Published Paper Details:

Food waste reduction, as a major application area of the Internet of Things (IoT) and big data technologies, has become one of the most pressing issues. In recent years, there has been an unprecedented increase in food waste, which has had a negative impact on economic growth in many countries. Food waste has also caused serious environmental problems. Agricultural production, post-harvest handling, and storage, as well as food processing, distribution, and consumption, can all lead to food wastage. This wastage is primarily caused by inefficiencies in the food supply chain and a lack of information at each stage of the food cycle. In order to minimize such effects, the Internet of Things, data science, and various management models are used to reduce food waste in food supply chains. This paper provides a comprehensive review of IoT and data science based food waste management models, algorithms, and technologies with the aim of improving resource efficiency and highlights the key challenges and opportunities for future research. Food waste has become a critical global challenge, with significant economic, environmental, and social implications. This project focuses on developing a data-driven solution for food menu recommendation and waste management using data science. By leveraging historical data and predictive analytics, the system aims to optimize food utilization and minimize waste at the household and organizational levels. Machine learning models are employed for predicting waste and recommending efficient menus based on consumption patterns, ensuring resource efficiency and sustainability. This project presents a comprehensive solution to improve decision-making in food management while reducing waste.