International Journal of Computational Intelligence Systems

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Volume 9, Issue 5, September 2016, Pages 850 - 862

A Machine Vision System for Detecting Fertile Eggs in the Incubation Industry

Authors
Mahdi Hashemzadeh*, hashemzadeh@azaruniv.edu, Nacer Farajzadehn.farajzadeh@azaruniv.edu
Faculty of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran
*Corresponding author.
Corresponding Author
Mahdi Hashemzadehhashemzadeh@azaruniv.edu
Received 19 August 2015, Accepted 8 May 2016, Available Online 1 September 2016.
DOI
10.1080/18756891.2016.1237185How to use a DOI?
Keywords
Machine Vision; Image Processing; Egg; Fertility Detection; Incubation Industry; Auto-Candling
Abstract

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.

Copyright
© 2016. the authors. Co-published by Atlantis Press and Taylor & Francis
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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<Previous Article In Issue
Next Article In Issue>
Journal
International Journal of Computational Intelligence Systems
Volume-Issue
9 - 5
Pages
850 - 862
Publication Date
2016/09/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.1080/18756891.2016.1237185How to use a DOI?
Copyright
© 2016. the authors. Co-published by Atlantis Press and Taylor & Francis
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Mahdi Hashemzadeh
AU  - Nacer Farajzadeh
PY  - 2016
DA  - 2016/09/01
TI  - A Machine Vision System for Detecting Fertile Eggs in the Incubation Industry
JO  - International Journal of Computational Intelligence Systems
SP  - 850
EP  - 862
VL  - 9
IS  - 5
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2016.1237185
DO  - 10.1080/18756891.2016.1237185
ID  - Hashemzadeh2016
ER  -