SurplusShift: A Technology-Driven Approach for Efficient Surplus Food Redistribution and Food Waste Minimization

Abstract

Food waste has become one of the most pressing sustainability issues not in just India but worldwide. In India alone, households generate an estimated 78.2 million tons of food waste every year [1]. This happens alongside widespread food insecurity, highlighting a serious disconnect between surplus food and those who need it most. There is no reliable system exist, who can coordinate donation, verify food safety and redistribute surplus food efficiently. This paper presenting a digital platform designed to address these gaps with enabling structured redistribution of surplus food. The proposed system combines the power of machine learning models, GPS enabled logistics, add coordinated workflow, connecting donor\'s NGO\'s and volunteers effectively [12]. By automating food quality verification, the platform improves trust and ensures that recovered food is both safe and delivered on time. Inedible food is redirected to biogas facilities, supporting circular economy practices and minimizing environmental impact [2]. The platform is implemented using a Flutter-based web application, a Django backend, ML microservices, and MongoDB for data storage, resulting in a scalable and flexible architecture. Testing results indicates low coordination latency improved operational efficiency and strong potential for significantly reduce household, as well as some part of industrial food waste.

Introduction

The text discusses the global issue of food waste, highlighting the contradiction between large-scale food surplus and widespread hunger. In India alone, about 78.2 million tonnes of food are wasted annually, contributing to environmental pollution through methane emissions in landfills. Existing food redistribution systems are inefficient, relying on manual coordination, phone calls, and informal communication, which often leads to delays, poor tracking, and safety concerns due to the lack of proper food quality assessment.

To address these problems, the proposed solution introduces a smart, AI-driven online platform that connects food donors (households, restaurants, and event organizers) with NGOs, volunteers, street animal feeders, and biogas plants. The system integrates machine learning for food image verification and classification, GPS-based routing for efficient pickup and delivery, and real-time communication through a centralized network. It also ensures that inedible food is redirected to biogas plants, supporting a circular economy approach.

The platform is built using a Flutter frontend, Django backend, MongoDB database, and ML microservices. Donors upload food details and images, which are validated by a CNN-based model (ResNet-50) trained to detect whether the image contains food (around 92% accuracy). Another model classifies food type to determine usability and routing. Based on this, food is either allocated to humans, animals, or energy recovery systems.

The system’s objectives include reducing food waste, improving donation efficiency, ensuring food safety through automated checks, and enabling proper disposal of inedible food through biogas conversion.

Conclusion

This project shows that one shared platform can handle the full journey of surplus food instead of leaving each step to separate, manual arrangements. In our design, donors, NGOs, volunteers, and biogas?plant partners all use role?based screens on the same system, with data stored in a common backend. Basic checks using machine?learning models and location data help filter out poor?quality entries [12] and guide each batch of food toward people, animals, or biogas, instead of letting it quietly slip back into the waste stream [16].

References

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Copyright

Copyright © 2026 Onkar Sarambale, Satish Yedage, Saurabh Magar, Shubham Rode, Shivam Misal. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.