DOI QR코드

DOI QR Code

계란 등급판정을 위한 파각란 자동 검사 시스템

Automatic Eggshell Crack Detection System for Egg Grading

  • Choi, Wan-Kyu (National Institute of Agricultural Engineering (NIAE), RDA) ;
  • Lee, Kang-Jin (National Institute of Agricultural Engineering (NIAE), RDA) ;
  • Son, Jae-Ryong (National Institute of Agricultural Engineering (NIAE), RDA) ;
  • Kang, Suk-Won (National Institute of Agricultural Engineering (NIAE), RDA) ;
  • Lee, Ho-Young (Department of Biosystems Engineering, Seoul National University)
  • 발행 : 2008.10.25

초록

Egg grading is determined by exterior and interior quality. Among the evaluation methods for the egg quality, a candling method is common to identify eggs with cracked shells and interior defects. But this method is time-consuming and laborious. In addition, practically, it is challenging to detect hairline and micro cracks. In this study, an on-line inspection system based on acoustic resonance frequency analysis was developed to detect hairline cracks on eggshells. A roller conveyor was used to transfer eggs along one lane to the impact position where each of eggs rotated by the roller was excited with an impact device at four different locations on the eggshell equator. The impact device was consisted of a plastic hammer and a rotary solenoid. The acoustic response of the egg to the impact was measured with a small condenser microphone at the same position as the impact device was installed. Two acoustic parameters, correlation coefficient for normalized power spectra and standard deviation of peak resonant frequencies, were used to detect cracked eggs. Intact eggs showed relatively high correlations among the four normalized power spectra and low standard deviations of the four peak resonant frequencies. On the other hand, cracked eggs showed low correlations and high standard deviations as compared to the intact. This method allowed a crack detection rate of 97.6%.

키워드

참고문헌

  1. Cho, H. K., W. K. Choi and J. H. Paek. 2000. Detection of surface cracks in shell eggs by acoustic impulse method. Transactions of the ASAE 43(6):1921-1926 https://doi.org/10.13031/2013.3097
  2. Elster, R. T. and J. W. Goodrum. 1991. Detection of cracks in eggs using machine vision. Transactions of the ASAE 34(1):307-312 https://doi.org/10.13031/2013.31663
  3. Jindal, V. K. and Eakasit Sritham. 2003. Detecting eggshell cracks by acoustic impulse response and artificial neural networks. 2003 ASAE Annual International Meeting. Paper Number:036170
  4. Ketelaere, B. De, P. Coucke and J. De Baerdemaeker. 2000. Eggshell crack detection based on acoustic resonance frequency analysis. J. of Agricultural Engineering Research 76:157-163 https://doi.org/10.1006/jaer.2000.0542
  5. Lawrence, K. C., S. C. Yoon, G. W. Heitschmidt, D. R. Jones and B. Park. 2008. Imaging system with modified-pressure chamber for crack detection in shell eggs. Sensing and Instrumentation for Food Quality and Safety 2(2). (in press)
  6. 이수환, 조한근, 최완규. 2000. 기계시각과 인공 신경망을 이용 한 파란의 판별. 한국농업기계학회지 25(5):409-414
  7. 조한근, 권양. 1995. 컴퓨터 시각을 이용한 계란 표면의 결함 검출. 한국농업기계학회지 20(4):368-375
  8. 최완규, 조한근. 2002. 계란의 음향진동 특성. 한국농업기계학 회지 27(4):293-300
  9. 축산물 등급판정소. 2007. 축산물 등급판정 세부기준

피인용 문헌

  1. Investigation of Reliability of Automatic Cracked and Bloody Egg Detector vol.20, pp.1, 2013, https://doi.org/10.11002/kjfp.2013.20.1.69