Chicken Egg Detection Based-on Image Processing Concept: A Review

Authors

  • Shoffan Saifullah Department of Informatics, Universitas Pembangunan Nasional Veteran Yogyakarta
  • Rabbimov Ilyos Mehriddinovich Faculty of Digital Technologies, Samarkand State University
  • Lean Karlo Tolentino Department of Electronics Engineering, Technological University of the Philippines

DOI:

https://doi.org/10.31315/cip.v1i1.6129

Abstract

The concept of image processing has been implemented and developed in various fields, including the poultry industry. The focus in development is on egg detection. Detection is not only in the concept of object detection but also in other things such as weight prediction, egg physical characteristics, to embryo detection. This staged process starts from the image acquisition process, preprocessing, segmentation up to identifying or detecting eggs. This article provides details about the concept of image processing in detecting chicken eggs based on a review of previous studies. The studies discussed the basic concepts of image processing in detecting chicken eggs and their technical application. Based on image processing’s basic concept, there are four main parts: image acquisition, preprocessing, segmentation, and identification or classification. The acquisition process is carried out with a variety of tools that can capture images to be processed. The result of the acquisition is preprocessed by one or more methods that can improve image quality. After that, the image segmentation process is used to determine the object to be detected. Image segmentation can be used as a reference for objects processed by feature extraction. The feature extraction aims to provide certain fertile (embryonic) characteristics and unfertile (non-embryonic) egg images. The identification process is precise which objects are detected and not. The concept of segmentation and identification/classification can be implemented in computer-based applied applications. Besides, these methods are still developing and improving their accuracy and implementation in the poultry industry.

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