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A hatching egg activity classification method aims to accurately and quickly distinguish between dead embryos and live embryos. The existing embryonic classification models collect egg images via a specific imaging system. The image features are then extracted to identify and classify the properties of the hatching eggs. The current state-of-the-art embryonic image classification methods are easily affected by the image quality and are not efficient. To address these issues, we propose a new classification model based on fully convolutional networks (FCNs) and a gated recurrent unit (GRU) that decides whether an embryo is dead or alive by determining embryotic heartbeat signal indicators. Our dataset consists of heartbeat signals from 50k distinct chicken embryos. The experimental results based on our dataset show that our proposed model is the most accurate compared with all baseline models. The reason for this is that our model can capture more useful information from heartbeat signals. In addition, our model can classify 83 hatching eggs per second.

Fully Convolutional Network With Gated Recurrent Unit for Hatching Egg Activity Classification