Research Article
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Year 2018, , 82 - 87, 20.09.2018
https://doi.org/10.31015/jaefs.18013

Abstract

References

  • Abdanan Mehdizadeh, S., Minaei, S., Hancock, N.H., Karimi Torshizi, M.A. (2014). An intelligent system for egg quality classification based on visible-infrared transmittance spectroscopy. Inf. Process. Agric. 1, 105–114. https://doi.org/10.1016/j.inpa.2014.10.002
  • Al-amri, S.S., Kalyankar, N. V., Khamitkar, S.D. (2010). Image segmentation by using edge detection. Int. J. Comput. Sci. Eng. 2, 804–807.
  • Altuntas, E., Sekeroglu, A. (2010). Mechanical behaviors and physical properties of chicken egg as affected by different egg weights. J. Food Process Eng. 9–11. https://doi.org/10.1111/j.1745
  • Asadi, V., Raoufat, M.H., (2010). Egg Weight Estimation by Machine Vision and Neural Network Techniques (A case study Fresh Egg). Int. J. Nat. Eng. Sci. 4, 1–4.
  • Broyde, R.M. (2000). Blood Spots in Eggs. J. Halacha Contemp. Soc., 40, 47-58.
  • Chmelař, P., Dobrovolný, M. (2012). The Optical Measuring Device for the Autonomous Exploration and Mapping of Unknown Environments 7, 41–50.
  • CountrySTAT-Philippines, (2014). Poultry and Eggs: Volume of Production by Region [WWW Document]. URL http://countrystat.bas.gov.ph/?cont=10&pageid=1&ma=B40PNVLP (accessed 6.16.16).
  • Dangphonthong, D., Pinate, W. (2016). Analysis of weight egg using image processing, in: Proceedings of Academics World 17th International Conference. Tokyo, Japan, pp. 55–57.
  • National Instruments Corporation. Spatial Calibration [WWW Document]. URL http://zone.ni.com/reference/en-XX/help/372916J-01/nivisionconcepts/spatial_calibration/ (accessed 6.21.16).
  • Paganelli, C.V., Olszowka, A., Ar, A. (1974). The Avian Egg: Surface Area, Volume, and Density. Condor 76, 319–325.
  • Soltani, M., Omid, M., Alimardani, R. (2015). Egg volume prediction using machine vision technique based on pappus theorem and artificial neural network. J. Food Sci. Technol. 52, 3065–3071. https://doi.org/10.1007/s13197-014-1350-6.
  • Waranusast, R., Intayod, P., Makhod, D. (2016). Egg size classification on Android mobile devices using image processing and machine learning. 2016 Fifth ICT Int. Student Proj. Conf. 170–173. https://doi.org/10.1109/ICT-ISPC.2016.7519263

A cost-effective approach for chicken egg weight estimation through computer vision

Year 2018, , 82 - 87, 20.09.2018
https://doi.org/10.31015/jaefs.18013

Abstract

Egg weighing and classification are among the most significant phases
done in egg processing by industries which are tedious if done manually by
poultry owners, and egg inspectors and graders. 
This study presented an alternative way of estimating chicken egg weight
through computer vision minimizing human interaction during the process. In
this study, fifteen eggs of white leghorn chicken layers of different sizes
were tested. The eggs’ image was captured using an inexpensive yet reliable
webcam which was then loaded onto the MatLab workspace for image processing and
further image analysis. The center of gravity of the image was determined, and
the extraction of minor axis length and major axis length followed. The
obtained values were used to compute the egg’s weight mathematically. Through
the different image processing methods, image dimensions were extracted and
used to calculate the desired output. The results of this study showed 96.31%
accuracy in estimating the egg’s weight and classification validated by manual
egg weighing and classification procedure. 

References

  • Abdanan Mehdizadeh, S., Minaei, S., Hancock, N.H., Karimi Torshizi, M.A. (2014). An intelligent system for egg quality classification based on visible-infrared transmittance spectroscopy. Inf. Process. Agric. 1, 105–114. https://doi.org/10.1016/j.inpa.2014.10.002
  • Al-amri, S.S., Kalyankar, N. V., Khamitkar, S.D. (2010). Image segmentation by using edge detection. Int. J. Comput. Sci. Eng. 2, 804–807.
  • Altuntas, E., Sekeroglu, A. (2010). Mechanical behaviors and physical properties of chicken egg as affected by different egg weights. J. Food Process Eng. 9–11. https://doi.org/10.1111/j.1745
  • Asadi, V., Raoufat, M.H., (2010). Egg Weight Estimation by Machine Vision and Neural Network Techniques (A case study Fresh Egg). Int. J. Nat. Eng. Sci. 4, 1–4.
  • Broyde, R.M. (2000). Blood Spots in Eggs. J. Halacha Contemp. Soc., 40, 47-58.
  • Chmelař, P., Dobrovolný, M. (2012). The Optical Measuring Device for the Autonomous Exploration and Mapping of Unknown Environments 7, 41–50.
  • CountrySTAT-Philippines, (2014). Poultry and Eggs: Volume of Production by Region [WWW Document]. URL http://countrystat.bas.gov.ph/?cont=10&pageid=1&ma=B40PNVLP (accessed 6.16.16).
  • Dangphonthong, D., Pinate, W. (2016). Analysis of weight egg using image processing, in: Proceedings of Academics World 17th International Conference. Tokyo, Japan, pp. 55–57.
  • National Instruments Corporation. Spatial Calibration [WWW Document]. URL http://zone.ni.com/reference/en-XX/help/372916J-01/nivisionconcepts/spatial_calibration/ (accessed 6.21.16).
  • Paganelli, C.V., Olszowka, A., Ar, A. (1974). The Avian Egg: Surface Area, Volume, and Density. Condor 76, 319–325.
  • Soltani, M., Omid, M., Alimardani, R. (2015). Egg volume prediction using machine vision technique based on pappus theorem and artificial neural network. J. Food Sci. Technol. 52, 3065–3071. https://doi.org/10.1007/s13197-014-1350-6.
  • Waranusast, R., Intayod, P., Makhod, D. (2016). Egg size classification on Android mobile devices using image processing and machine learning. 2016 Fifth ICT Int. Student Proj. Conf. 170–173. https://doi.org/10.1109/ICT-ISPC.2016.7519263
There are 12 citations in total.

Details

Primary Language English
Subjects Food Engineering
Journal Section Research Articles
Authors

Alphany Aragua This is me 0000-0003-3239-5345

Val İrvin Mabayo 0000-0003-2231-5604

Publication Date September 20, 2018
Submission Date April 1, 2018
Acceptance Date May 11, 2018
Published in Issue Year 2018

Cite

APA Aragua, A., & Mabayo, V. İ. (2018). A cost-effective approach for chicken egg weight estimation through computer vision. International Journal of Agriculture Environment and Food Sciences, 2(3), 82-87. https://doi.org/10.31015/jaefs.18013

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