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

Year 2018, Volume: 2 Issue: 3, 82 - 87, 20.09.2018

Alphany Aragua Val İrvin Mabayo

https://doi.org/10.31015/jaefs.18013

Cited By: 2

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. 

Keywords

Egg weight , webcam , image processing , image analysis

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