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, Volume 77, Issue 17, pp 22071–22082 | Cite as

Hatching eggs classification based on deep learning

  • Lei Geng
  • Tingyu Yan
  • Zhitao Xiao
  • Jiangtao Xi
  • Yuelong Li
Article
  • 182 Downloads

Abstract

In order to realize the fertility detection and classification of hatching eggs, a method based on deep learning is proposed in this paper. The 5-days hatching eggs are divided into fertile eggs, dead eggs and infertile eggs. Firstly, we combine the transfer learning strategy with convolutional neural network (CNN). Then, we use a network of two branches. In the first branch, the dataset is pre-trained with the model trained by AlexNet network on large-scale ImageNet dataset. In the second branch, the dataset is directly trained on a multi-layer network which contains six convolutional layers and four pooling layers. The features of these two branches are combined as input to the following fully connected layer. Finally, a new model is trained on a small-scale dataset by this network and the final accuracy of our method is 99.5%. The experimental results show that the proposed method successfully solves the multi-classification problem in small-scale dataset of hatching eggs and obtains high accuracy. Also, our model has better generalization ability and can be adapted to eggs of diversity.

Keywords

Deep learning CNN Transfer learning Classification Hatching eggs 

Notes

Acknowledgements

This work is supported by National Natural Science Foundation of China under grant No.61771340 and the key technologies R & D program of Tianjin under grant No.14ZCZDGX00033.

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Lei Geng
    • 1
    • 2
  • Tingyu Yan
    • 1
    • 2
  • Zhitao Xiao
    • 1
    • 2
  • Jiangtao Xi
    • 3
  • Yuelong Li
    • 4
  1. 1.School of Electronics and Information EngineeringTianjin Polytechnic UniversityTianjinChina
  2. 2.Tianjin Key Laboratory of Optoelectronic Detection Technology and SystemsTianjinChina
  3. 3.School of Electrical, Computer and Telecommunications EngineeringUniversity of WollongongWollongongAustralia
  4. 4.School of Computing EngineeringTianjin Polytechnic UniversityTianjinChina

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