A Machine Vision System for Detecting Fertile Eggs in the Incubation Industry
One of the important factors in increasing the productivity of the incubation industry is to be sure that the eggs placed in the incubators are fertile. In this research, a fertility detection machine vision system is developed and evaluated. To this end, a mechatronic machine is fabricated for acqu...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2016-09-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://www.atlantis-press.com/article/25868733/view |
| _version_ | 1811300975508455424 |
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| author | Mahdi Hashemzadeh Nacer Farajzadeh |
| author_facet | Mahdi Hashemzadeh Nacer Farajzadeh |
| author_sort | Mahdi Hashemzadeh |
| collection | DOAJ |
| description | One of the important factors in increasing the productivity of the incubation industry is to be sure that the eggs placed in the incubators are fertile. In this research, a fertility detection machine vision system is developed and evaluated. To this end, a mechatronic machine is fabricated for acquiring accurate digital images of eggs without harming them. An appropriate and cheap light source is also introduced for illuminating the eggs, which potentially enables a CCD camera to obtain good quality and informative images from inner side of the eggs. Finally, a robust machine vision algorithm is developed to process the captured images and distinguish fertile eggs from infertile ones. In order to evaluate the system, a large egg image dataset is provided using 240 incubated eggs (including 190 fertile and 50 infertile eggs). The fertility detection accuracy of the system on the provided dataset reaches 47.13% at day 1 of incubation, 81.41% at day 2, 93.08% at day 3, 97.73% at day 4, and 98.25% at day 5. Comparisons with existing approaches show that the proposed method achieves a superior performance. The obtained results indicate that the proposed system is highly reliable and applicable in the incubation industry. |
| first_indexed | 2024-04-13T07:00:30Z |
| format | Article |
| id | doaj.art-f98d7f1807aa4aa3b5b63dd3cdea1f78 |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-04-13T07:00:30Z |
| publishDate | 2016-09-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-f98d7f1807aa4aa3b5b63dd3cdea1f782022-12-22T02:57:08ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832016-09-019510.1080/18756891.2016.1237185A Machine Vision System for Detecting Fertile Eggs in the Incubation IndustryMahdi HashemzadehNacer FarajzadehOne of the important factors in increasing the productivity of the incubation industry is to be sure that the eggs placed in the incubators are fertile. In this research, a fertility detection machine vision system is developed and evaluated. To this end, a mechatronic machine is fabricated for acquiring accurate digital images of eggs without harming them. An appropriate and cheap light source is also introduced for illuminating the eggs, which potentially enables a CCD camera to obtain good quality and informative images from inner side of the eggs. Finally, a robust machine vision algorithm is developed to process the captured images and distinguish fertile eggs from infertile ones. In order to evaluate the system, a large egg image dataset is provided using 240 incubated eggs (including 190 fertile and 50 infertile eggs). The fertility detection accuracy of the system on the provided dataset reaches 47.13% at day 1 of incubation, 81.41% at day 2, 93.08% at day 3, 97.73% at day 4, and 98.25% at day 5. Comparisons with existing approaches show that the proposed method achieves a superior performance. The obtained results indicate that the proposed system is highly reliable and applicable in the incubation industry.https://www.atlantis-press.com/article/25868733/viewMachine VisionImage ProcessingEggFertility DetectionIncubation IndustryAuto-Candling |
| spellingShingle | Mahdi Hashemzadeh Nacer Farajzadeh A Machine Vision System for Detecting Fertile Eggs in the Incubation Industry International Journal of Computational Intelligence Systems Machine Vision Image Processing Egg Fertility Detection Incubation Industry Auto-Candling |
| title | A Machine Vision System for Detecting Fertile Eggs in the Incubation Industry |
| title_full | A Machine Vision System for Detecting Fertile Eggs in the Incubation Industry |
| title_fullStr | A Machine Vision System for Detecting Fertile Eggs in the Incubation Industry |
| title_full_unstemmed | A Machine Vision System for Detecting Fertile Eggs in the Incubation Industry |
| title_short | A Machine Vision System for Detecting Fertile Eggs in the Incubation Industry |
| title_sort | machine vision system for detecting fertile eggs in the incubation industry |
| topic | Machine Vision Image Processing Egg Fertility Detection Incubation Industry Auto-Candling |
| url | https://www.atlantis-press.com/article/25868733/view |
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