Use Of Deep Learning To Determine The Freshness Of Egg

Öz

The freshness of the egg is important for both hatching and human consumption. It is quite difficult to determine the freshness of the egg without damaging it with classical methods. Deep learning is a powerful method used to classify data without processing or with much less processing. In this study, 30 eggs were photographed as experimental material for 29 days and the images obtained were used as data. It is aimed to determine how many days old the eggs are, which are foldered according to the days of the photos obtained. As a result of the study, 91.78% valuation accuracy value was obtained. Obtaining inputs without preprocessing shows that the Deep learning method can be used when a fast decision is required and the machine needs to make its own decision.

Anahtar Kelimeler

• Egg
• Freshness
• Deep learning
• Convolutional neural networks
• Non-destuructive

Kaynakça

1. Abdel-Nour, N., Ngadi, M., Prasher, S. and Karimi Y. (2011). Prediction of egg freshness and albumen quality using visible/near infrared spectroscopy. Foof Bioprocess Technol. 4:731-736.
2. Aboonajmi, M., & Najafabadi, T. A. (2014). Prediction of Poultry Egg Freshness Using Vis-Nir Spectroscopy with Maximum Likelihood Method. International Journal of Food Properties, 17(10), 2166-2176.
3. Aboonajmi, M., Saberi, A., Abbasian Najafabadi, T., & Kondo N. (2016). Quality assessment of poultry egg based on visible–near infrared spectroscopy and radial basis function networks. International Journal of Food Properties. 19 (2016), pp. 1163-1172, 10.1080/10942912.2015.1075215.
4. Anonymous (2016). United states department of agriculture. Shell eggs from farm to table. (Accessed date: 1.1.2017)https://www.google.co.kr/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0ahukewioxro93klrahvmfjqkhblvcgaqfgglmae&url=https%3a%2f%2fwww.fsis.usda.gov%2fwps%2fwcm%2fconnect%2f5235aa20-fee1–4e5b-86f58d6e09f351b6%2fshell_eggs_from_farm_to_table.pdf%3fmod%3dajperes&usg=afqjcnflrhj01jg7qhkkfx8gsc6snb3owq.
5. Anonymous (2017). European parliament and the council of the European union. 2017/745 of the European parliament and of the council of 5 April 2017 on medical devices, amending directive 2001/83/ec, regulation (ec) no 178/2002. https://eur-lex.europa.eu/eli/reg/2017/745/oj. (Accessed date: 10.10.2023).
6. Coronel-Reyes, J., Ramirez-Morales, I., Fernandez-Blanco, E., Rivero, D., & Pazos A. (2017). Determination of egg storage time at room temperature using a low-cost NIR spectrometer and machine learning techniques. Computers and Electronics in Agriculture. 145 (2018), pp. 1-10, 10.1016/j.compag.2017.12.030.
7. Cevik, K. K., Kocer, H. E., & Boga, M. (2022). Deep Learning Based Egg Fertility Detection. Vet. Sci. 2022, 9(10), 574; https://doi.org/10.3390/vetsci9100574.
8. Dang, D. X., Li, C. J., Cui, Y., Zhou, H., Lou, Y., & Li, D. (2023). Egg quality, hatchability, gosling quality, and amino acid profile in albumen and newly-hatched goslings’ serum as affected by egg storage. Poultry Science, Volume 102, Issue 4, 2023.

9. Dong, X., Dong, J., Peng, Y., & Tang X. (2017). Comparative study of albumen pH and whole egg pH for the evaluation of egg freshness. Spectroscopy Letters, 50 (9), pp. 463-469, 10.1080/00387010.2017.1360357.
10. Dong, X., Li, Z., Shen, Z., & Tang, X. (2018a). Nondestructive egg freshness assessment from the equatorial and blunt region based on visible near infrared spectroscopy. Spectroscopy Letters, 51 (10) (2018a), pp. 540-546, 10.1080/00387010.2018.1525409.
11. Dong, X., Tang, X., Dong, J., Shen, Z., Li, Y., Peng, Y., & Li, Y. (2018b). Nondestructive egg freshness assessment of air chamber diameter by VIS-NIR. Spectroscopy Letters, (2018b).
12. Gao, X. W., Hui, R., & Tian, Z. (2017). Classification of CT brain images based on deep learning networks. Comput. Methods Programs Biomed., 138 49–5.
13. Haugh, R. R. (1937). The haugh unit for measuring egg quality. US Egg Poultry Magazine, 43, 552–555.
14. He, K. M., Zhang, X. Y., Ren, S. Q. & Sun, J. (2016). Deep Residual Learning for Image Recognition. Ieee Conference on Computer Vision and Pattern Recognition (Cvpr), 770-778.
15. Hossain, M., Hu, J., Yoo, J. S., Jang, S. Y., & Kim, I. H. (2023). Effect of Genetically Modified Organisms Feed Ingredients (Corn And Soybean) in Diet on Egg Production, Egg Broken Rate and Egg Quality in Layers. Brazilian Journal of Poultry Science. ISSN 1516-635X 2023 / v.25 / n.3 / 001-006.
16. Karoui, R., Kemps, B., Bamelis, F., De Katelaere, B., Decuypere, E., & De Baerdemaeker, J. (2006). Methods to evaluate egg freshness in research and industry: A review. European Food Research Technology, 222, 727–732.
17. Karoui, R., Nicolaï, B., & De Baerdemaeker, J. (2008). Monitoring the egg freshness during storage under modified atmosphere by fluorescence spectroscopy. Food and Bioprocess Technology, 1, 346–356.
18. Lecun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature. 521(7553), pp. 436–444. doi: 10.1038/nature14539.
19. Narushin, V., Romanov, M., Salamon, A., & Kent, J. (2023). Egg Quality Index: A more accurate alternative to the Haugh unit to describe the internal quality of goose eggs. Food Bioscience. 55. Article 102968. 10.1016/j.fbio.2023.102968.
20. Robinson, D. S., & Monsey, J. B. (1972). Changes in the composition of ovomucin during liquefaction of thick white. Journal of the Science of Food and Agriculture, 23, 29–38.
21. Shi, C., Wang, Y., Zhang, C., Yuan, J., Cheng, Y., Jia, B., & Zhu, C. (2022). Nondestructive Detection of Microcracks in Poultry Eggs Based on the Electrical Characteristics Model. Agriculture. 12. 1137. 10.3390/agriculture12081137.
22. Tabidi M. H. (2011). Tabidi Impact of storage period and quality on composition of table egg Adv. Environ. Biol.. 5, pp. 856-861.
23. Tainika B., Abdallah N., Damaziak, K., Waithaka, N., Shah, T., & Wojcik, W. (2020). Egg storage conditions and manipulations during storage: effect on egg quality traits, embryonic development, hatchability and chick quality of broiler hatching eggs. World's Poultry Science Journal, DOI: 10.1080/00439339.2023.2252785.
24. Wang, S., Cheng, J., & Wen, Y. (2010). Research on non-destructive comprehensive detection and grading of poultry eggs based on intelligent robot D. Li, C. Zhao (Eds.), Computer and computing technologies in agriculture III. Springer (2010), pp. 487-498.
25. Wells, P. C., & Norris, K. H. (1987). Egg quality current problem and recent advances. In B. M. Freeman (Ed.), Egg quality current problems and recent advances. Abingdon: Carfax.
26. Yang, J., Qie, R., Li, T., Shi, Y., & Pan, H. (2016). Nondestructive Detection Method of Egg Quality Based on Multi-Sensor Information Fusion Technology. Journal of Computational and Theoretical Nanoscience. 13. 5932-5937. 10.1166/jctn.2016.5508.
27. Yimenu, S. M., Kim, J. Y., & Kim, B. S. (2017a). Prediction of egg freshness during storage using electronic nose. Poultry Science, Volume 96, Issue 10, Pages 3733-3746, ISSN 0032-5791, https://doi.org/10.3382/ps/pex193.
28. Yimenu, S. M., Kim, J. Y., Koo, J., & Kim, B. S. (2017b). Predictive modeling for monitoring egg freshness during variable temperature storage conditions. Poultry Science. Volume 96, Issue 8, Pages 2811-2819, ISSN 0032-5791, https://doi.org/10.3382/ps/pex038.
29. Zhang, J., Lu, W., Jian, X., Hu, Q., & Dai, D. (2023). Nondestructive Detection of Egg Freshness Based on Infrared Thermal Imaging. Sensors. 23. 5530. 10.3390/s23125530.