THE DETECTION OF EGGSHELL CRACKS USING DIFFERENT CLASSIFIERS

Abstract

Chicken eggs, which are widely consumed in daily life due to their rich nutritional values, are also used in many products. The increasing need for eggs must be met quickly for various circumstances. Eggs are subjected to various impacts and shaken from production to packaging. In some cases, these effects cause an eggshell to crack. While these cracks are sometimes visible, they are sometimes micro-sized and cannot be seen. The cracks on the egg allow harmful micro-organisms to spoil the egg in a short time. In this study, acoustic signals generated by a mechanical effect to the eggs were recorded for 0.2 seconds at 50 kHz sampling frequency using a microphone. To determine the active part in the collected acoustic signal data, a clipping process was implemented by a thresholding process. Thus, the exactly correct moment of mechanical contact on the eggshell was easily detected. After passing the determined threshold value, statistical parameters such as min, max, difference, mean, standard deviation, skewness and kurtosis were extracted from the data obtained, and 7-dimensional feature vectors were created. Finally, the Common Vector Approach (CVA) is applied on the extracted feature vectors, 100% success rate has been achieved for the test data set. The ANN and SVM classifiers in where the same feature vectors are treated were used for the comparison purpose, and exactly the same classification rates are attained; however, the less number of eggs are tested with the ANN and SVM classifiers in the same amount of time. With the proposed mechanical system and classification methodology, it takes about 0.2008 seconds to determine whether the shells of eggs are cracked/intact. Therefore, the proposed combination of the feature vectors based on statistical features and CVA as a classifier for the detection of cracks on eggshells is notably appropriate especially for industrial applications in terms of speed and accuracy aspects.

Keywords

• Egg
• Eggshell
• Common Vector Approach
• CompactRIO

References

1. [1] Singh M, Brar J. Egg safety in the realm of preharvest food safety. Microbiol. Spectr 2016; 4(4):o. 4, Aug. 2016, DOI: 10.1128/microbiolspec.PFS-0005-2014.
2. [2] Mazzuco H, Bertechini A-G. Critical points on egg production: causes, importance, and incidence of eggshell breakage and defect. Ciência e Agrotecnologia, 2013; 38(1): 7–14.
3. [3] Van Mourik S, Alders B, Helderman F, van de Ven L-J-F, Koerkamp P-W-G-G. Predicting hairline fractures in eggs of mature hens. Poult. Sci., 2017; 96(6): 1956–1962.
4. [4] Widdicombe J-P, Rycroft A-N, Gregory N-G. Hazards with cracked eggs and their relationship to eggshell strength. J. Sci. Food Agric, 2009; 89(2): 201–205.
5. [5] Wu L, Wang Q, Jie D, Wang S, Zhu Z, Xiong L. Detection of crack eggs by image processing and soft-margin support vector machine J. Comput. Methods Sci. Eng., 2018; (18)1: 21–31.
6. [6] Abdullah M-H, Nashat S, Anwar S-A, Abdullah M-Z. A framework for crack detection of fresh poultry eggs at visible radiation. Comput. Electron. Agric., 2017; 141: 81–95.
7. [7] Fang W, Youxian W. Detecting preserved eggshell crack using machine vision. In 2011 International Conference of Information Technology, Computer Engineering and Management Sciences, 2011; 3: 62–65.
8. [8] Omid M, Soltani M, Dehrouyeh M-H, Mohtasebi S-S, Ahmadi H. An expert egg grading system based on machine vision and artificial intelligence techniques. J. Food Eng, 2013; 118(1): 70–77.

9. [9] Wang F, Zhang S, Tan Z. Non-destructive crack detection of preserved eggs using a machine vision and multivariate analysis. Wuhan Univ. J. Nat. Sci, 2017; 22(3): 257–262.
10. [10] Öztürk N. Görüntü işleme teknikleri ile beyaz yumurtalar üzerindeki yumurta kabuğu kusurlarının algılanması. MSc, Karadeniz Teknik Üniversitesi, Trabzon, Turkey, 2014.
11. [11] Abbaspour-gılandeh Y, Azizi A. Identification of cracks in eggs shell using computer vision and hough transform. Yüzüncü Yıl Üniversitesi Tarım Bilimleri Dergisi, 2019; 28(4): 375-383.
12. [12] Türkoğlu M. Yumurta kabuğu görüntülerinde kırık tespiti için daha hızlı bölgesel tabanlı çok katmanlı evrişimsel sinir ağları. Gazi University Journal of Science, 2021; 9(1): 148-157.
13. [13] Chen H, Ma J, Zhuang Q, Zhao S, Xie Y. Submillimeter crack detection technology of eggs based on improved light source. In IOP Conference Series: Earth and Environmental Science, 22-25 January 2021; Guangzhou, China. 697.
14. [14] Dong J, Lu B, He K, Li B, Zhao B, Tang X. Assessment of hatching properties for identifying multiple duck eggs on the hatching tray using machine vision technique. Computers and Electronics in Agriculture, 2021; 184:106076.
15. [15] Orlova Y, Linker R, Spektor B. Expansion of cracks in chicken eggs exposed to sub-atmospheric pressure. Biosyst. Eng., 2012; 112(4): 278–284.
16. [16] Lawrence K-C, Yoon S-C, Jones D-R, Heitschmidt G-W, Park B, Windham W-R. Modified pressure system for imaging egg cracks. Trans. ASABE, 2009; 52(3): 983–990.
17. [17] Lawrence K-C, Yoon S-C, Heitschmidt G-W, Jones D-R, Park B. Imaging system with modified-pressure chamber for crack detection in-shell eggs. Sens. Instrum. Food Qual. Saf., 2008; 2(2): 116–122.
18. [18] Li Y, Dhakal S, Peng Y. A machine vision system for identification of micro-crack in eggshell. J. Food Eng., 2012; 109(1): 127–134.
19. [19] Priyadumkol J, Kittichaikarn C, Thainimit S. Crack detection on unwashed eggs using image processing. J. Food Eng., 2017; 209: 76–82.
20. [20] Wang H, Mao J, Zhang J, Jiang H, Wang J. Acoustic feature extraction and optimization of crack detection for eggshell. J. Food Eng.,. 2016; 171: 240–247.
21. [21] Lin H, Zhao J, Chen Q, Cai J, Zhou P. Eggshell crack detection based on acoustic response and support vector data description algorithm. Eur. food Res. Technol. 2009; 230(1): 95–100.
22. [22] Deng X, Wang Q, Chen H, Xie H. Eggshell crack detection using a wavelet-based support vector machine. Comput. Electron. Agric. 2010; 70(1): 135–143.
23. [23] Zhao Y, Wang J, Lu Q, Jiang R. Pattern recognition of eggshell crack using PCA and LDA. Innov. Food Sci. Emerg. Technol. 2010; 11(3): 520–525.
24. [24] Ding T, Lu W, Zhang C, Du J, Ding W, Zhao X. Eggshell crack identification based on Welch power spectrum and generalized regression neural network (GRNN). Food Sci 2015; 36: 156–160.
25. [25] Wang S-C, Ren Y-L, Chen H, Xiong L-R, Wen Y-X. Detection of cracked-shell eggs using acoustic signal and fuzzy recognition. Transactions CSAE 2004; 20(4): 130–132.
26. [26] Strnková J, Nedomová Š. Eggshell crack detection using dynamic frequency analysis. MENDELNET; 2013; Brno. 603-608.
27. [27] Jin C, Xie L, Ying Y. Eggshell crack detection based on the time-domain acoustic signal of rolling eggs on a step-plate, J. Food Eng., 2015; 153: 53–62.
28. [28] Li P, Wang Q, Zhang Q, Cao S, Liu Y, Zhu T. Non-destructive detection on the egg crack based on wavelet transform. IERI Procedia, 2012; 2: 372–382.
29. [29] Sun L, Feng S, Chen C, Liu X, Cai J. Identification of eggshell crack for hen egg and duck egg using correlation analysis based on acoustic resonance method. J. Food Process Eng., 2020;13430.
30. [30] CompactRIO - Wikipedia. https://en.wikipedia.org/wiki/CompactRIO. (accessed Feb. 09, 2020).
31. [31] LabVIEW - Vikipedi. https://tr.wikipedia.org/wiki/LabVIEW (accessed Feb. 09, 2021).
32. [32] Elibol S-G, Yumurtacı M, Ergin S, Yabanova İ. Classıfıcatıon of dynamıc EGG weıghts usıng feature extractıon methods. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering, 2020; 21(4):499-513.
33. [33] Gülmezoğlu M-B, Dzhafarov V, Keskin M, Barkana A. A novel approach to isolated word recognition, IEEE Trans. on Acoustic Speech and Signal Processing, 1999; 7(6): 620-628.
34. [34] Gülmezoğlu M-B, Dzhafarov V. Barkana A. The common vector approach and its relation to principal component analysis. IEEE Trans. on Speech and Audio Processing, 2001; 9(6): 655-662.
35. [35] Gülmezoğlu M-B, Dzhafarov V, Edizkan R, Barkana A. The common vector approach and its comparison with other subspace methods in case of sufficient data. Computer Speech and Language, 2007; 21: 266-281.
36. [36] Gülmezoğlu, M-B, Ergin S. An approach for bearing fault detection in electrical motors, European Trans. on Electrical Power, 2007; 17(6): 628-641.
37. [37] Oja E. Subspace methods of pattern recognition. John Wiley and Sons, Inc.: New York, 1983.
38. [38] Gülmezoğlu M-B, Dzhafarov V, Edizkan R, Barkana A. The common vector approach and its comparison with other subspace methods in case of sufficient data, Computer Speech and Language, 2007; 21: 266-281.
39. [39] De Ketelaere B, Coucke P, De Baerdemaeker J. Eggshell crack detection based on acoustic resonance frequency analysis. J. Agric. Engng Res., 2000; 76: 157-163.
40. [40] Sun L, Bi X-k, Lin H, Zhao J-w, Cai J-r. On-line detection of eggshell crack based on acoustic resonance analysis. Journal of Food Engineering, 2013; 116: 240-245.