Aboonajmi, M., Akram, A., Nishizu, T., Kondo, N., Setarehdan, S.K. & Rajabipour, A. (2010). An ultrasound based technique for the determination of poultry egg quality. Research in Agricultural Engineering, 56(1), pp.26-32.
Aboonajmi, M. & Najafabadi, T.A. (2012). Quality assessment poultry egg using spectroscopy and maximum liklihood(ml) classifier. In NABEC-CSBE/SCGAB 2012 Joint Meeting and Technical Conference. Jul. 15-18. Ontario, Canada.
Amiriparian, S., Gerczuk, M., Ottl, S., Cummins, N., Freitag, M., Pugachevskiy, S., Baird, A. & Schuller, B.W. (2017). Snore Sound Classification Using Image-Based Deep Spectrum Features. In INTERSPEECH. Aug, 3512-3516.
Boddapati, V., Petef, A., Rasmusson, J., & Lundberg, L. (2017). Classifying environmental sounds using image recognition networks. Procedia computer science, 112, pp.2048-2056.
Chamberlain, D., Kodgule, R., Ganelin, D., Miglani, V., & Fletcher, R. R. (2016). Application of semi-supervised deep learning to lung sound analysis. In 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Aug, 804-807.
Deshpande, H., Singh, R. & Nam, U. (2001). Classification of music signals in the visual domain. In COST-G6 conference on digital audio effects. Dec. 7-9. Verona, Italy.
Dutta, R., Hines, E.L., Gardner, J.W., Udrea, D.D. & Boilot, P. (2003). Non-destructive egg freshness determination: an electronic nose based approach. Measurement Science and Technology, 14(2), pp.190.
Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., & Lew, M. S. (2016). Deep learning for visual understanding: A review. Neurocomputing, 187, pp.27-48.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778.
Kamilaris, A. & Prenafeta-Boldu´, F.X. (2018). Deep learning in agriculture: a survey. Computer and Electronics in Agriculture, 147, pp.70–90.
Karoui, R., Kemps, B., Bamelis, F., De Ketelaere, B., Decuypere, E. & De Baerdemaeker, J. (2006). Methods to evaluate egg freshness in research and industry: A review. European Food Research and Technology, 222(5-6), pp.727-732.
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(4), pp.346-356.
Kemps, B.J., De Ketelaere, B., Bamelis, F.R., Mertens, K., Decuypere, E.M., De Baerdemaeker, J.G. & Schwägele, F. (2007). Albumen freshness assessment by combining visible near-infrared transmission and low-resolution proton nuclear magnetic resonance spectroscopy. Poultry science, 86(4), pp.752-759.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, 1097-1105.
Lin, H., Zhao, J., Sun, L., Chen, Q. & Zhou, F. (2011). Freshness measurement of eggs using near infrared (NIR) spectroscopy and multivariate data analysis. Innovative Food Science & Emerging Technologies, 12(2), pp.182-186.
Liu, Y., Ying, Y., Ouyang, A. & Li, Y. (2007). Measurement of internal quality in chicken eggs using visible transmittance spectroscopy technology. Food control, 18(1), pp.18-22.
Omid, M., Soltani, M., Dehrouyeh, M. H., Mohtasebi, S. S., & Ahmadi, H. (2013). An expert egg grading system based on machine vision and artificial intelligence techniques. Journal of food engineering, 118(1), pp.70-77.
Pan, L. Q., Zhan, G., Tu, K., Tu, S. C. & Liu, P. (2011). Eggshell crack detection based on computer vision and acoustic response by means of back-propagation artificial neural network. European Food Research Technology, 233 (3), pp.457–463.
Ragni, L., Al-Shami, A., Berardinelli, A., Mikhaylenko, G. & Tang, J. (2007). Quality evaluation of shell eggs during storage using a dielectric technique. Transactions of the ASABE, 50(4), pp.1331-1340.
Ragni, L., Cevoli, C. & Berardinelli, A. (2010). A waveguide technique for non-destructive determination of egg quality parameters. Journal of Food Engineering, 100, pp.343-348.
Raji, A.O., Aliyu, J., Igwebuike, J.U. & Chiroma, S. (2009). Effect of storage methods and time on egg quality traits of laying hens in a hot dry climate. Journal of Agricultural and Biological Science, 4(4), pp.1-7.
Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv, 1409.1556.
Suktanarak, S. & Teerachaichayut, S. (2017). Non-destructive quality assessment of hens’ eggs using hyperspectral images. Journal of Food Engineering, 215, pp.97-103.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V. & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, 1-9.
Yalcin, H. (2017). Plant phenology recognition using deep learning: Deep-Pheno. In 6th International Conference on Agro-Geoinformatics, Fairfax VA, USA.
Yongwei, W., Wang, J., Zhou, B. & Lu, Q. (2009). Monitoring storage time and quality attribute of egg based on electronic nose. Analytica Chimica Acta, 650(2), pp.183-188.
Zhang, W., Cui, D. & Ying, Y. (2014). Nondestructive measurement of pear texture by acoustic vibration method. Postharvest Biology Technology, 96, pp.99–105.
Zhang, W., Pan, L., Tu, S., Zhan, G. & Tu, K. (2015). Non-destructive internal quality assessment of eggs using a synthesis of hyperspectral imaging and multivariate analysis. Journal of Food Engineering, 157, pp.41-48.
Zhao, J., Lin, H., Chen, Q., Huang, X., Sun, Z. & Zhou, F. (2010). Identification of egg’s freshness using NIR and support vector data description. Journal of food Engineering, 98(4), pp.408-414.
)