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Hatching eggs classification based on deep learning

Journal Article


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.

UOW Authors


  •   Geng, Lei (external author)
  •   Yan, Tingyu (external author)
  •   Xiao, Zhitao (external author)
  •   Xi, Jiangtao
  •   Li, Yuelong (external author)

Publication Date


  • 2018

Citation


  • L. Geng, T. Yan, Z. Xiao, J. Xi & Y. Li, "Hatching eggs classification based on deep learning," Multimedia Tools and Applications, vol. 77, (17) pp. 22071-22082, 2018.

Scopus Eid


  • 2-s2.0-85033457130

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/843

Number Of Pages


  • 11

Start Page


  • 22071

End Page


  • 22082

Volume


  • 77

Issue


  • 17

Place Of Publication


  • United States

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.

UOW Authors


  •   Geng, Lei (external author)
  •   Yan, Tingyu (external author)
  •   Xiao, Zhitao (external author)
  •   Xi, Jiangtao
  •   Li, Yuelong (external author)

Publication Date


  • 2018

Citation


  • L. Geng, T. Yan, Z. Xiao, J. Xi & Y. Li, "Hatching eggs classification based on deep learning," Multimedia Tools and Applications, vol. 77, (17) pp. 22071-22082, 2018.

Scopus Eid


  • 2-s2.0-85033457130

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/843

Number Of Pages


  • 11

Start Page


  • 22071

End Page


  • 22082

Volume


  • 77

Issue


  • 17

Place Of Publication


  • United States