A Machine Vision System for Detecting Fertile Eggs in the Incubation Industry. Issue 5 (2nd September 2016)

• Record Type: Journal Article
• Authors: Hashemzadeh, Mahdi; Farajzadeh, Nacer
• Is Part Of: International journal of computational intelligence systems. Volume 9:Issue 5(2016)
• Page Start: 850
• Page End: 862
• Publication Date: 2016-09-02
• Subjects: Machine Vision -- Image Processing -- Egg -- Fertility Detection -- Incubation Industry -- Auto-Candling; Computational intelligence -- Periodicals; 006.305
• DOI: 10.1080/18756891.2016.1237185
• Languages: English
• ISSNs: 1875-6891

Abstract

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.