Sheep Ages Recognition Based on Teeth Images

Hadi Yahia Albrkaty1

1

Publication Date: 2024/09/17

Abstract: The health of sheep’s teeth affects the abundance of meat and their good health through their healthy teeth, as it may cause their teeth to erode or break due to the presence of lean sheep. Also, by looking at the teeth of sheep, we can categorize them according to their ages to deal with each type as needed. Knowing the sheep age from their teeth is a pure sheep owners and shepherds’ skill. The spread of cell phones presents an opportunity for any people to benefit from many applications that make strange and difficult domains familiar to the public. Designing and implementing a sheep ages recognition system would significantly affect the speed and quality work of many buyers, sellers and interested people. The proposed project aims at addressing the Sheep ages recognition problem. A number of efficient deep learning architectures will be used, in order to select the best one that ensure the trade-off between optimizing the classification performance and model size. Moreover, a real dataset will be collected for 3 different sheep ages and an appropriate performance metrics will be used to evaluate the different proposed models. Besides, pre-processing and data augmentation techniques will be investigated to overcome the collected data.

Keywords: No Keywords Available

DOI: https://doi.org/10.38124/ijisrt/IJISRT24SEP371

PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT24SEP371.pdf

REFERENCES

  1. Geoff Casburn, Sheep Development Officer, Wagga Wagga. July 2016, Primefact 1481, second edition.
  2. R. Azad, B. Azad, and I. T. Kazerooni, ''Real-time and robust method forhand gesture recognition system based on cross-correlation coefficient'', Adv. Comput. Sci., Int. J., vol. 2, no. 5/6, pp. 121–125, Nov. 2013.
  3. X. Chen, L. Zhang, T. Liu, and M. M. Kamruzzaman, ''Research on deep learning in the field of mechanical equipment fault diagnosis image quality'', Journal of Visual Communication and Image Representation, Vol. 62, pp. 402–409, Jul. 2019.
  4. A. Abyaneh, A. Foumani and V. Pourahmadi, ''Deep Neural Networks Meet CSI-Based Authentication'', arXiv Organization, Cornell university, Nov. 2018.
  5. M. S. Hossain and G. Muhammad, ''Emotion recognition using secure edge and cloud computing'', Information Sciences, Vol. 504, No. 2019, pp. 589–601, Dec. 2019.
  6. D. Melinte and L. Vladareanu, ''Facial Expressions Recognition for Human–Robot Interaction Using Deep Convolutional Neural Networks with Rectified Adam Optimizer'', MDPI Journal, Vol. 20, Issue. 13, Apr. 2020.
  7. Q. Zhu, P. Zhang, Z. Wang and X.Ye, ''A New Loss Function for CNN Classifier Based on Predefined Evenly-Distributed Class Centroids'', IEEE Transaction and Journals, Vol. 8, pp. 10888-10895, Dec. 2019.
  8. A. Abdelhady, AE. Hassanenin, A. Fahmy A. ''Sheep identity recognition, age and weight estimation datasets''. arXiv.org perpetual, pp. 1-7, 2018.
  9. H. Raadsma, I. Harris, D. Tao, M. Khatkar, J. Gao, S. Thompson, W. Gibson and M. Ferguson ''Artificial Intelligence in Wool Production'', pp. 1-44, 2019.
  10. C. Ma, X. Sun, C. Yao, M. Tian, L. Li , ''Research on Sheep Recognition Algorithm Based on Deep Learning in Animal Husbandry'', Journal of Physics: Conference Series.
  11. K. Simonyan, and A. Zisserman, '' Very Deep Convolutional Networks for Large-Scale Image Recognition''. The 3rd International Conference on Learning Representations (ICLR2015), 2015.
  12. https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_v1.py.
  13. K. He, X. Zhang, S. Ren and J. Sun, “Deep Residual Learning for Image Recognition,” in CVPR, 2016.
  14. S. Xie, R.B. Girshick, P. Dollar, Z. Tu and K. He, '' Aggregated Residual Transformations for Deep Neural Networks '', journal CoRR, volume abs/1611.05431, 2016.
  15. Tan, M., & Le, Q. (2019, May). Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning (pp. 6105-6114). PMLR.
  16. A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library. In: Advances in Neural Information Processing Systems 32 [Internet]. Curran Associates, Inc. p. 8024–35, 2019.
  17. M. Chao, S. Xiaolin, Y. Chunxia, T. Minglu, L. Linyi, 'Research on Sheep Recognition Algorithm Based on Deep Learning in Animal Husbandry', Journal of Physics: Conference Series, 2020.
  18. M . Grinberg, “Flask web development: developing web applications with python.”,  " O'Reilly Media, Inc.",   2018.