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Fertility detection of hatching eggs based on a convolutional neural network

Journal Article


Abstract


  • In order to achieve the goal of detecting the fertility of hatching eggs which are divided into fertile eggs and dead eggs more accurately and effectively, a novel method combining a convolution neural network (CNN) and a heartbeat signal of the hatching eggs is proposed in this paper. Firstly, we collected heartbeat signals of 9-day-later hatching eggs by the method of PhotoPlethysmoGraphy (PPG), which is a non-invasive method to detect the change of blood volume in living tissues by photoelectric means. Secondly, a sequential convolutional neural network E-CNN, which was used to analyze heartbeat sequence of hatching eggs, was designed. Thirdly, an end-to-end trainable convolutional neural network SR-CNN, which was used to process heartbeat waveform images of hatching eggs, was designed to improve the classification performance in this paper. Key to improving the classification performance of SR-CNN is the SE-Res module, which combines the channel weighting unit "Squeeze-and-Excitation" (SE) block and the residual structure. The experimental results show that two models trained on our dataset, with E-CNN and SR-CNN, are able to achieve the fertility detection of the hatching eggs with superior identification accuarcy, up to 99.50% and 99.62% respectively, on our test set. It is demonstrated that the proposed method is feasible for identifying and classifying the survival of hatching eggs accurately and effectively.

UOW Authors


  •   Geng, Lei (external author)
  •   Hu, Yuzhou (external author)
  •   Xiao, Zhitao (external author)
  •   Xi, Jiangtao

Publication Date


  • 2019

Citation


  • L. Geng, Y. Hu, Z. Xiao & J. Xi, "Fertility detection of hatching eggs based on a convolutional neural network," Applied Sciences (Switzerland), vol. 9, (7) pp. 1408-1-1408-16, 2019.

Scopus Eid


  • 2-s2.0-85064093760

Start Page


  • 1408-1

End Page


  • 1408-16

Volume


  • 9

Issue


  • 7

Place Of Publication


  • Switzerland

Abstract


  • In order to achieve the goal of detecting the fertility of hatching eggs which are divided into fertile eggs and dead eggs more accurately and effectively, a novel method combining a convolution neural network (CNN) and a heartbeat signal of the hatching eggs is proposed in this paper. Firstly, we collected heartbeat signals of 9-day-later hatching eggs by the method of PhotoPlethysmoGraphy (PPG), which is a non-invasive method to detect the change of blood volume in living tissues by photoelectric means. Secondly, a sequential convolutional neural network E-CNN, which was used to analyze heartbeat sequence of hatching eggs, was designed. Thirdly, an end-to-end trainable convolutional neural network SR-CNN, which was used to process heartbeat waveform images of hatching eggs, was designed to improve the classification performance in this paper. Key to improving the classification performance of SR-CNN is the SE-Res module, which combines the channel weighting unit "Squeeze-and-Excitation" (SE) block and the residual structure. The experimental results show that two models trained on our dataset, with E-CNN and SR-CNN, are able to achieve the fertility detection of the hatching eggs with superior identification accuarcy, up to 99.50% and 99.62% respectively, on our test set. It is demonstrated that the proposed method is feasible for identifying and classifying the survival of hatching eggs accurately and effectively.

UOW Authors


  •   Geng, Lei (external author)
  •   Hu, Yuzhou (external author)
  •   Xiao, Zhitao (external author)
  •   Xi, Jiangtao

Publication Date


  • 2019

Citation


  • L. Geng, Y. Hu, Z. Xiao & J. Xi, "Fertility detection of hatching eggs based on a convolutional neural network," Applied Sciences (Switzerland), vol. 9, (7) pp. 1408-1-1408-16, 2019.

Scopus Eid


  • 2-s2.0-85064093760

Start Page


  • 1408-1

End Page


  • 1408-16

Volume


  • 9

Issue


  • 7

Place Of Publication


  • Switzerland