Classification of Egg Fertility on the Image of Kampong Chicken Egg Using the Frequency Distribution Feature and Naive Bayes Classifier Algorithm’s - Diantoro, Aris; Santoso, Irwan Budi (Proceedings of the International Conference on Green Technology, 2017)
Losses in hatching chicken eggs make breeders income decreased. The main cause of this because it is less effective and efficient in distinguishing the fertilities state in eggs. Detection of fertile and infertile eggs will automatically facilitate the selection and removal of fertile eggs and infertile eggs. This will bring more benefits to farms such as time efficiency and more selling power. Infertile eggs will provide the selling price if known as early as possible so as not to fail hatched. A Fuzzy C Means method and the Naive Bayes Classifier are designed to identify the fertility state of the egg. By putting eggs near a light source as well as a black background in dark spaces, then take the image with a high quality camera. From the resulting image the camera extracted features or distinctive features that distinguish between fertile and infertile eggs. The total amount of data used in this study is 350 eggs from the field survey. Training data used 250 data, 125 fertile egg image data and 125 infertile egg image data. While for data testing using 100 data, 50 data fertilized egg image and 50 data image infertile egg. Based on the results of the training data trials obtained the best accuracy is 93.2% at interval 4 with RGB feature, 50% at interval 3 on Grayscale feature. Accuracy results in the data testing test obtained by 87% on RGB features, 35% on Grayscale features.