Comparison of Egg Fertility Identification based on GLCM Feature Extraction using Backpropagation and K-means Clustering Algorithms

Saifullah, Shoffan; Permadi, Vynska Amalia

doi:10.1109/icsitech46713.2019.8987496

Cited by 19 publications

(25 citation statements)

References 24 publications

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“…The acquisition process is the initial process of creating a digital image [8]. This process is used to determine the required data and the method of taking digital images [13].…”

Section: Image Acquisition Processmentioning

confidence: 99%

“…The design has three factors, such as s a smartphone camera, an LED lamp for candling, and dark environment conditions [14] [15]. The results of image acquisition produce color images (RGB) [4,7,8,11] from eggs. The condition of an egg that has an embryo will be marked by images such as branches or roots.…”

Section: Figure 1 Process Of Egg Fertility Detectionmentioning

confidence: 99%

“…Feature extraction for egg fertility identification using the GLCM [8,11,12] shows that the image of egg fertility can be analyzed by feature extraction. The identification of egg fertility with GLCM and Backpropagation (BP) based on the results of grayscaling gives a system accuracy of 82.35% [12].…”

Section: Introductionmentioning

confidence: 99%

“…The identification of egg fertility with GLCM and Backpropagation (BP) based on the results of grayscaling gives a system accuracy of 82.35% [12]. The development of GLCM and BP [8] is an image enhancement process for preprocessing and image segmentation. The comparison with the Kmeans Clustering method in the process of identifying unsupervised learning.…”

Section: Introductionmentioning

confidence: 99%

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K-Means Clustering for Egg Embryo's Detection Based-on Statistical Feature Extraction Approach of Candling Eggs Image

Saifullah

2020

Sinergi

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This research discusses the detection of embryonic eggs using the k-means clustering method based on statistical feature extraction. The processes that occur in detection are image acquisition, image enhancement, feature extraction, and identification/detection. The data used consisted of 200 egg image data, consisting of 100 test data and 100 new test data. The acquisition process uses a smartphone camera by capturing candled egg objects. The results of image acquisition become a reference in the process of image enhancement and feature extraction using Statistical Feature Extraction. The statistical feature extraction applied is the Gray Level Co-occurrence Matrix (GLCM) method, which consists of 6 features, namely Energy, Contrast, Entropy, Variance, Correlation, and Homogeneity. The results of feature extraction (6 features) are grouped by the K-means Clustering method. The clustering process uses Euclidean distance calculations to determine the proximity of features. The results of grouping and testing give the best average results with an accuracy of ≈ 74% from several test samples.

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“…The acquisition process is the initial process of creating a digital image [8]. This process is used to determine the required data and the method of taking digital images [13].…”

Section: Image Acquisition Processmentioning

confidence: 99%

Section: Figure 1 Process Of Egg Fertility Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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K-Means Clustering for Egg Embryo's Detection Based-on Statistical Feature Extraction Approach of Candling Eggs Image

Saifullah

2020

Sinergi

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“…Selain penggunaan metode HSV, segmentasi dan buffer regresi linier dapat mengklasifikasikan jenis telur dan melakukan prediksi bobot dengan keakuratan 42% dari 36 sampel telur [5]. Selain itu, ekstraksi fitur statistik orde 1 dan orde 2, serta k-nearest neighbor (k- [14], proses segmentasi citra telur dengan watermarking [15], identifikasi telur dengan ekstraksi fitur GLCM [16] dan Backpropagation (BP) [17], dan identifikasi fertilitas telur dengan perbandingan BP dan K-means berdasarkan fitur GLCM [18].…”

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Analisis Perbandingan He Dan Clahe Pada Image Enhancement Dalam Proses Segmenasi Citra Untuk Deteksi Fertilitas Telur

Saifullah

2020

j. nas. pendidik. teknik. inform.

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Perkembangan teknologi di bidang peternakan mampu memberikan kemudahan dalam proses penetasan ayam. Namun, proses deteksi fertilitas telur telah diperiksa secara manual oleh pekerja yang menyortir telur yang fertil dan infertil. Penelitian ini bertujuan untuk mempermudah proses pendeteksian gambar fertilitas telur menggunakan sistem komputerisasi secara otomatis. Deteksi fertilitas telur dilakukan preprocessing dengan metode Image Enhancement. Dalam metode ini, metode Histogram Equalization (HE) dan metode Contrast Limited Adaptive Histogram Equalization (CLAHE) dibandingkan satu sama lain pada proses peningkatan citra (Image Enhancement). HE memberikan hasil yang dapat mengidentifikasi fertilitas telur. Namun, ada satu faktor penting dalam pemrosesan gambar, yaitu pengambilan telur (proses akuisisi). Proses deteksi fertilitas telur menggunakan segmentasi dengan metode morfologi. Proses pengujian yang dilakukan menggunakan metode kekauratan pada metode HE dan CLAHE yang masing-masing adalah sebesar 96% dan 79%. Hasil menunjukkan bahwa hasil HE lebih jelas terlihat dibandingkan dengan CLAHE.

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Imaging Technology in Egg and Poultry Research

Saifullah

Khaliduzzaman

2022

Informatics in Poultry Production

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Comparison of Egg Fertility Identification based on GLCM Feature Extraction using Backpropagation and K-means Clustering Algorithms

Cited by 19 publications

References 24 publications

K-Means Clustering for Egg Embryo's Detection Based-on Statistical Feature Extraction Approach of Candling Eggs Image

K-Means Clustering for Egg Embryo's Detection Based-on Statistical Feature Extraction Approach of Candling Eggs Image

Analisis Perbandingan He Dan Clahe Pada Image Enhancement Dalam Proses Segmenasi Citra Untuk Deteksi Fertilitas Telur

Imaging Technology in Egg and Poultry Research

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