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

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

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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.

Item Type: Conference or Workshop Item (Lecture)
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Grading; K-nearest neighbours; Classification process; Principal component analysis
Subjects: T Technology > TS Manufactures
Faculty/Division: Faculty of Manufacturing Engineering
Depositing User: Mrs. Neng Sury Sulaiman
Date Deposited: 17 Jul 2018 02:23
Last Modified: 17 Jul 2018 02:23
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