Eggs Fertilities Detection System on the Image of Kampung Chicken Egg Using Naive Bayes Classifier Algorithm

Aris Diantoro, Irwan Budi Santoso


Losses in chicken eggs hatchery make breeders income declined. The main cause of these things because it is less effective and efficient in distinguishing the state of fertilities in the eggs. The detection of fertile and infertile eggs will automatically provide ease of selection and removal of the eggs are fertile and infertile eggs. This will bring more profits for breeder as well as time efficiency more and selling power. Infertile eggs will give breeders the sale price if it is known as early as possible in order not to fail hatching. A method fuzzy c means and naive bayes classifier is designed to identify the state of the fertility of eggs. By putting eggs near the source light and black background in a dark room, then taked of image with a high qualities camera. From the resulting camera image, then extracted features or take characteristics that distinguish between fertile and infertile eggs. The total amount of data used in this study of 450 eggs image sourced from the field survey. Training data is used   250 data, 125 fertile eggs image data and 125 infertile eggs image data. As for testing the data using the 200 data, the image data 150 fertile eggs and 50 infertile eggs image data. Based on trial results of training data is obtained the best accuracy is equal to 80% at intervals of 5, 86.4% at intervals of 5 and dimensions 70x60, and 99.6% on 1x2 resize. The accuracy of the results obtained by 78%, 82% and 94% in trials testing data.


Egg, Fertil, infertile, Fuzzy C Means, Naive Bayes classifier.

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