Live embryo detection using a non-contact heart rate measurement of chicken eggs

Sutapun, Boonsong; Bakunda, John Ngunga; Pongchalee, Pornthep

doi:10.1117/12.2502727

Cited by 1 publication

(2 citation statements)

References 13 publications

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“…This result is obtained with the number of hidden layers being one and the neuron hidden layer being 12. Previous research shows that the implementation of hidden layers of more than 12 gets the same results, so it is enough with the maximum value [2], [9]. In addition, this feature has almost the same distribution of values, namely mean, variance, entropy, and kurtosis.…”

Section: Identification Of Chicken Egg Fertility and The Accuracy Resultsmentioning

confidence: 93%

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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: Identification Of Chicken Egg Fertility and The Accuracy Resultsmentioning

confidence: 93%

“…The five parameters can be calculated using ( 2)-( 6) in detail as follows: Mean (μ) is a measurement that shows the size of the image dispersion. This calculation uses calculations based on histogram and grayscale values (2).…”

Section: First Order Statistics (Fos) Feature Extractionmentioning

confidence: 99%

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

Saifullah

Suryotomo

2021

RESTI

View full text Add to dashboard Cite

show abstract

Live embryo detection using a non-contact heart rate measurement of chicken eggs

Cited by 1 publication

References 13 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

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