8 February 2017 Omega-3 chicken egg detection system using a mobile-based image processing segmentation method
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Proceedings Volume 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016); 1022516 (2017) https://doi.org/10.1117/12.2266088
Event: Eighth International Conference on Graphic and Image Processing, 2016, Tokyo, Japan
Abstract
An Omega-3 chicken egg is a chicken egg produced through food engineering technology. It is produced by hen fed with high omega-3 fatty acids. So, it has fifteen times nutrient content of omega-3 higher than Leghorn’s. Visually, its shell has the same shape and colour as Leghorn’s. Each egg can be distinguished by breaking the egg’s shell and testing the egg yolk’s nutrient content in a laboratory. But, those methods were proven not effective and efficient. Observing this problem, the purpose of this research is to make an application to detect the type of omega-3 chicken egg by using a mobile-based computer vision. This application was built in OpenCV computer vision library to support Android Operating System. This experiment required some chicken egg images taken using an egg candling box. We used 60 omega-3 chicken and Leghorn eggs as samples. Then, using an Android smartphone, image acquisition of the egg was obtained. After that, we applied several steps using image processing methods such as Grab Cut, convert RGB image to eight bit grayscale, median filter, P-Tile segmentation, and morphology technique in this research. The next steps were feature extraction which was used to extract feature values via mean, variance, skewness, and kurtosis from each image. Finally, using digital image measurement, some chicken egg images were classified. The result showed that omega-3 chicken egg and Leghorn egg had different values. This system is able to provide accurate reading around of 91%.
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Oky Dwi Nurhayati, Kurniawan Teguh M., Cintya Amalia P., "Omega-3 chicken egg detection system using a mobile-based image processing segmentation method", Proc. SPIE 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016), 1022516 (8 February 2017); doi: 10.1117/12.2266088; https://doi.org/10.1117/12.2266088
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