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Eggshell crack detection based on computer vision and acoustic response by means of back-propagation artificial neural network

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Abstract

An experimental system utilizing acoustic response system (ARS) and computer vision system (CVS) for eggshell crack detection was implemented. Firstly, the acoustic response signals were captured and analyzed, and six parameters (f 1, f 2, f 3, f 4: the dominant response frequency; CS: the mean value of coefficient of skewness; CE: the mean value of coefficient of excess) were analyzed. The ARS including a back-propagation artificial neural network model with a structure of 6 input nodes, 15 hidden nodes, and one output node was built to detect eggshell cracks. Secondly, the eggshell images were captured and processed by the computer vision system, and five geometrical characteristic parameters of crack and noise regions on the eggshell images were acquired. The CVS including a back-propagation artificial neural network model with a structure of 5 input nodes, 10 hidden nodes, and one output node was built to detect eggshell cracks. Finally, the quality of eggs, with or without cracks, was evaluated based on detection results from both CVS and ARS. This method allows the fusion of information obtained from CVS to ARS. The results showed that the detection accuracy of cracked eggs were 68 and 92%, respectively, by CVS and ARS. However, the accuracy equaled to 98% by the information infusion of two techniques. The result was superior to only one technique, and the method based on the information fusion of computer vision and acoustic response was applicable for detecting egg cracks. This research provides a new technology detection of cracked egg.

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References

  1. Patel VC, McClendon RW, Goodrum JW (1998) Color computer vision and artificial neural networks for the detection of defects in poultry eggs. Artif Intell Rev 12:163–176

    Article  Google Scholar 

  2. Elster RT, Goodrum JW (1991) Detection in eggs by machine vision. Trans ASAE 30:307–312

    Google Scholar 

  3. Goodmm JW, Elster RT (1992) Machine vision for crack detection in rotating eggs. Trans ASAE 35:1323–1328

    Google Scholar 

  4. Han YJ, Feng Y (1994) Eggshell inspection using global image analysis. Trans ASAE 10:109–114

    Google Scholar 

  5. Lawrence KC, Yoon SC, Heitschmidt GW, Jones DR, Park B (2008) Imaging system with modified-pressure chamber for crack detection in shell eggs. Sens Instrum Food Qual 2:116–122

    Article  Google Scholar 

  6. Pan LQ, Tu K, Su ZP (2007) Crack detection in eggs using computer vision and BP neural network. Trans CSAE 23:154–158

    Google Scholar 

  7. De Ketelaere B, Coucke P, De Baerdemaeker J (2000) Eggshell crack detection based on acoustic resonance frequency analysis. J Agric Eng Res 76:157–163

    Article  Google Scholar 

  8. Coucke P, De Ketelaere B, De Baerdemaeker J (2003) Experimental analysis of the dynamic mechanical behaviour of a chicken egg. J Sound Vib 266:711–721

    Article  Google Scholar 

  9. Bamelis FR, De Ketelaere B, Mertens K, Kemps BJ, Decuypere EM, De Baerdemaeker JG (2008) Measuring the conductance of eggshells using the acoustic resonance technique and optical transmission spectra. Comput Electron Agric 62:35–40

    Article  Google Scholar 

  10. Deng XY, Wang QH, Chen H, Xie H (2009) Eggshell crack detection using a wavelet-based support vector machine. Comput Electron Agric 70:135–143

    Article  Google Scholar 

  11. Lin H, Zhao JW, Chen QS, Cai JR, Zhou P (2009) Eggshell crack detection based on acoustic response and support vector data description algorithm. Eur Food Res Technol 230:95–100

    Article  CAS  Google Scholar 

  12. Lin H, Zhao JW, Chen QS, Cai JR, Zhou P (2009) Eggshell crack detection based on acoustic impulse response and supervised pattern recognition. Czech J Food Sci 27:393–402

    Google Scholar 

  13. Wang J, Jiang RS, Yu Y (2004) Relationship between dynamic resonance frequency and egg physical properties. Food Res Int 37:45–50

    Article  Google Scholar 

  14. Pan LQ, Tu K, Zhao L, Zhao YZ, Pan XJ (2005) Preliminary research of chicken egg crack detection based on acoustic resonance analysis. Trans CSAE 21:11–15

    Google Scholar 

  15. Steinmetz V, Crochon M, Bellon-Maurel V, Garcia Fernandez JL, Barreiro Elorza P, Verstreken L (1996) Sensors for fruit firmness assessment: comparison and fusion. J Agric Eng Res 64:15–28

    Article  Google Scholar 

  16. Steinmetz V, Roger JM, Moltó E, Blasco J (1999) On-line fusion of colour camera and spectrophotometer for sugar content prediction of apples. J Agric Eng Res 73:207–216

    Article  Google Scholar 

  17. Bleibaum RN, Stonea H, Tanb T, Labrecheb S, Saint-Martinb E, Iszb S (2002) Comparison of sensory and consumer results with electronic nose and tongue sensors for apple juices. Food Qual Pefer 13:409–422

    Article  Google Scholar 

  18. Buratti S, Benedetti S, Scampicchio M, Pangerodb EC (2004) Characterization and classification of Italian Barbera wines by using an electronic nose and an amperometric electronic tongue. Anal Chem Acta 525:133–139

    Article  CAS  Google Scholar 

  19. Cozzolinoa D, Smytha HE, Cynkara W, Dambergsa RG, Gishena M (2005) Usefulness of chemometrics and mass spectrometry-based electronic nose to classify Australian white wines by their varietal origin. Talanta 68:382–387

    Article  Google Scholar 

  20. Buratti S, Ballabio D, Benedetti S, Cosioa MS (2007) Prediction of Italian red wine sensorial descriptors from electronic nose, electronic tongue and spectrophotometric measurements by means of genetic algorithm regression models. Food Chem 100:211–218

    Article  CAS  Google Scholar 

  21. Sinellia N, Benedettia S, Bottegaa G, Rivaa M, Buratti S (2006) Evaluation of the optimal cooking time of rice by using FT-NIR spectroscopy and an electronic nose. J Cereal Sci 44:137–143

    Article  Google Scholar 

  22. Cosio MS, Ballabio D, Benedetti S, Gigliottib C (2007) Evaluation of different storage conditions of extra virgin olive oils with an innovative recognition tool built by means of electronic nose And electronic tongue. Food Chem l01:485–491

    Article  Google Scholar 

  23. Chen QS, Zhao JW, Cai JR, Saritpom V (2008) Inspection of tea quality by using multi-sensor information fusion based on NIR spectroscopy and machine vision. Trans CSAE 24:5–10

    CAS  Google Scholar 

  24. Liu P, Tu K, Pan LQ (2009) Duck egg’s shell intensity model based on fusion of computer vision and impact excitation. Trans CSAM 40:175–180

    Google Scholar 

  25. Xu D, Wu Z (2002) System analysis and design based on MATLAB 6.X—Neural Network. Xidian University Press, Xian, China, pp 186–190

    Google Scholar 

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Acknowledgments

This work was sponsored by National High Technology Research and Development Program of China (863 Program No. 2007AA10Z213), Agriculture Program of Jiangsu Province, China (Grant No. BE 2007320) and CAU-NAU Science Research Open Foundation for Young Teachers (Grant No. NC2008004).

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Authors and Affiliations

  1. College of Food Science and Technology, Nanjing Agricultural University, Nanjing, Jiangsu, 210095, People’s Republic of China

    Lei-qing Pan, Ge Zhan, Kang Tu & Peng Liu

  2. Neuroscience Research Australia, The University of New South Wales, Sydney, NSW, 2052, Australia

    Sicong Tu

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  1. Lei-qing Pan
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  2. Ge Zhan
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  3. Kang Tu
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  4. Sicong Tu
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  5. Peng Liu
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Correspondence to Kang Tu.

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Pan, Lq., Zhan, G., Tu, K. et al. Eggshell crack detection based on computer vision and acoustic response by means of back-propagation artificial neural network. Eur Food Res Technol 233, 457–463 (2011). https://doi.org/10.1007/s00217-011-1530-9

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  • DOI: https://doi.org/10.1007/s00217-011-1530-9

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