An intelligent AI-based sustainable food packaging and storage system for waste minimization and environmental sustainability

Food spoilage and inefficient storage remain major challenges in modern food supply chains, where sustainability, resource optimization, and energy efficiency are critical. This study proposes an AI-based smart food packaging and storage system using Reinforcement Learning (RL) to support real-time preservation decisions. The RL agent monitors packaging performance, estimates spoilage risk, optimizes energy use, and selects appropriate control actions to maintain product quality and sustainability. In the present implementation, spoilage risk is represented through a synthetic state-transition function to demonstrate decision-making capability, however, the framework is designed to incorporate sensory and microbiological data for future data-driven prediction. The methodology is implemented in Python using standard libraries such as TensorFlow and NumPy for simulation and real-time control. System performance is evaluated using key indicators, including energy efficiency, waste reduction, sustainability, and inventory management, showing marked improvement over conventional strategies. The RL-optimized system achieves an overall performance score of 94% compared with traditional approaches. The proposed framework integrates adaptive RL-based decision-making with sustainable packaging material selection, creating a dynamic and environmentally responsible storage ecosystem beyond traditional static control methods. Its novelty lies in combining real-time learning, smart material selection, and multi-objective optimization within a unified architecture. Results indicate that AI-enabled packaging and storage can significantly reduce waste and energy consumption while supporting environmental sustainability and food security. Beyond storage optimization, the framework offers scalability for broader sustainable supply chain management and real-time waste control. Future work will prioritize the integration of IoT-enabled sensing for real-time, data-driven storage management systems globally.