Enhancing the destructive egg quality assessment using the machine vision and feature extraction technique
The rapid growth of the food industry necessitates rigorous quality control, particularly in egg production. This study explores advanced methodologies for egg quality assessment by integrating the Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and k-Nearest Neighbour (KNN)...
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| Format: | Article |
| Language: | English |
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Czech Academy of Agricultural Sciences
2025-06-01
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| Series: | Research in Agricultural Engineering |
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| Online Access: | https://rae.agriculturejournals.cz/artkey/rae-202502-0006_enhancing-the-destructive-egg-quality-assessment-using-the-machine-vision-and-feature-extraction-technique.php |
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| author | Ehsan Sheidaee Pourya Bazyar |
| author_facet | Ehsan Sheidaee Pourya Bazyar |
| author_sort | Ehsan Sheidaee |
| collection | DOAJ |
| description | The rapid growth of the food industry necessitates rigorous quality control, particularly in egg production. This study explores advanced methodologies for egg quality assessment by integrating the Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and k-Nearest Neighbour (KNN) with machine vision techniques. While traditional destructive methods like measuring the Haugh unit (HU) offer direct insights, but render eggs unusable, non-destructive techniques, such as imaging and spectroscopy, allow continuous quality monitoring. Over a 20-day period, egg samples were evaluated using a digital camera to capture key parameters like the albumen and yolk heights. The study's image processing involved noise reduction, feature extraction, and calibration. The PCA captured 90.18% of the data variability, while LDA achieved 100% classification accuracy, and KNN demonstrated 80% accuracy. These findings underscore the effectiveness of combining machine vision with statistical methods to enhance the egg grading accuracy, contributing to consumer safety and industry standards. |
| format | Article |
| id | doaj-art-7583d07baabe45188e0d97995f13a40f |
| institution | OA Journals |
| issn | 1212-9151 1805-9376 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Czech Academy of Agricultural Sciences |
| record_format | Article |
| series | Research in Agricultural Engineering |
| spelling | doaj-art-7583d07baabe45188e0d97995f13a40f2025-08-20T02:07:51ZengCzech Academy of Agricultural SciencesResearch in Agricultural Engineering1212-91511805-93762025-06-017129510410.17221/86/2024-RAErae-202502-0006Enhancing the destructive egg quality assessment using the machine vision and feature extraction techniqueEhsan Sheidaee0Pourya Bazyar1Biosystems Engineering Department, Tarbiat Modares University, Tehran, IranDepartment of Mechanical Engineering and Production Management, Hamburg University of Applied Science, Hamburg, GermanyThe rapid growth of the food industry necessitates rigorous quality control, particularly in egg production. This study explores advanced methodologies for egg quality assessment by integrating the Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and k-Nearest Neighbour (KNN) with machine vision techniques. While traditional destructive methods like measuring the Haugh unit (HU) offer direct insights, but render eggs unusable, non-destructive techniques, such as imaging and spectroscopy, allow continuous quality monitoring. Over a 20-day period, egg samples were evaluated using a digital camera to capture key parameters like the albumen and yolk heights. The study's image processing involved noise reduction, feature extraction, and calibration. The PCA captured 90.18% of the data variability, while LDA achieved 100% classification accuracy, and KNN demonstrated 80% accuracy. These findings underscore the effectiveness of combining machine vision with statistical methods to enhance the egg grading accuracy, contributing to consumer safety and industry standards.https://rae.agriculturejournals.cz/artkey/rae-202502-0006_enhancing-the-destructive-egg-quality-assessment-using-the-machine-vision-and-feature-extraction-technique.phphaugh unitimage processingclassificationquality controlalbumin height |
| spellingShingle | Ehsan Sheidaee Pourya Bazyar Enhancing the destructive egg quality assessment using the machine vision and feature extraction technique Research in Agricultural Engineering haugh unit image processing classification quality control albumin height |
| title | Enhancing the destructive egg quality assessment using the machine vision and feature extraction technique |
| title_full | Enhancing the destructive egg quality assessment using the machine vision and feature extraction technique |
| title_fullStr | Enhancing the destructive egg quality assessment using the machine vision and feature extraction technique |
| title_full_unstemmed | Enhancing the destructive egg quality assessment using the machine vision and feature extraction technique |
| title_short | Enhancing the destructive egg quality assessment using the machine vision and feature extraction technique |
| title_sort | enhancing the destructive egg quality assessment using the machine vision and feature extraction technique |
| topic | haugh unit image processing classification quality control albumin height |
| url | https://rae.agriculturejournals.cz/artkey/rae-202502-0006_enhancing-the-destructive-egg-quality-assessment-using-the-machine-vision-and-feature-extraction-technique.php |
| work_keys_str_mv | AT ehsansheidaee enhancingthedestructiveeggqualityassessmentusingthemachinevisionandfeatureextractiontechnique AT pouryabazyar enhancingthedestructiveeggqualityassessmentusingthemachinevisionandfeatureextractiontechnique |