Design of a Non-Invasive Egg Sexing Device Utilizing Artificial Intelligence for Duck Species

This study introduces a non-invasive egg sexing device that combines artificial intelligence (AI), spectroscopy, and computer vision technology to accurately determine the sex of duck embryos inside eggs. The device utilizes the plumage color as a reliable indicator of sex, employing a hybrid Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) model alongside spectroscopic analysis. Extensive simulations and experiments validate the proposed algorithm, achieving an impressive 98.68% accuracy rate in sex determination, with an average processing time of 37.46 milliseconds, significantly enhancing farming efficiency. Additionally, the research assesses the impact of spectroscopy on egg hatchability, demonstrating a higher hatchability rate of 74.80
^%
within a population of 500 eggs. This finding indicates that spectroscopy does not adversely affect egg viability. Overall, this study presents a sustainable solution for effectively managing male ducklings in the industry, optimizing resource utilization, and mitigating wastage.