AI-Powered Food Waste Minimization for Sustainable Food Systems

AI-Powered Food Waste Minimization for Sustainable Food Systems

Gomathi P,Rajesh V,Rishi Kesavan R,Pranesh MV,Santhosh S

Assistant Professor, Department of Computer Science and Engineering, Sri Shakthi Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India. E-mail: srigomathi.sri@gmail.com

#3 Student, Department of Computer Science and Engineering, Sri Shakthi Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India. E-mail: 9750rajesh@gmail.com

#4 Student, Department of Computer Science and Engineering, Sri Shakthi Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India. E-mail: praneshmanikandan446@gmail.com

#5 Student, Department of Computer Science and Engineering, Sri Shakthi Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India. E-mail: santhoshamuthavalli@gmail.com

#6 Student, Department of Computer Science and Engineering, Sri Shakthi Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India. E-mail: rishikesavan653@gmail.com

Abstract: Food waste is a growing global concern, contributing significantly to environmental degradation and economic loss. This project aims to address the issue of food waste management through an innovative, data-driven approach using machine learning techniques. By analyzing historical data on food production, consumption patterns, and waste trends, the project builds predictive models to identify and quantify factors that lead to food waste. These models help forecast potential waste and provide actionable insights for minimizing food loss across the supply chain, from production to consumption.Our machine learning algorithms assess patterns in data collected from various sources, enabling real-time decision-making for effective resource allocation, demand forecasting, and inventory management. The goal is to create a scalable and adaptable system that can be implemented in various sectors, such as retail, hospitality, and household consumption, to reduce waste at multiple stages.The results demonstrate that intelligent food waste management can significantly reduce waste levels and promote sustainable food practices. This project highlights the potential of technology-driven solutions in mitigating food waste, contributing to environmental sustainability, and supporting the global movement toward a more efficient and eco-friendly food system.Highlight how food waste impacts both individual and organizational finances, and how the project aims to reduce these losses through accurate demand predictions and waste prevention measures. Emphasize the environmental benefits, such as lowering greenhouse gas emissions and reducing the strain on natural resources by cutting down on food waste.Briefly mention the types of data used in the project, such as supply chain records, weather data (which can impact crop yields), or real-time inventory data from stores or restaurants.

Keywords:Food Waste Reduction, Machine Learning, Sustainable Food Systems, Predictive Analytics, Supply Chain Optimization, Demand Forecasting, Environmental Sustainability, Resource Efficiency, Inventory Management, Food Supply Chain, Data-Driven Solutions, Waste Minimization, Real-Time Data Analysis, Cost Efficiency, AI in Food Management, Food Loss Prevention, Smart Waste Management, Waste Prediction Model, Sustainable Practices, Consumption Patterns Analysis