# Fertility Detection of Hitching Eggs Based on a Convolutional Neural Network
## 1 Introduction  
Vaccine-virus immunization is the main method for avian flu pandemic preparedness. Currently bird flu vaccines are generally produced by brewing up live flu virus strains in specific pathogen-free eggs. In the process of virus strain embryos culture, the ability to automatically discriminate the survival of vaccinated eggs prior to virus proliferative cultivation, which can protect the normal eggs from bacterial infections and obtain sterile virus strains, is conducive to removing the dead vaccinated eggs early. If the dead embryos fail to be separated from the hatching eggs early and accurately, the same batch of cultured normal hatching eggs will be contaminated, which will result in major safety and health hazards. Therefore, the fertility detection and classification of the hatching eggs are significant for the production of avian influenza vaccine. Currently, the fertility detection of hatching eggs adopts the method of traditional manual candling by judging the blood vessel characteristics of the embryos. Nevertheless, this method can easily be affected by subjective factors and has the disadvantage of low detection accuracy. In addition, workers who work hard for a long period of time are prone to cause misdetection and missed detection. At present, there are some popular methods proposed to solve the survival identification and classification problems of hatching eggs. The machine vision methods are widely used in the research of early hatching eggs, which mainly includes image enhancement, segmentation and classification. A non-destructive detection system based on machine vision is designed for identifying the fertility of eggs prior to virus cultivation [1]. Firstly, this method uses the SUSAN (Small Univalue Segment Assimilating Nucleus) algorithm to detect and eliminate the speckle noise. Then a multi-layer feature extraction method is used to segment the blood vessel information in the image of the egg embryos. Finally, the fertility judgment of the hatching eggs is determined by calculating the percentage that the blood vessel area occupies in the ROI (Region of Interest) image. A multi-information fusion method is proposed for detecting the fertility of hatching eggs [2], which fuses the image, temperature and transmittance of embryos and takes it as input for the BP (Back Propagation) neural network to train and classify. Experiments show that the multi-information fusion technology can achieve 96.25% recognition accuracy, however, this method has high requirements on equipment and the operation process is rather complicated. A near-infrared hyperspectral imaging method is proposed to detect the fertility of early hatching eggs [3] by extracting two types of spectral transmission characteristics from the original and Gabor filtered images texture information. However, this method cannot realize multi-classification and the pretreatment process is complicated. High frequency ultrasound imaging was used to detect the development of embryonic cardiovascular tissue [4]. The improved B-mode ultrasound and Doppler effect [5] were used to study the cardiovascular development of egg embryos. However, this method could cause physical damage to the embryos. [PERSON] et al. [6] propose a method which fuses computer vision technique and the impact excitation technique to increase the detecting accuracy and stability of the fertility of hatching eggs during the early hatching period by adopting the LVQ (Learning Vector Quantization) artificial neural network. Weight Fuzzy C-means clustering algorithm [7] is utilized to find the threshold to segment the blood vessels of the hatching eggs. The fertility is detected by counting the number of blood vessels. A double branches convolutional neural network is proposed to realize the fertility detection and classification of 5-days hatching eggs by using a convolution neural network (CNN) to extract the features of the embryos' blood vessels [8].