Chicken Egg Fertility Identification using FOS and BP-Neural Networks
on Image Processing

Chicken Egg Fertility Identification using FOS and BP-Neural Networks
on Image Processing

Backpropagation, Feature Extraction, First Order Statistical, Classification, Image Processing

This article aims to test FOS (first-order statistical) in extracting features of embryonated eggs. This test uses the initial step of
image processing to get the best input image in feature extraction. The image processing method starts from the image
acquisition process, then improves with image preprocessing and segmentation. Image acquisition in this study uses the concept
of egg candling in a dark place captured with a smartphone camera. The acquisition results are improved by image
preprocessing using gray scaling, image enhancement (by Histogram Equalization), and segmentation of chicken egg image.
The segmentation results were extracted using FOS with five parameters: mean, entropy, variance, skewness, and kurtosis.
Based on the calculation of these parameters, it is graphed and shows the difference in patterns between fertile and infertile
eggs. However, some eggs have a similar pattern, thus affecting the identification process. The identification process used
neural networks by the backpropagation method for training and testing. The training results provide an accuracy value of
100% of all training data; however, 80% of the new test data obtained test results at testing. This test is carried out with 100
data, 50 each for training and test data. Based on the test results, which significantly affect the level of accuracy is the feature
extraction method. FOS pattern in detecting the fertility of chicken eggs by BP Neural Network is still categorized as low, so
it is necessary to improve methods to get maximum results

Shoffan Saifullah1
, Andiko Putro Suryotomo2

Universitas Pembangunan Nasional Veteran Yogyakarta

25-10-2021

Fajar bagus W

PDF

Indonesia

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