Automated egg grading system using computer vision: Investigation on weight measure versus shape parameters

Chicken egg is a source of food of high demand by humans. Human operators cannot work perfectly and continuously when conducting egg grading. Instead of an egg grading system using weight measure, an automatic system for egg grading using computer vision (using egg shape parameter) can be used to im...

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Main Authors:	Ahmad Fakhri, Ab. Nasir, Siti Suhaila, Sabarudin, Anwar, P. P. Abdul Majeed, Ahmad Shahrizan, Abdul Ghani
Format:	Conference or Workshop Item
Language:	English
Published:	IOP Publishing Ltd 2018
Subjects:	TS Manufactures
Online Access:	http://umpir.ump.edu.my/id/eprint/21479/ http://umpir.ump.edu.my/id/eprint/21479/ http://umpir.ump.edu.my/id/eprint/21479/1/Automated%20egg%20grading%20system%20using%20computer%20vision.pdf

id	ump-21479
recordtype	eprints
spelling	ump-214792018-07-17T02:23:09Z http://umpir.ump.edu.my/id/eprint/21479/ Automated egg grading system using computer vision: Investigation on weight measure versus shape parameters Ahmad Fakhri, Ab. Nasir Siti Suhaila, Sabarudin Anwar, P. P. Abdul Majeed Ahmad Shahrizan, Abdul Ghani TS Manufactures Chicken egg is a source of food of high demand by humans. Human operators cannot work perfectly and continuously when conducting egg grading. Instead of an egg grading system using weight measure, an automatic system for egg grading using computer vision (using egg shape parameter) can be used to improve the productivity of egg grading. However, early hypothesis has indicated that more number of egg classes will change when using egg shape parameter compared with using weight measure. This paper presents the comparison of egg classification by the two above-mentioned methods. Firstly, 120 images of chicken eggs of various grades (A–D) produced in Malaysia are captured. Then, the egg images are processed using image pre-processing techniques, such as image cropping, smoothing and segmentation. Thereafter, eight egg shape features, including area, major axis length, minor axis length, volume, diameter and perimeter, are extracted. Lastly, feature selection (information gain ratio) and feature extraction (principal component analysis) are performed using k-nearest neighbour classifier in the classification process. Two methods, namely, supervised learning (using weight measure as graded by egg supplier) and unsupervised learning (using egg shape parameters as graded by ourselves), are conducted to execute the experiment. Clustering results reveal many changes in egg classes after performing shape-based grading. On average, the best recognition results using shape-based grading label is 94.16% while using weight-based label is 44.17%. As conclusion, automated egg grading system using computer vision is better by implementing shape-based features since it uses image meanwhile the weight parameter is more suitable by using weight grading system. IOP Publishing Ltd 2018-04 Conference or Workshop Item PeerReviewed pdf en cc_by http://umpir.ump.edu.my/id/eprint/21479/1/Automated%20egg%20grading%20system%20using%20computer%20vision.pdf Ahmad Fakhri, Ab. Nasir and Siti Suhaila, Sabarudin and Anwar, P. P. Abdul Majeed and Ahmad Shahrizan, Abdul Ghani (2018) Automated egg grading system using computer vision: Investigation on weight measure versus shape parameters. In: International Conference on Innovative Technology, Engineering and Sciences (iCITES 2018), 1-2 March 2018 , Universiti Malaysia Pahang, Pahang, Malaysia. pp. 1-9., 342 (1). ISSN 17578981 http://iopscience.iop.org/article/10.1088/1757-899X/342/1/012003/pdf
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TS Manufactures
spellingShingle	TS Manufactures Ahmad Fakhri, Ab. Nasir Siti Suhaila, Sabarudin Anwar, P. P. Abdul Majeed Ahmad Shahrizan, Abdul Ghani Automated egg grading system using computer vision: Investigation on weight measure versus shape parameters
description	Chicken egg is a source of food of high demand by humans. Human operators cannot work perfectly and continuously when conducting egg grading. Instead of an egg grading system using weight measure, an automatic system for egg grading using computer vision (using egg shape parameter) can be used to improve the productivity of egg grading. However, early hypothesis has indicated that more number of egg classes will change when using egg shape parameter compared with using weight measure. This paper presents the comparison of egg classification by the two above-mentioned methods. Firstly, 120 images of chicken eggs of various grades (A–D) produced in Malaysia are captured. Then, the egg images are processed using image pre-processing techniques, such as image cropping, smoothing and segmentation. Thereafter, eight egg shape features, including area, major axis length, minor axis length, volume, diameter and perimeter, are extracted. Lastly, feature selection (information gain ratio) and feature extraction (principal component analysis) are performed using k-nearest neighbour classifier in the classification process. Two methods, namely, supervised learning (using weight measure as graded by egg supplier) and unsupervised learning (using egg shape parameters as graded by ourselves), are conducted to execute the experiment. Clustering results reveal many changes in egg classes after performing shape-based grading. On average, the best recognition results using shape-based grading label is 94.16% while using weight-based label is 44.17%. As conclusion, automated egg grading system using computer vision is better by implementing shape-based features since it uses image meanwhile the weight parameter is more suitable by using weight grading system.
format	Conference or Workshop Item
author	Ahmad Fakhri, Ab. Nasir Siti Suhaila, Sabarudin Anwar, P. P. Abdul Majeed Ahmad Shahrizan, Abdul Ghani
author_facet	Ahmad Fakhri, Ab. Nasir Siti Suhaila, Sabarudin Anwar, P. P. Abdul Majeed Ahmad Shahrizan, Abdul Ghani
author_sort	Ahmad Fakhri, Ab. Nasir
title	Automated egg grading system using computer vision: Investigation on weight measure versus shape parameters
title_short	Automated egg grading system using computer vision: Investigation on weight measure versus shape parameters
title_full	Automated egg grading system using computer vision: Investigation on weight measure versus shape parameters
title_fullStr	Automated egg grading system using computer vision: Investigation on weight measure versus shape parameters
title_full_unstemmed	Automated egg grading system using computer vision: Investigation on weight measure versus shape parameters
title_sort	automated egg grading system using computer vision: investigation on weight measure versus shape parameters
publisher	IOP Publishing Ltd
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/21479/ http://umpir.ump.edu.my/id/eprint/21479/ http://umpir.ump.edu.my/id/eprint/21479/1/Automated%20egg%20grading%20system%20using%20computer%20vision.pdf
first_indexed	2023-09-18T22:31:32Z
last_indexed	2023-09-18T22:31:32Z
_version_	1777416318896570368

Automated egg grading system using computer vision: Investigation on weight measure versus shape parameters

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