Prediction Model for Chicken Egg Fertility Using Artificial Neural Network

Fadchar, Nemilyn A.; Cruz, Jennifer C. Dela

doi:10.1109/iciea49774.2020.9101966

Cited by 8 publications

(12 citation statements)

References 8 publications

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“…A multi-layer neural network with one hidden layer (unit Z) is shown in Figure 4. The output unit (Y) and the hidden unit have a bias (1). Wok denotes (bias) in the output unit Yk, and Voj denotes the bias in the hidden unit Zj.…”

Section: Classificationmentioning

confidence: 99%

“…Identifying chicken eggs fertility in hatching chicken eggs is exciting to increase chicken production results. The identification process is carried out using the observation method using light directed at the egg so that the contents of the egg can be seen (candling) [1]- [3]. This identification is still being made manually (done by humans) [1], [4], or there are already several tools from various studies [5], [6], both simulators and implementations [7]- [9].…”

Section: Introductionmentioning

confidence: 99%

“…The identification process is carried out using the observation method using light directed at the egg so that the contents of the egg can be seen (candling) [1]- [3]. This identification is still being made manually (done by humans) [1], [4], or there are already several tools from various studies [5], [6], both simulators and implementations [7]- [9]. Even though the tools already exist, there are still many researchers doing development to get better results.…”

Section: Introductionmentioning

confidence: 99%

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

Saifullah

Suryotomo

2021

RESTI

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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.

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Section: Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Saifullah

Suryotomo

2021

RESTI

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“…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%

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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Determination of Sugar Apple Ripeness via Image Processing Using Convolutional Neural Network

Sanchez¹,

Esteves²,

Linsangan³

2023

2023 15th International Conference on Computer and Automation Engineering (ICCAE)

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Prediction Model for Chicken Egg Fertility Using Artificial Neural Network

Cited by 8 publications

References 8 publications

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

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

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

Determination of Sugar Apple Ripeness via Image Processing Using Convolutional Neural Network

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