Chicken Egg Fertility Identification using FOS and BP-Neural Networks on 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 p...

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Autores principales:	Shoffan Saifullah, Andiko Putro Suryotomo
Formato:	article
Lenguaje:	ID
Publicado:	Ikatan Ahli Indormatika Indonesia 2021
Materias:	backpropagation feature extraction first order statistical classification image processing Systems engineering TA168 Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/a1110a208a6a4a8d86c910087b258284
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spelling	oai:doaj.org-article:a1110a208a6a4a8d86c910087b2582842021-11-16T13:16:11ZChicken Egg Fertility Identification using FOS and BP-Neural Networks on Image Processing2580-076010.29207/resti.v5i5.3431https://doaj.org/article/a1110a208a6a4a8d86c910087b2582842021-10-01T00:00:00Zhttp://jurnal.iaii.or.id/index.php/RESTI/article/view/3431https://doaj.org/toc/2580-0760This 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 SaifullahAndiko Putro SuryotomoIkatan Ahli Indormatika Indonesiaarticlebackpropagationfeature extractionfirst order statisticalclassificationimage processingSystems engineeringTA168Information technologyT58.5-58.64IDJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), Vol 5, Iss 5, Pp 919-926 (2021)
institution	DOAJ
collection	DOAJ
language	ID
topic	backpropagation feature extraction first order statistical classification image processing Systems engineering TA168 Information technology T58.5-58.64
spellingShingle	backpropagation feature extraction first order statistical classification image processing Systems engineering TA168 Information technology T58.5-58.64 Shoffan Saifullah Andiko Putro Suryotomo Chicken Egg Fertility Identification using FOS and BP-Neural Networks on Image Processing
description	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.
format	article
author	Shoffan Saifullah Andiko Putro Suryotomo
author_facet	Shoffan Saifullah Andiko Putro Suryotomo
author_sort	Shoffan Saifullah
title	Chicken Egg Fertility Identification using FOS and BP-Neural Networks on Image Processing
title_short	Chicken Egg Fertility Identification using FOS and BP-Neural Networks on Image Processing
title_full	Chicken Egg Fertility Identification using FOS and BP-Neural Networks on Image Processing
title_fullStr	Chicken Egg Fertility Identification using FOS and BP-Neural Networks on Image Processing
title_full_unstemmed	Chicken Egg Fertility Identification using FOS and BP-Neural Networks on Image Processing
title_sort	chicken egg fertility identification using fos and bp-neural networks on image processing
publisher	Ikatan Ahli Indormatika Indonesia
publishDate	2021
url	https://doaj.org/article/a1110a208a6a4a8d86c910087b258284
work_keys_str_mv	AT shoffansaifullah chickeneggfertilityidentificationusingfosandbpneuralnetworksonimageprocessing AT andikoputrosuryotomo chickeneggfertilityidentificationusingfosandbpneuralnetworksonimageprocessing
_version_	1718426495188205568

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

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