Identification of chicken egg fertility using SVM classifier based on first-order statistical feature extraction


Shoffan Saifullah(1*); Andiko Putro Suryotomo(2);

(1) Universitas Pembangunan Nasional Veteran Yogyakarta
(2) Universitas Pembangunan Nasional Veteran Yogyakarta
(*) Corresponding Author

  

Abstract


This study aims to identify chicken eggs fertility using the support vector machine (SVM) classifier method. The classification basis used the first-order statistical (FOS) parameters as feature extraction in the identification process. This research was developed based on the processs identification process, which is still manual (conventional). Although currently there are many technologies in the identification process, they still need development. Thus, this research is one of the developments in the field of image processing technology. The sample data uses datasets from previous studies with a total of 100 egg images. The egg object in the image is a single object. From these data, the classification of each fertile and infertile egg is 50 image data. Chicken egg image data became input in image processing, with the initial process is segmentation. This initial segmentation aims to get the cropped image according to the object. The cropped image is repaired using image preprocessing with grayscaling and image enhancement methods. This method (image enhancement) used two combination methods: contrast limited adaptive histogram equalization (CLAHE) and histogram equalization (HE). The improved image becomes the input for feature extraction using the FOS method. The FOS uses five parameters, namely mean, entropy, variance, skewness, and kurtosis. The five parameters entered into the SVM classifier method to identify the fertility of chicken eggs. The results of these experiments, the method proposed in the identification process has a success percentage of 84.57%. Thus, the implementation of this method can be used as a reference for future research improvements. In addition, it may be possible to use a second-order feature extraction method to improve its accuracy and improve supervised learning for classification.

Keywords


Egg Fertility; Feature Extraction; Identification; Image Processing; Machine Learning

  
  

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doi  https://doi.org/10.33096/ilkom.v13i3.937.285-293 		  

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