Design of an AI-based egg fertility detection system for incubators

The conventional approach to determine the fertility of chicken eggs, called candling, is subjective, time-consuming, and ineffective and thereby results in low hatching rates and financial losses. As a result, a more reliable, accurate, and sustainable system that uses artificial intelligence and automated systems to improve the process of assessing egg fertility for incubation. The system proposes capturing of images of eggs at the early stages of incubation by a camera and sent to a cloud server, where a Convolutional Neural Network (CNN) analyses and classifies the data to determine the fertility status of the eggs. The system aims to increase the quality and hatching rate of chicken eggs, lower labour expenses and human error, and boost the poultry industry's sustainability and profitability. The framework of the system is based on the current poultry egg fertility assessment methods in Zimbabwe, which include candling, infrared thermography, ultrasound, heart rate detection, oxygen flux detection, visible or near-infrared transmittance spectroscopy, and thermal imaging. The system is expected to perform better than existing systems in terms of accuracy, speed, and cost-effectiveness. The experimental results show that, the method's average identification time is 0.210 seconds, and its identification accuracy is 98.40 percent. These results demonstrate the viability of the suggested AI-based method for automatically determining an egg's fertility without human intervention, greatly increasing hatcheries' and related businesses' productivity.