Reducing the environmental footprint in hatcheries through a new approach to sexing bird eggs

Abstract

Industrial poultry farming can satisfy the population’s need for meat up to 98%, and for eggs – 92%. With the growth of world production of poultry products, the volume of hatchery waste also increases, because the hatched cockerel chicks are destroyed after incubation due to the inefficiency of their further cultivation (more than 7 billion). Determination of the sex of the embryo in the egg before incubation will significantly reduce the cost of egg production and the environmental burden from the activities of poultry farms. Within the framework of this article, the tasks of developing models for determining the sex of an embryo in a bird egg before incubation using machine learning (ML) methods are solved. During the first experiment, the identifiability of each of the samples was checked by the ML methods. During the second experiment, using various methods (decision trees, random forests, adaptive boosting, logistic regression and support vectors), a preliminary set of models was obtained. The third experiment ended with the formation of the resulting set of features and obtaining the final ML model. This made it possible to determine the sex of the embryo using 16 geometric parameters of the egg with an acceptable level of accuracy.