Hatching eggs classification based on deep learning

Authors

Lei Geng, Tianjin University
Tingyu Yan, Tianjin Polytechnic University
Zhitao Xiao, Tianjin University of Technology, Tianjin Polytechnic University, Institute of Molecular and Cell BiologyFollow
Jiangtao Xi, University of WollongongFollow
Yuelong Li, Tianjin Polytechnic University

RIS ID

117390

Publication Details

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.

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.