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

Saifullah, Shoffan; Suryotomo, Andiko Putro

doi:10.29207/resti.v5i5.3431

Cited by 5 publications

(4 citation statements)

References 31 publications

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“…The final process is edge detection by applying four approaches: Roberts, Prewitt, Sobel, and Canny [33]. These operators are calculated based on gradient (7) with different kernels. Roberts operator using a 2x2 pixels kernel.…”

Section: Methodsmentioning

confidence: 99%

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Detection of Chicken Egg Embryos using BW Image Segmentation and Edge Detection Methods

Saifullah

Suryotomo

Yuhefizar

2021

RESTI

Self Cite

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This study aims to identify chicken egg embryos with the concept of image processing. This concept uses input and output in images. Thus the identification process, which was originally carried out using manual observation, was developed by computerization. Digital images are applied in identification by various image preprocessing, image segmentation, and edge detection methods. Based on these three methods, image processing has three processes: image grayscaling (convert to a grayscale image), image adjustment, and image enhancement. Image adjustment aims to clarify the image based on color correction. Meanwhile, image enhancement improves image quality, using histogram equalization (HE) and Contrast Limited Adaptive Histogram Equalization methods (CLAHE). Specifically for the image enhancement method, the CLAHE-HE combination is used for the improvement process. At the end of the process, the method used is edge detection. In this method, there is a comparison of various edge detection operators such as Roberts, Prewitt, Sobel, and canny. The results of edge detection using these four methods have the SSIM value respectively 0.9403; 0.9392; 0.9394; 0.9402. These results indicate that the SSIM values of the four operators have the same or nearly the same value. Thus, the edge detection method can provide good edge detection results and be implemented because the SSIM value is close to 1.00 (more than 0.93). Image segmentation detected object (egg and embryo), and the continued process by edge detection showed clearly edge of egg and embryo.

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Section: Methodsmentioning

confidence: 99%

“…This detection process uses image processing to determine the presence of embryos in eggs [6]. The identification process [7] in previous studies used machine learning methods [8], both supervised [9] and unsupervised learning [10]. The process starts by taking the image used as a dataset.…”

Section: Introductionmentioning

confidence: 99%

Detection of Chicken Egg Embryos using BW Image Segmentation and Edge Detection Methods

Saifullah

Suryotomo

Yuhefizar

2021

RESTI

Self Cite

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“…The segmentation process in this study uses the concept of clustering. The image is divided into three parts: background, eggs, and yolk eggs [29], [30]. This segmentation process uses the K-means algorithm with a value of 𝐾 = 3.…”

Section: K-means Segmentation Processmentioning

confidence: 99%

K-Means Segmentation Based-on Lab Color Space for Embryo Detection in Incubated Egg

Saifullah

Dreżewski

Khaliduzzaman

et al. 2022

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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The quality of the hatching process influences the success of the hatch rate besides the inherent egg factors. Eliminating infertile or dead eggs and monitoring embryonic growth are very important factors in efficient hatchery practices. This process aims to sort eggs that only have embryos to remain in the incubator until the end of the hatching process. This process aims to sort eggs with embryos to remain hatched until the end. Maximum checking is done the first week in the hatching period. This study aims to detect the presence of embryos in eggs. Detection of the existence of embryos is processed using segmentation. Egg images are segmented using the K-means algorithm based on Lab color images. The results of the image acquisition are converted into Lab color space images. The results of Lab color space images are processed using K-means for each color. The K-means process uses cluster k=3, where this cluster divides the image into three parts: background, eggs, and yolk. Egg yolks are part of eggs that have embryonic characteristics. This study applies the concept of color in the initial segmentation and grayscale in the final stages. The initial phase results show that the image segmentation results using k-means clustering based on Lab color space provide a grouping of three parts. At the grayscale image processing stage, the results of color image segmentation are processed with grayscaling, image enhancement, and morphology. Thus, it seems clear that the yolk segmented shows the presence of egg embryos. Based on this process and results, the initial stages of the embryo detection process used K-means segmentation based on Lab color space. The evaluation uses MSE and MSSIM, with values of 0.0486 and 0.9979; this can be used as a reference that the results obtained can detect embryos in egg yolk. This protocol could be used in a nondestructive quantitative study on embryos and their morphology in a precision poultry production system in the future.

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“…Identification utilizing image processing or computer vision is one example of technology deployment. [2]. The detection process uses candling technology to obtain the contents of images during the detection process to find the clear objects.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning based Automatic Image Annotation System for Image Retrieval with Arabic language

Al-Shammary,

Zaghden,

Bouhlel

2023

Preprint

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Social media platforms like YouTube, Twitter, and Facebook have grown into new modalities of communication, allowing a great number of individuals to interact and learn. Furthermore, many social media users currently produce and share incorrect thoughts and images that are not accompanied by words. The automated generation of any text for raw photos is a difficult operation, particularly when working with Arabic and a limited amount of training samples. To tackle this challenge, smart technology and deep learning technology have been offered. The method employs a bi-level architecture, which allows for the use of self-supervision to produce rotation variants in order to increase the number of training samples. This, in turn, enhances the variability of the model representation and enables the investigation of high-level object information for more detailed picture production. GoogleNet model automatically overcomes the stability issues associated with parsing and synthesising any picture. The proposed technique makes use of deep learning technology to explain images received from the internet environment and gather information about the existence of identified items in order to create an optimal detection method that does not interfere with the deep learning process.

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

Cited by 5 publications

References 31 publications

Detection of Chicken Egg Embryos using BW Image Segmentation and Edge Detection Methods

Detection of Chicken Egg Embryos using BW Image Segmentation and Edge Detection Methods

K-Means Segmentation Based-on Lab Color Space for Embryo Detection in Incubated Egg

Deep Learning based Automatic Image Annotation System for Image Retrieval with Arabic language

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