Adaptive IoT and AI-Enhanced QualityAssurance System for Sustainable and Secure Bakery Production

This research is the response of the growing demand for high- quality, sustainable bakery products, an adaptive IoT and AI-enhanced quality assurance framework tailored for bakery production. The proposed system integrates real-time IoT sensors with adaptive machine learning algorithms, enabling dynamic adjustments to baking parameters based on environmental variations and product specifications. This self- learning system aims to ensure consistent product quality while reducing production inefficiencies. To address cybersecurity concerns associated with IoT networks, the framework incorporates anomaly detection algorithms and secure communication protocols, enhancing resilience against cyber threats and safeguarding operational integrity. The system also prioritizes sustainability, utilizing energy-efficient IoT devices and exploring energy harvesting from bakery equipment to power sensors, thereby minimizing the environmental footprint of the bakery's IoT infrastructure.In addition, the framework features a consumer feedback loop, enabling end-users to contribute insights on product quality, which further refines the AI algorithms in alignment with market preferences. Through advanced data management, the system minimizes waste and optimizes resource use, advancing sustainable production. This research aims to create a scalable, secure, and eco-conscious solution that revolutionizes bakery production, setting a precedent for IoT and AI applications in food manufacturing.