AI- Kitchen Mate : Smart Cooking for Zero Food Waste

EcoKitchen Connect demonstrates how intelligent, data-driven systems can be effectively applied at the household level to address the persistent challenge of food waste. By integrating digital inventory management with expiry-aware prediction and personalized recommendations, the system transforms routine kitchen activities into informed and waste-conscious decision-making processes. The proposed approach shifts the focus from reactive waste handling to proactive prevention, which is widely recognized as the most effective strategy for reducing food waste and its associated environmental impact. The system’s modular and layered architecture enables seamless interaction between user interfaces, data management components, and AI-supported decision modules. This design not only supports scalability and adaptability but also ensures that EcoKitchen Connect can function across a range of technological environments from basic mobile-only households to advanced smart kitchens equipped with IoT devices. The lightweight implementation strategy allows the system to operate efficiently on low-power devices, making it accessible to a broad user base without requiring specialized hardware or infrastructure. Through experimental evaluation and user-centered analysis, EcoKitchen Connect has shown measurable benefits in reducing forgotten and expired food items. Users reported improved awareness of inventory status, more efficient meal planning, and greater utilization of leftover ingredients. The expiry-aware recommendation methodology played a central role in these outcomes by guiding users toward timely actions, such as consuming or sharing food before spoilage. These findings highlight the system’s potential to deliver both economic benefits to households and environmental benefits through reduced waste. Beyond individual households, EcoKitchen Connect offers broader societal value by encouraging ethical consumption and community-oriented food sharing. The system’s support for donation and redistribution aligns with sustainability goals and contributes to addressing food insecurity. By promoting responsible consumption habits and facilitating the redirection of surplus food, the platform extends its impact beyond waste reduction to social responsibility. In conclusion, EcoKitchen Connect provides a practical, scalable, and ethically grounded solution for intelligent kitchen management. Its AI-ready design establishes a strong foundation for future enhancements while delivering immediate benefits in real-world settings. The system illustrates how user-centered AI applications can play a significant role in advancing sustainable living, reducing environmental impact, and supporting efficient resource utilization at both household and community levels.

[1] FAO, Global Food Losses and Food Waste – Extent, Causes and Prevention, Food and Agriculture Organization of the United Nations, Rome, Italy, 2011. [2] B. Gustavsson, J. Cederberg, and U. Sonesson, “Global food losses and food waste,” FAO Report, Rome, Italy, 2011. [3] A. Papargyropoulou et al., “The food waste hierarchy as a framework for the management of food surplus and food waste,” Journal of Cleaner Production, vol. 76, pp. 106–115, 2014. [4] S. Thyberg and D. J. Tonjes, “Drivers of food waste and their implications for sustainable policy development,” Resources, Conservation & Recycling, vol. 106, pp. 110–123, 2016. [5] K. Parfitt, M. Barthel, and S. Macnaughton, “Food waste within food supply chains,” Philosophical Transactions of the Royal Society B, vol. 365, no. 1554, pp. 3065–3081, 2010. [6] J. Aschemann-Witzel et al., “Consumer-related food waste: Causes and potential for action,” Sustainability, vol. 7, no. 6, pp. 6457–6477, 2015. [7] L. Silvennoinen et al., “Food waste volume and composition in households,” Food Policy, vol. 37, no. 2, pp. 143–151, 2012. [8] S. Principato, Food Waste at Consumer Level: A Comprehensive Literature Review, Springer, 2018. [9] G. Beigl et al., “IoT-based monitoring systems for food waste reduction,” IEEE Internet of Things Journal, vol. 6, no. 3, pp. 5123–5132, 2019. [10] A. Sheth, “Internet of Things to smart IoT through semantic, cognitive and perceptual computing,” IEEE Intelligent Systems, vol. 31, no. 2, pp. 108–112, 2016.