# Fertility Detection of Hitching Eggs Based on a Convolutional Neural Network
## 1 Introduction
The experimental results show that this method successfully solves the multi-classification problem in a small-scale dataset of hatching eggs and obtains a detection accuracy up to 99.5%. Visible transmission spectroscopy combined with multivariate analysis was used to develop a nonde-structive detection system for hatching egg fertility [9], which has a rather high classification accuracy of hatching eggs. However, the effectiveness of multivariate analysis remains to be verified on large-scale datasets of hatching eggs with this method. These methods mentioned above have greatly improved the fertility detection performance of hatching eggs. However, these methods either cause damage to the embryos or require complicated data preprocessing and have poor detection performance. As an important feature that can directly reflect the survival of animals, a heartbeat signal has the advantages of simplicity, reality and objectivity. In recent years, PhotoPlethysmoGraphy (PPG) has been widely used in the measurement of animal heart rate [10; 11; 12]. As a non-invasive means to obtain the heartbeat signal of animals, the noise of the heartbeat signal collected by the photoelectric volume pulse wave tracing technique is less than other acquisition methods and can truly reflect the animal's heart function. With the rapid improvement of hardware computing power and big data, deep learning is becoming more and more popular. As a representative algorithm of deep learning, the convolution neural network is popular among academia and industry due to the rapid development of deep learning and the outstanding performance in substantial visual tasks. In order to improve the performance of CNN, a lot of excellent CNN models have emerged in recent years and have achieved good performance [13; 14; 15]. Furthermore, there are a number of effective measures used for optimizing network structure and improving the classification accuracy of the model. Reference [15] proposed a parameter-modified linear unit that generalized the traditional linear unit and accelerated the model fitting speed with an extra computation cost of almost zero. Reference [16] proposed an operation named Batch normalization to accelerate the convergence of the model and alleviate the gradient dispersion of the deep network. ResNet [17] has largely settled the problem of vanishing gradients emergence which shackles convergence from the beginning with the network depth increasing. The Squeeze-Excitation (SE) module [18] adaptively reconstructs the channel features by weighting the output of each convolutional layer to improve the network performance. In this paper, we propose a method to solve the problem of fertility detection and classification of hatching eggs by combining CNN and heartbeat signal of hatching eggs. The main contributions of this work are as follows:  
* We propose a deep learning method for the fertility detection of hatching eggs. Our method achieves better performance on survival identification of the hatching eggs thanks to the combination of CNN and the heartbeat signal of hatching eggs. * We consider the heartbeat signal of hatching eggs as an effective feature to distinguish between fertile eggs and dead eggs, and collect it by using the method of PhotoPlethysmoGraphy to avoid introducing a lot of noise. * We design a sequence convolutional neural network E-CNN which is used to classify heartbeat sequence of hatching eggs. * We design a 10-layer-deep convolutional neural network SR-CNN for the fertility detection of hatching eggs by recognizing the heartbeat signal waveform of the embryos, which combines the channel weighting unit \"Squeeze-and-Excitation\" (SE) and the residual structure to improve the performance of the network.