Publication: Yolk color measurement using image processing and deep learning
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Issued Date
2021-03-31
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ISSN
17551315
17551307
17551307
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2-s2.0-85104209274
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Mahidol University
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SCOPUS
Bibliographic Citation
IOP Conference Series: Earth and Environmental Science. Vol.686, No.1 (2021)
Suggested Citation
C. Kaewtapee, A. Supratak Yolk color measurement using image processing and deep learning. IOP Conference Series: Earth and Environmental Science. Vol.686, No.1 (2021). doi:10.1088/1755-1315/686/1/012054 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/76863
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Title
Yolk color measurement using image processing and deep learning
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Abstract
A high yellow yolk color of laying hens is required by customer. As yolk color measurement is determined by visual perception, color score may be expressed differently. The objective of this study was to develop the recognition of yolk color using red green blue (RGB) image and deep learning. The three hundred and fifty-three RGB images were obtained. The rectified linear unit (ReLU) and softmax were used as the activation function. An optimizer was configured with Adam, and categorical crossentropy was used as a loss function. The results showed that the loss had decreased to 0.45 and 0.63, whereas the accuracy had increased and reached 0.80 and 0.76 for training dataset and testing dataset, respectively. For evaluation, the loss value was 0.27 and 0.63, whereas the accuracy value was 0.90 and 0.76 for training dataset and testing dataset, respectively. The average f1-score was 0.76, whereas the highest precision (1.00) was observed in color score 5, 6 and 8. In conclusion, RGB image can be used as an alternative method to classify yolk color score with lower cost of analysis for egg producers in the near future.