---

New technologies in determining the gender of eggs will greatly help to end the extermination of male chickens, which will save a lot in the poultry industry. These technologies are so valuable and important that many companies and research centers are willing to make large investments to progress in this field. Today, two invasive and non-invasive methods are used to determine the gender of the egg. Invasive diagnostic methods often lead to a decrease in the viability of samples, while non-invasive methods with high accuracy and viability of samples have created a great development capability among researchers. In other words, invasive diagnostic methods determine the gender of the embryos inside the egg with a high percentage, but it can endanger the continuation of the hatching process and jeopardize food safety. However, the use of non-invasive methods in line with industrial use has priority due to the fact that there is no danger to the chick embryo in the process of sex determination. In this review study, while examining the importance of gender determination during hatching for the poultry industry, an attempt has been made to examine and compare all the new technologies used to determine gender in the egg-laying and hatching stages and compare its advantages and disadvantages.

---

Sexing is a difficult task for most birds (especially ornamental birds) involving expensive, state-of-the-art equipment and experiments. An intelligent fowl sexing system was developed based on data mining methods to distinguish hen from cock hatchlings. The vocalization of one-day-old hatchlings was captured by a microphone and a sound card. To obtain more accurate information from the recordings, time-domain sound signals were converted into the frequency domain and the time-frequency domain using Fourier transform and discrete wavelet transform, respectively. During data-mining from signals of these three domains, 25 statistical features were extracted. The Improved Distance Evaluation (IDE) method was used to select the best features and also to reduce the classifier's input dimensions. Fowls’ sound signals were classified by Support Vector Machine (SVM) with a Gaussian Radial Basis Function (GRBF). This classifier identified and classified cocks and hens based on the selected features from time, frequency and time-frequency domains. The highest accuracy of the SVM at time, frequency and time-frequency domains was 68.51, 70.37 and 90.74 percent, respectively. Results showed that the proposed system can successfully distinguish between Hen and Cock hatchlings. The results further suggest that signal processing and feature selection methods can maximize the classification accuracy.