Vision-based Egg Grading System using Support Vector Machine

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Way Soong Lim
Kang Lai Desmond Ji
Sin Ting Lim
Boon Chin Yeo


Being known as a nutrient-dense food, eggs are high in demand in the marketplace and high-quality eggs are much sought-after. Hence, egg grading is in place to sort eggs into different grades. Experienced graders are required for their knowledge to classify egg grades and as humans are involved, errors when performing manual grading are unavoidable. This study aims to develop a vision-based egg classification system that requires minimal human intervention. The proposed system houses a camera to acquire real-time images of the eggs and these images are served as the input to the algorithm. Based on the 6 geometrical features derived from the geometric parameters of the egg image, the eggs are classified using Support Vector Machine (SVM). The experiment results show the proposed egg grading system with a linear kernel SVM model can yield as high as 92.59% training accuracy.


[Manuscript received: 1 July 2023 | Accepted: 10 October 2023 | Published: 30 April 2024]

Article Details

How to Cite
Lim, W. S., Desmond Ji, K. L., Lim, S. T., & Yeo, B. C. (2024). Vision-based Egg Grading System using Support Vector Machine. International Journal on Robotics, Automation and Sciences, 6(1), 13–19.


G. S. Khoo, “Egg suppliers sell assets to stay float”, The Malaysian Insights, 8 Sept 2022.

A. F. Ab Nasir, S. S. Sabarudin, A. P. A. Majeed and A. S. A. Ghani, "Automated egg grading system using computer vision: Investigation on weight measure versus shape parameters," in IOP Conference Series: Materials Science and Engineering, vol. 342, p. 1-9, 2018.

P. Chockalingam, “Smart manufacturing with smart technologies – a review”, International Journal on Robotics, Automation and Science, vol. 5(2), p. 85-88, 2023

“United States Standards, Grades, and Weight Class for Shell Eggs”, U.S. Department of Agriculture (USDA), 2000.

J. Alikhanov, A. Moldazhanov, A. Kulmakhambetova, Z. Shynybay, S. M. Penchev, T. D. Georgieva and P. I. Daskalov, "Express Methods and Procedures for Determination of the Main Egg Quality Indicators," TEM Journal: Technology, Education, Management, Informatics, vol. 10, p. 171–176, 2021.

R. Vargas, C. Ruiz and C. Navas, "Merging Manual and Automated Egg Candling: A Safety and Social Solution," vol. 9, pp. 70-76, June 2018.

S. Harnsoongnoen and N. Jaroensuk, "The grades and freshness assessment of eggs based on density detection using machine vision and weighing sensor," Scientific Reports, vol. 11, August 2021.

J. Chaki and N. Dey, A beginner's guide to image preprocessing techniques, CRC Press, 2018.

D. Indra, T. Hasanuddin, R. Satra and N. R. Wibowo, "Eggs Detection Using Otsu Thresholding Method," in 2018 2nd East Indonesia Conference on Computer and Information Technology (EIConCIT), 2018.

J. Thipakorn, R. Waranusast and P. Riyamongkol, "Egg weight prediction and egg size classification using image processing and machine learning," in 2017 14th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2017.

R. Waranusast, P. Intayod and D. Makhod, "Egg size classification on Android mobile devices using image processing and machine learning," in 2016 Fifth ICT International Student Project Conference (ICT-ISPC), 2016.

D. Bzdok, M. Krzywinski and N. Altman, "Machine learning: supervised methods," Nature Methods, vol. 15, p. 1-5, 2018.

A. Althnian, D. AlSaeed, H. Al-Baity, A. Samha, A. B. Dris, N. Alzakari, A.A. Elwafa and H. Kurdi, "Impact of dataset size on classification performance: An empirical evaluation in the medical domain", Applied Sciences, vol. 496(11), p.1-18, 2021.

G. Bradski and A. Kaehler, Learning OpenCV: Computer vision with the OpenCV library, " O'Reilly Media, Inc.", 2008.

J. So, S.Y. Joe, S.H. Hwang, S.J. Hong and S.H.Lee, "Current advances in detection of abnormal egg: a review", Journal of Animal Science and Technology, vol. 64(5), p. 813-829, 2022.

L. Shapiro and G. Stockman, "Computer Vision Prentice Hall," Inc., New Jersey, 2001.

A. M. Loorutu, H. Yazid, and K. S. A. Rahman, "Prostate cancer classification based on histopathological images", International Journal on Robotics, Automation and Science, vol. 5(2), p. 43-53, 2023.