Prediction of Quality of Eggs in Poultry Farming

The work proposes an innovative system to modernize chicken egg quality testing and prediction using advanced image processing and deep learning. Comprising three modules, it aims to revolutionize poultry industry standards. The first module uses controlled lighting to take high-resolution pictures of eggs. Using variables like air cell size, the image processing module extracts important information including crack detection, yolk quantity, and egg age prediction. Hardware improvements include the creation of specialized tools, such as controlled lighting systems and precise cameras, to ensure the best possible image acquisition. A graphical user interface (GUI) that is easy to use places emphasis on features such as clear controls, educational displays, and tools for modifying parameters, examining results, and providing feedback. The objective of this method is to optimize interactions and speed integration with current poultry industry operations. This system is also beneficial for small-scale operations, allowing individual customers to use the device for personal egg quality assessment. Future research may explore incorporating more quality parameters and the development of complete system automated devices and a mobile app.