Abstract:
The poultry industry has experienced significant development in recent years due to the rising demand for high-quality protein sources. However, it is crucial to maintain the quality of chicken eggs during the incubation process in order to ensure the generation of healthy chicks and optimal production results. At present, the conventional method of manually inspecting eggs and using artificial candling leads to a time-consuming, labor-intensive, and error-prone process of evaluating and monitoring the quality of poultry eggs. The quality of eggs is influenced by a range of environmental conditions, including temperature, humidity, and lighting, which can be challenging to control through manual means. Furthermore, the absence of immediate feedback results in delays in identifying possible issues during the incubation process. In addition, current technologies lack guidance on automating the egg mirage process and enhancing the incubation process. This study aims to address these challenges by developing a comprehensive solution that combines artificial intelligence (AI) and Internet of Things (IoT) technologies. The solution automates the egg mirage process and the environmental aspect of egg quality analysis and monitoring. This automation reduces the reliance on manual labor and enhances the accuracy of data collection. Consequently, it improves the efficiency and quality of the egg production process while also reducing costs. The web-based program will display real-time feedback on the quality of eggs, allowing the farmer to easily assess the condition and maturity of the eggs in the incubator. A Convolutional Neural Network was employed as the classifier algorithm for discerning between viable, non-fertile, and deceased eggs. The suggested system achieves a TinyML classification F1Score of 97.4% when deployed on the Arduino Nano 33 BLE Sense. This deployment demonstrates notable enhancements in efficiency, leading to an accuracy rate of 95.79%. The system possesses the capacity to revolutionize the poultry industry by offering a more effective and precise method to monitor the quality of eggs. This enables poultry farmers to enhance and mechanize the process, reduce errors, and offer immediate feedback, leading to accelerated incubation and increased production rates.
