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Processing the Acoustic Signal Received from the Eggshell Based on Wavelet Packet Transformation and Entropy and Detecting the Crack with Artificial Neural Networks

Year 2021, Volume: 8 Issue: 1, 125 - 135, 30.06.2021
https://doi.org/10.35193/bseufbd.847763

Abstract

Eggs are widely consumed in many products industry and homes as they are rich in vitamins and minerals. In order to meet increasing need quickly, automation has been made in chicken farms for processes, such as collecting eggs, weight classifying, separating cracked, and packing. If shell is cracked, harmful microorganisms can easily enter into it, and egg will deteriorate in a short time due to contact with air. Cracks can be large enough to be visible to naked eye, and sometimes they are micro-sized and cannot be detected by human eye. In this study, detection of cracked eggshell based on signal processing and machine learning was carried out. Acoustic signal generated as a result of impact made to shell by means of mechanical system was recorded for 0.2 seconds at a sampling frequency of 50kHz with microphone. Separately, 50 eggs data with intact and cracks shells were recorded with system and data set were created. Threshold value of 0.74V was used to determine time from moment of impact to egg shell to damping, and 680 data were taken after this value. The detail and approximation components with different frequencies were extracted by applying Wavelet Packet Transform (WPT) from 2nd level with db4 main wavelet. By calculating entropy value of each component, 1x4 feature vector was obtained. Artificial Neural Network (ANN) was used to determine efficiency of extracted feature vector in detecting crack egg shell. 100% performance was achieved and an egg's shell crack detection time was determined in approximately 0.216 seconds.

References

  • Sing, M. & Brar, J. (2016). Egg Safety in the Realm of Preharvest Food Safety. Microbiol. Spectr., 4 (4),1-14, doi: 10.1128/microbiolspec.PFS-0005-2014.
  • Mazzuco, H. & Bertechini, A. G. (2014). Critical points on egg production: causes, importance and incidence of eggshell breakage and defects. Ciência e Agrotecnologia, 38 (1), 7–14.
  • van Mourik, S. Alders, B. P. G. J. Helderman, F. van de Ven, L. J. F. & Groot Koerkamp, P. W. G. (2017). Predicting hairline fractures in eggs of mature hens. Poult. Sci., 96 (6), 1956–1962.
  • Rycroft, J. P. A. N. & Gregory, N. G. (2009). Hazards with cracked eggs and their relationship to egg shell strength. J. Sci. Food Agric., 89 (2), 201–205.
  • Öztürk, N. (2014). Görüntü işleme teknikleri ile beyaz yumurtalar üzerindeki yumurta kabuğu kusurlarının algılanması. (Y. Lisans Tezi), Karadeniz Teknik Üniversitesi, Fen Bilimleri Enstitüsü / Elektrik-Elektronik Mühendisliği ABD, Trabzon.
  • Abdullah, M. H. Nashat, S. Anwar, S. A. & Abdullah, M. Z. (2017). A framework for crack detection of fresh poultry eggs at visible radiation Comput. Electron. Agric., 141, 81–95.
  • Fang, W. & Youxian, W. (2011). Detecting preserved eggshell crack using machine vision. 2011 International Conference of Information Technology, 2011, Nanjing, 62–65.
  • Omid, M. Soltani, M. Dehrouyeh, M. H. Mohtasebi, S. S. & Ahmadi, H. (2013). An expert egg grading system based on machine vision and artificial intelligence techniques. J. Food Eng., 118 (1), 70–77, doi: 10.1016/J.JFOODENG.2013.03.019.
  • Wang, F. Zhang, S. & Tan, Z. (2017). Non-destructive crack detection of preserved eggs using a machine vision and multivariate analysis. Wuhan Univ. J. Nat. Sci., 22 (3), 257–262.
  • Wu, L. Wang, Q. Jie, D. Wang, S. Zhu, Z. & Xiong, L. (2018). Detection of crack eggs by image processing and soft-margin support vector machine. J. Comput. Methods Sci. Eng., 18 (1), 21–31.
  • Orlova, Y. Linker, R. & Spektor, B. (2012). Expansion of cracks in chicken eggs exposed to sub-atmospheric pressure. Biosyst. Eng., 112, (4), 278–284.
  • Lawrence, K. C. Yoon, S. C. Jones, D. R. Heitschmidt, G. W. Park, B. & Windham,W. R. (2009). Modified pressure system for imaging egg cracks. Trans. ASABE, 52 (3), 983–990.
  • Lawrence, K. C. Yoon, S. C. Heitschmidt, G. W. Jones, D. R. & Park, B. (2008). Imaging system with modified-pressure chamber for crack detection in shell eggs. Sens. Instrum. Food Qual. Saf., 2 (2), 116–122.
  • Li, Y. Dhakal, S. & Peng, Y. (2012). A machine vision system for identification of micro-crack in egg shell. J. Food Eng., 109 (1), 127–134, doi: 10.1016/J.JFOODENG.2011.09.024.
  • Priyadumkol, J. Kittichaikarn, C. & Thainimit, S. (2017). Crack detection on unwashed eggs using image processing. J. Food Eng., 209, 76–82.
  • Wang, S. C. Ren, Y. L. Chen, H. Xiong, L. R. & Wen, Y. X. (2004). Detection of cracked-shell eggs using acoustic signal and fuzzy recognition. Transations CSAE, 20 (4), 130–132.
  • Lin, H. Zhao, J. Chen, Q. Cai, J. & Zhou, P. (2009). Eggshell crack detection based on acoustic response and support vector data description algorithm. Eur. food Res. Technol., 230 (1), 95–100.
  • Deng, X. Wang, Q. Chen, H. & Xie, H. (2010). Eggshell crack detection using a wavelet-based support vector machine. Comput. Electron. Agric., 70 (1), 135–143, doi: 10.1016/J.COMPAG.2009.09.016.
  • Zhao,Y. Wang, J. Lu, Q. & Jiang, R. (2010). Pattern recognition of eggshell crack using PCA and LDA. Innov. Food Sci. Emerg. Technol., 11 (3), 520–525.
  • Ding, T. Lu, W. Zhang, C. Du, J. Ding, W. & Zhao, X. (2015). Eggshell crack identification based on Welch power spectrum and generalized regression neural network (GRNN). Food Sci, 36, 156–160.
  • Wang, H. Mao, J. Zhang, J. Jiang, H. & Wang, J. (2016). Acoustic feature extraction and optimization of crack detection for eggshell, J. Food Eng., 171, 240–247.
  • Strnková, J. & Nedomová, Š. (2013). Eggshell Crack Detection Using Dynamic Frequency Analysis. MENDELNET, 2013, Brno, 603-608.
  • Jin, C. Xie, L. & Ying, Y. (2015). Eggshell crack detection based on the time-domain acoustic signal of rolling eggs on a step-plate. J. Food Eng., 153, 53–62, doi: 10.1016/J.JFOODENG.2014.12.011.
  • Li, P. Wang, Q. Zhang, Q. Cao, S. Liu,Y. & Zhu, T. (2012). Non-destructive Detection on the Egg Crack Based on Wavelet Transform. IERI Procedia, 2, 372–382, doi: 10.1016/J.IERI.2012.06.104.
  • Sun, L. Feng, S. Chen,C. Liu, X. & Cai, J. (2020). Identification of eggshell crack for hen egg and duck egg using correlation analysis based on acoustic resonance method. J. Food Process Eng., 43 (8), 1-9.
  • Wikipedia. (2020). CompactRIO. https://en.wikipedia.org/wiki/CompactRIO, (Ekim 09, 2020).
  • Wikipedia. (2020). LabVIEW. https://tr.wikipedia.org/wiki/LabVIEW, (Ekim 09, 2020).
  • Xiong, S. Zhou, H. He, S. Zhang, L. Xia, Q. Xuan, J. & Shi, T. (2020).A novel end-to-end fault diagnosis approach for rolling bearings by ıntegrating wavelet packet transform into convolutional neural network structures. Sensors, 20, doi:10.3390/s20174965.
  • Chen, G. Li, Q. Li, D. Wu, Z. & Liu, Y. (2019). Main frequency band of blast vibration signal based on wavelet packet transform. Applied Mathematical Modelling, 74, 569-585.
  • Dodia, S. Edla, D. R. Bablani, A. Ramesh, D. & Kuppili, V. (2019). An efficient EEG based deceit identification test using wavelet packet transform and linear discriminant analysis. Journal of neuroscience methods, 314, 31–40.
  • Uyar, M. (2008). Güç kalitesindeki bozulma türlerinin akıllı örüntü tanıma yaklaşımları ile belirlenmesi. (Doktora Tezi), Fırat Üniversitesi, Fen Bilimleri Enstitüsü / Elektrik-Elektronik Mühendisliği ABD, Elazığ.

Yumurta Kabuğundan Alınan Akustik Sinyalin Dalgacık Paket Dönüşümü ve Entropiye Dayalı Olarak İşlenmesi ve Yapay Sinir Ağlarıyla Çatlağın Belirlenmesi

Year 2021, Volume: 8 Issue: 1, 125 - 135, 30.06.2021
https://doi.org/10.35193/bseufbd.847763

Abstract

Endüstride birçok üründe ve evlerimizde, vitaminler ve mineraller bakımından zengin olmasından dolayı yumurta yaygın olarak tüketilmektedir. Artan ihtiyacın hızlı bir şekilde karşılanması için tavuk çiftliklerinde yumurtaların toplanması, ağırlıklarına göre sınıflandırılması, sağlam/çatlak olanların ayrılması, paketlenmesi vb. işlemler için otomasyona geçiş yapılmıştır. Kabuğun çatlak olması durumunda içerisine zararlı mikroorganizmalar kolaylıkla girebileceği gibi yumurta içinin havayla temasından dolayı kısa sürede bozulmasına yol açacaktır. Çatlaklar gözle görülebilecek kadar büyük olabildiği gibi bazen de mikro boyutta olmakta insan gözüyle tespit edilememektedir. Bu çalışmada yumurta kabuğunun çatlak/sağlam olması durumunun sinyal işleme ve makine öğrenme tabanlı tespiti gerçekleştirilmiştir. Mekanik sistem vasıtasıyla kabuğa yapılan darbe neticesindeki oluşan akustik sinyal sistemdeki mikrofonla 50kHz örnekleme frekansında 0.2 sn süresince kayıt altına alınmaktadır. Kabuğu sağlam ve çatlak olan ayrı ayrı 50 yumurta verisi düzenekle kayıt altına alınıp veri seti oluşturulmuştur. Yumurta kabuğuna darbenin uygulanma anından sönümlenene kadarki zamanın tespiti için 0.74V eşik değeri kullanılıp bu değerden sonraki 680 veri alınmıştır. Bu verilere db4 ana dalgacığı ile 2. seviyeden Dalgacık Paket Dönüşümü (DPD) uygulanarak farklı frekanslı detay ve yaklaşım bileşenleri çıkartılmıştır. Her bir bileşenin entropi değeri hesaplanarak 1x4 boyutunda özellik vektörü elde edilmiştir. Çıkartımı yapılan özellik vektörünün yumurta kabuğundaki çatlağın tespitindeki etkinliğini belirlemek için Yapay Sinir Ağı (YSA) kullanılmıştır. %100 başarım elde edilmiş olup bir yumurtanın kabuk çatlak/sağlam belirleme süresi yaklaşık olarak 0.216sn’dir.

References

  • Sing, M. & Brar, J. (2016). Egg Safety in the Realm of Preharvest Food Safety. Microbiol. Spectr., 4 (4),1-14, doi: 10.1128/microbiolspec.PFS-0005-2014.
  • Mazzuco, H. & Bertechini, A. G. (2014). Critical points on egg production: causes, importance and incidence of eggshell breakage and defects. Ciência e Agrotecnologia, 38 (1), 7–14.
  • van Mourik, S. Alders, B. P. G. J. Helderman, F. van de Ven, L. J. F. & Groot Koerkamp, P. W. G. (2017). Predicting hairline fractures in eggs of mature hens. Poult. Sci., 96 (6), 1956–1962.
  • Rycroft, J. P. A. N. & Gregory, N. G. (2009). Hazards with cracked eggs and their relationship to egg shell strength. J. Sci. Food Agric., 89 (2), 201–205.
  • Öztürk, N. (2014). Görüntü işleme teknikleri ile beyaz yumurtalar üzerindeki yumurta kabuğu kusurlarının algılanması. (Y. Lisans Tezi), Karadeniz Teknik Üniversitesi, Fen Bilimleri Enstitüsü / Elektrik-Elektronik Mühendisliği ABD, Trabzon.
  • Abdullah, M. H. Nashat, S. Anwar, S. A. & Abdullah, M. Z. (2017). A framework for crack detection of fresh poultry eggs at visible radiation Comput. Electron. Agric., 141, 81–95.
  • Fang, W. & Youxian, W. (2011). Detecting preserved eggshell crack using machine vision. 2011 International Conference of Information Technology, 2011, Nanjing, 62–65.
  • Omid, M. Soltani, M. Dehrouyeh, M. H. Mohtasebi, S. S. & Ahmadi, H. (2013). An expert egg grading system based on machine vision and artificial intelligence techniques. J. Food Eng., 118 (1), 70–77, doi: 10.1016/J.JFOODENG.2013.03.019.
  • Wang, F. Zhang, S. & Tan, Z. (2017). Non-destructive crack detection of preserved eggs using a machine vision and multivariate analysis. Wuhan Univ. J. Nat. Sci., 22 (3), 257–262.
  • Wu, L. Wang, Q. Jie, D. Wang, S. Zhu, Z. & Xiong, L. (2018). Detection of crack eggs by image processing and soft-margin support vector machine. J. Comput. Methods Sci. Eng., 18 (1), 21–31.
  • Orlova, Y. Linker, R. & Spektor, B. (2012). Expansion of cracks in chicken eggs exposed to sub-atmospheric pressure. Biosyst. Eng., 112, (4), 278–284.
  • Lawrence, K. C. Yoon, S. C. Jones, D. R. Heitschmidt, G. W. Park, B. & Windham,W. R. (2009). Modified pressure system for imaging egg cracks. Trans. ASABE, 52 (3), 983–990.
  • Lawrence, K. C. Yoon, S. C. Heitschmidt, G. W. Jones, D. R. & Park, B. (2008). Imaging system with modified-pressure chamber for crack detection in shell eggs. Sens. Instrum. Food Qual. Saf., 2 (2), 116–122.
  • Li, Y. Dhakal, S. & Peng, Y. (2012). A machine vision system for identification of micro-crack in egg shell. J. Food Eng., 109 (1), 127–134, doi: 10.1016/J.JFOODENG.2011.09.024.
  • Priyadumkol, J. Kittichaikarn, C. & Thainimit, S. (2017). Crack detection on unwashed eggs using image processing. J. Food Eng., 209, 76–82.
  • Wang, S. C. Ren, Y. L. Chen, H. Xiong, L. R. & Wen, Y. X. (2004). Detection of cracked-shell eggs using acoustic signal and fuzzy recognition. Transations CSAE, 20 (4), 130–132.
  • Lin, H. Zhao, J. Chen, Q. Cai, J. & Zhou, P. (2009). Eggshell crack detection based on acoustic response and support vector data description algorithm. Eur. food Res. Technol., 230 (1), 95–100.
  • Deng, X. Wang, Q. Chen, H. & Xie, H. (2010). Eggshell crack detection using a wavelet-based support vector machine. Comput. Electron. Agric., 70 (1), 135–143, doi: 10.1016/J.COMPAG.2009.09.016.
  • Zhao,Y. Wang, J. Lu, Q. & Jiang, R. (2010). Pattern recognition of eggshell crack using PCA and LDA. Innov. Food Sci. Emerg. Technol., 11 (3), 520–525.
  • Ding, T. Lu, W. Zhang, C. Du, J. Ding, W. & Zhao, X. (2015). Eggshell crack identification based on Welch power spectrum and generalized regression neural network (GRNN). Food Sci, 36, 156–160.
  • Wang, H. Mao, J. Zhang, J. Jiang, H. & Wang, J. (2016). Acoustic feature extraction and optimization of crack detection for eggshell, J. Food Eng., 171, 240–247.
  • Strnková, J. & Nedomová, Š. (2013). Eggshell Crack Detection Using Dynamic Frequency Analysis. MENDELNET, 2013, Brno, 603-608.
  • Jin, C. Xie, L. & Ying, Y. (2015). Eggshell crack detection based on the time-domain acoustic signal of rolling eggs on a step-plate. J. Food Eng., 153, 53–62, doi: 10.1016/J.JFOODENG.2014.12.011.
  • Li, P. Wang, Q. Zhang, Q. Cao, S. Liu,Y. & Zhu, T. (2012). Non-destructive Detection on the Egg Crack Based on Wavelet Transform. IERI Procedia, 2, 372–382, doi: 10.1016/J.IERI.2012.06.104.
  • Sun, L. Feng, S. Chen,C. Liu, X. & Cai, J. (2020). Identification of eggshell crack for hen egg and duck egg using correlation analysis based on acoustic resonance method. J. Food Process Eng., 43 (8), 1-9.
  • Wikipedia. (2020). CompactRIO. https://en.wikipedia.org/wiki/CompactRIO, (Ekim 09, 2020).
  • Wikipedia. (2020). LabVIEW. https://tr.wikipedia.org/wiki/LabVIEW, (Ekim 09, 2020).
  • Xiong, S. Zhou, H. He, S. Zhang, L. Xia, Q. Xuan, J. & Shi, T. (2020).A novel end-to-end fault diagnosis approach for rolling bearings by ıntegrating wavelet packet transform into convolutional neural network structures. Sensors, 20, doi:10.3390/s20174965.
  • Chen, G. Li, Q. Li, D. Wu, Z. & Liu, Y. (2019). Main frequency band of blast vibration signal based on wavelet packet transform. Applied Mathematical Modelling, 74, 569-585.
  • Dodia, S. Edla, D. R. Bablani, A. Ramesh, D. & Kuppili, V. (2019). An efficient EEG based deceit identification test using wavelet packet transform and linear discriminant analysis. Journal of neuroscience methods, 314, 31–40.
  • Uyar, M. (2008). Güç kalitesindeki bozulma türlerinin akıllı örüntü tanıma yaklaşımları ile belirlenmesi. (Doktora Tezi), Fırat Üniversitesi, Fen Bilimleri Enstitüsü / Elektrik-Elektronik Mühendisliği ABD, Elazığ.
There are 31 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Zekeriya Balcı 0000-0002-1389-1784

Mehmet Yumurtacı 0000-0001-8528-9672

İsmail Yabanova 0000-0001-8075-3579

Semih Ergin 0000-0002-7470-8488

Publication Date June 30, 2021
Submission Date December 27, 2020
Acceptance Date February 22, 2021
Published in Issue Year 2021 Volume: 8 Issue: 1

Cite

APA Balcı, Z., Yumurtacı, M., Yabanova, İ., Ergin, S. (2021). Yumurta Kabuğundan Alınan Akustik Sinyalin Dalgacık Paket Dönüşümü ve Entropiye Dayalı Olarak İşlenmesi ve Yapay Sinir Ağlarıyla Çatlağın Belirlenmesi. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, 8(1), 125-135. https://doi.org/10.35193/bseufbd.847763