This research project focuses on developing an intelligent monitoring system using artificial intelligence to evaluate food quality in real-time across production and processing environments.

The system integrates computer vision, machine learning algorithms, and data analytics to analyze visual and chemical indicators of food quality. It is designed to detect contamination, classify food products, and identify defects with high accuracy.

Advanced predictive models are used to assess potential risks and ensure compliance with international food safety standards. The system also enables automated decision-making processes, reducing reliance on manual inspection.

This project contributes to the development of smart food safety systems and supports the adoption of AI-driven technologies in modern food industries.