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An indirect approach for egg weight sorting using image processing

Alikhanov, Jakhfer, Penchev, StanislavM., Georgieva, TsvetelinaD., Moldazhanov, Aidar, Shynybay, Zhandos, Daskalov, PlamenI.
Journal of food measurement & characterization 2018 v.12 no.1 pp. 87-93
correlation, egg weight, eggs, geometry, image analysis, mathematical models, regression analysis
An indirect approach for egg weight sorting, using image processing, is proposed in the paper. The eggs are sorted in four classes by weight. Regression analysis is used for approximation of relationship between egg weight and egg geometric parameters—perimeter, area, major and minor axis, shape index and shape factor. The values of the geometric parameters, collected by image processing and the one, collected by traditional method, are compared for each egg sample, using percent differences between data. The experimental results show that the most significant parameter for egg weight indirect measurement is the egg area, with correlation coefficient 0.989. The mathematical model for the relationship between weight and area of the egg is defined with coefficient of determination 0.978. The classification accuracy is achieved within the eggs test sample sorting. The total classification error is 2.5% for test set and 12.5% for training set.