.
• [3] L. Sun, X.K. Bi, H. Lin, J.W. Zhao, J.R. Cai, “On-line detection of eggshell crack based on acoustic resonance analysis”, Journal of Food Engineering, vol. 116, no. 1, pp. 240-245, 2013.
• [4] H. Wang, J. Mao, J. Zhang, H. Jiang, J. Wang, "Acoustic feature extraction and optimization of crack detection for eggshell", Journal of Food Engineering, vol. 171, pp. 240-247, 2016.
• [5] B. De Ketelaere, P. Coucke, J. De Baerdemaeker, "Eggshell crack detection based on acoustic resonance frequency analysis, Journal of Agricultural Engineering Research, vol. 76, no. 2, pp. 157-163, 2000.
• [6] H. Lin, J.W. Zhao, Q.S. Chen, J.R. Cai, P. Zhou, "Eggshell crack detection based on acoustic response and support vector data description algorithm", European Food Research and Technology, vol. 230, no. 1, pp. 95-100, 2009.
• [7] X. Deng, Q. Xiaoyan, H. Chen, H. Xie, “Eggshell crack detection using a wavelet-based support vector machine”, Computers and Electronics in Agriculture, vol. 70, no. 1, pp. 135-143, 2010.
• [8] P. Li, Q. Wang, Q. Zhang, S. Cao, Y. Liu, T. Zhu, “Non-destructive detection on the egg crack based on wavelet transform”, International Conference on Future Computer Supported Education, Seoul, pp. 372-382, 2012.
• [9] C. Jin, L. Xie, Y. Ying, “Eggshell crack detection based on the time-domain acoustic signal of rolling eggs on a step-plate”, Journal of Food Engineering, vol. 153, pp. 53-62, 2015.
• [10] W. Fang, W. Youxian, "Detecting preserved eggshell crack using machine vision", International Conference of Information Technology-Computer Engineering and Management Sciences, ICM, Nankin, pp. 62-65, 2011.
• [11] M. Omid, M. Soltani, M. H. Dehrouyeh, S. S. Mohtasebi, H. Ahmadi, "An expert egg grading system based on machine vision and artificial intelligence techniques", Journal of Food Engineering, vol. 118, no. 1, pp. 70-77, 2013.
• [12] N. Öztürk, A. Gangal, “Görüntü işleme teknikleri ile beyaz yumurtalar üzerindeki yumurta kabuğu kusurlarının algılanması”, 22nd Signal Processing and Communications Applications Conference, Trabzon, pp. 810-813, 2014.
• [13] Wikipedia, “CompactRIO”, Available: https://en.wikipedia.org/wiki/CompactRIO, [Accessed: May 28, 2018].
• [14] National Instruments, “CompactRIO”, Available: http://www.ni.com/compactrio/, [Accessed: December 23, 2017].
• [15] E. Öztemel, “Yapay Sinir Ağları”, Papatya Yayınclık, Istanbul, Turkey, 2006.
• [16] A. Yangın, “Yapay sinir ağı teknikleri kullanarak eğitim yayıncılığı sektöründe veri madenciliği”, M.S. thesis, Comp. Eng. Dept., Aydın Univ., İstanbul,Turkey, 2017.
• [17] Ö. F. Sezer, “Sürekli tavlama hatlarında enerji giderinin kalite ve boyut değerlerine göre optimize edilmesi ve geçişlerde operatör davranışlarının modellenmesi”, Ph.D. diss., Sakarya Univ., Sakarya, Turkey, 2017.
• [18] A. Aksakal, “Türkiye'deki resmi dairelerde talep tarafı yönetimi ve yapay zeka uygulamaları”, M.S. thesis, Kırıkkale Univ., Kırıkkale, Turkey, 2017.
• [19] Ö. Karal, “Destek vektör regresyon ile EKG verilerinin sıkıştırılması”, Journal of the Faculty of Engineering and Architecture of Gazi University, vol. 2, pp. 742-756, 2018.
• [20] A. Yahyaouı, “Göğüs hastalıklarının teşhis edilmesinde makine öğrenmesi algoritmalarının kullanılması”, Ph.D. diss., Sakarya Univ., Sakarya, Turkey, 2018.
• [21] E. Tuncer, “Uyku evrelemesinde çeşitli dalgacık ve sınıflandırıcıların performans analizi”, M.S. thesis, Kocaeli Univ., Kocaeli, Turkey, 2015.
• [22] İ. Yabanova, M. Yumurtacı, “Destek vektör makineleri kullanarak dinamik yumurta ağırlıklarının sınıflandırılması”, Journal of the Faculty of Engineering and Architecture of Gazi University, vol. 33, no. 2, pp. 393-402, 2018.
• [23] T. Güneş, P. Ediz, “Yüz ifade analizinde öznitelik seçimi ve çoklu SVM sınıflandırıcılarına etkisi”, Journal of the Faculty of Engineering and Architecture of Gazi University”, vol. 24, no. 1, pp. 7-14, 2009.
• [24] İ. Aydın, M. Karaköse, E. Akın, “Zaman serisi veri madenciliği ve destek vektör makinalar kullanan yeni bir akıllı arıza sınıflandırma yöntemi”, Journal of the Faculty of Engineering and Architecture of Gazi University, vol. 23, no. 2, pp. 431-440, 2008.
• [25] S. Ekici, S. Yildirim, M. Poyraz, “Energy and entropy-based feature extraction for locating fault on transmission lines by using neural network and wavelet packet decomposition”, Expert Systems with Applications, vol. 34, no. 4, pp. 2937-2944, 2008.
• [26] A. Kutlu, C. Turan, “Elektronik deney modüllerinin LabView ile kontrolü”, Süleyman Demirel Üniversitesi Uluslararası Teknolojik Bilimler Dergisi, vol. 2, no. 3, pp. 1-8, 2010.
• [27] National Instruments, “LabVIEW analytics and machine learning toolkit”, Available: http://sine.ni.com/nips/cds/view/p/lang/en/nid/216169 /, [Accessed: March 14, 2018].
• [28] N. Bagherzadi, “Post-operative prognostic prediction of esophageal cancer cases using bayesian networks and support vector machines” M.S. thesis, Middle East Tech. Univ., Ankara, Turkey, 2014.