FOOD SAFETY AND FRESHNESS THROUGH THE EYES OF ARTIFICIAL INTELLIGENCE: SURVEY, TRENDS, AND CHALLENGES

Importance of AI and DL: The integration of Artificial Intelligence (AI) and Deep Learning (DL) into food safety and freshness monitoring is revolutionizing the food industry by enabling real-time, data-driven quality control. This paper explores key AI applications—including electronic nose systems for meat freshness evaluation, computer vision for defect detection, intelligent packaging with smart sensors, and blockchain, enabled traceability, that enhance food safety while reducing waste. AI-based Solutions and Challenges: AI-driven solutions such as predictive analytics for dynamic shelf-life estimation, hyperspectral imaging for contamination detection, and automated sorting systems demonstrate superior accuracy over traditional methods. However, challenges like sensor reliability, data variability, high implementation costs, and regulatory compliance remain barriers to widespread adoption. Future Trends in Terms of Sustainability: Emerging trends highlight the potential of edge AI, federated learning, and explainable AI (XAI) to improve scalability and consumer trust. The future of food safety lies in smart, interconnected systems that ensure transparency, efficiency, and sustainability across the supply chain. By addressing current limitations, AI and DL can transform food quality management, delivering safer, fresher, and more sustainable food products to global markets. Keywords: Food Safety and Freshness, Artificial Intelligence, Deep Learning, Electronic Nose, Smart Packaging, Predictive Analytics, Intelligent Traceability, Real-Time Monitoring.