Research Article
Open Access
Endüstriyel Üretim Tesislerinde Yumurtaların Görsel Analizi Ve Sınıflandırılması İçin Raspberry Pi Tabanlı Gerçek Zamanlı Bir Uygulama
1Konya Technical University
✉ Corresponding author: Murat Ozan · muratozan23@gmail.com
Proceedings 2nd International Symposium on Innovative Approaches in Scientific Studies
Volume 3 Pages 727–731 Year 2018
References
- Gıda, Tarım ve Hayvancılık Bakanlığından:TÜRK GIDA KODEKSİ YUMURTA TEBLİĞİ (2014/55)
- Elster RT. and Goodrum JW., 1991, Detection of cracks in eggs using machine vision, Transactions of the Asae, 35(4): 307-312p
- Elster RT. and Goodrum JW., 1992, Machine vision for crack detection in rotating eggs, Transactions of the Asae, 34(1): 1323-1328p
- Han, Y. J. and Y. Feng (1994). Egg shell inspection using global image analysis. American Society ofAgricuLturaL Engineers. 10(1), 109-114.
- Garcia-Alegre MC., Ribeiro A., Guinea D. and Cristobal G.., 1996, Eggshell defects detection based on color processing, Instituto de Optice, Spanish Council for Scientific Research, Madrid-Spain
- Dehrouyeh MH., Omid M., Ahmadi H., Mohtasebi SS. and Jamzad M., 2010, Grading and quality inspection of defected eggs using machine vision, International Journal of Advance Science and Technology, vol.16
- Patel VC., McClendon RW. and Goodrum JW.,1994, Crack detection in eggs using computer vision and neural networks, AI Applications, 8(2):21-31p
- Mertens K., De Ketelaere B. and Kamers B., 2005, Dirt detection on Brown eggs by means of color computer vision, Poultry Science, 1653-1659p
- (2018) The Raspberry Pi website. [Online]. Available: https://www.raspberrypi.org/help/what-%20is-a-raspberry-pi/ https://www.raspberrypi.org/products/raspberry-pi-3-model-b-plus/ https://www.raspberrypi.org/products/camera-module-v2/