Egg fertility detection is an important aspect in increasing poultry hatching productivity. The traditional candling method with light has limitations in accuracy, especially at an incubation age of more than ten days. The study proposes IS-Candling, a web application to detect hatching egg fertility based on the Internet of Things (IoT) and artificial intelligence (AI). The system is designed so that embryonic development can be detected using cameras in real-time, then processed using machine learning algorithms to identify fertile or infertile conditions. The results of the analysis are visualized through a web interface in the form of an interactive dashboard, embryo development graph, egg status, and reports that can be accessed by farmers at any time. The test is carried out using the black box testing method to assess the functionality of the application and ease of use. The test results showed that the IS-Candling web application was able to display data accurately, improve monitoring efficiency, and provide better decision support than manual candling methods. This research contributes to the development of intelligent hatchery technology by leveraging the integration of IoT and web-based AI, which can support the modern livestock industry towards digitalization.
Utomo et al. (Thu,) studied this question.