Non-Invasive Sex Identification of Early-Stage Poultry Eggs Using Machine Vision

The routine culling of day-old male chicks represents a major ethical concern in the poultry industry. This practice has been banned in Germany, and a similar ban is being considered by the European Union. Each year, hundreds of millions of day-old male chicks are culled in the EU, with several billion culled worldwide. Various methods have been developed to determine the sex of chicks before hatching; however, most are invasive and identify sex relatively late, potentially after the onset of pain perception in embryos. Existing approaches include polymerase chain reaction analysis, spectroscopy, analysis of volatile organic compounds, morphological analysis, and machine vision. Previous studies have shown that machine vision can achieve accuracies of up to 89.25% by analyzing blood vessel patterns during early incubation. Despite this potential, research remains limited, particularly regarding different chicken breeds and the temporal development of embryos. In this study, we investigate the impact of both breed variation and temporal information on early-stage sex identification. Image data were collected on incubation days 4, 5, and 6 from a total of 208 chicken eggs. A convolutional neural network (CNN) and a hybrid convolutional neural network–recurrent neural network (CNN–RNN) model were evaluated to analyze spatial and temporal features. The results show that the CNN model achieved an accuracy of up to 71.43%, while the hybrid CNN–RNN model reached 67.85%. These findings indicate that incorporating temporal information did not improve performance compared to the baseline CNN. However, due to the limited size and quality of the dataset, no definitive conclusions can be drawn.