Research on Non-destructive Identification of Chick Embryo Gender Based on Deep Learning

Abstract

In the chick hatching industry, a common practice is to directly eliminate male chicks after hatching. However, this practice results in significant resource wastage. Timely detection of embryo gender and selection of male embryos are of great significance for reducing resource wastage and improving economic benefits. To address the serious lack of gender identification technology during chick hatching, this paper proposes a non-destructive identification method for chick embryos based on deep learning. We use the PyTorch framework to build a deep learning model and divide the dataset into 80% training set and 20% validation set for model training and validation. Experimental results show that our proposed model achieves an accuracy of 72.5% on the validation set. This study not only solves key technical problems for non-destructive identification of chick embryo gender but also provides new research ideas for precise gender identification of other oviparous species, promoting the intelligent development of production and breeding industries.