LifeLine Loop — AI-Powered Food Rescue Platform

Problem We Solved Every year, millions of tons of perfectly edible food are wasted across India from restaurants, weddings, hostels, corporate cafeterias, and households. At the same time, countless people struggle to access nutritious meals. One of the biggest challenges in food redistribution is the lack of a fast and intelligent system that can identify food, estimate quantity, and prioritize rescue operations before the food becomes unsafe to consume. Most donation platforms rely on manual entry and human judgment, which often leads to delays, inaccurate quantity estimates, and food spoilage. We wanted to build a smarter solution that reduces decision-making time and helps NGOs act quickly. Our Solution LifeLine Loop is an AI-powered food redistribution platform that connects food donors with NGOs and volunteer organizations. Instead of manually describing donated food, donors can simply upload a photo and provide a few basic details. Our machine learning system automatically analyzes the donation, estimates how many people can be served, and predicts how urgently the food needs to be collected. This enables NGOs to prioritize pickups efficiently and reduce food waste. The goal is simple: rescue more food, serve more people, and make the donation process effortless. Key Features AI Food Recognition Donors upload an image of the available food. Our Food Recognition model automatically classifies the food category using computer vision. Examples: Rice and Biryani Vegetables Desserts Bread-based foods Snacks This reduces manual effort and helps standardize food records across the platform. Serving Estimation Engine Many donors do not know how many people their food can feed. Our Serving Estimator predicts the approximate number of servings based on: Food category Food weight Container size For example, a donation weighing 20 kg may be estimated to feed around 40–50 people depending on food type. This information helps NGOs plan logistics and distribution more effectively. Expiry Risk Prediction Food donations are highly time-sensitive. Delays can result in food becoming unsafe for consumption. Our Expiry Risk Predictor analyzes: Food category Remaining safe consumption hours Storage temperature The model assigns: High Priority Medium Priority Low Priority This allows NGOs to focus on urgent pickups first and maximize successful food recovery. Machine Learning Models 1. Food Recognition Model Model: MobileNetV2 (Transfer Learning) Why MobileNetV2? Lightweight and fast Optimized for deployment High accuracy with limited resources Suitable for real-time prediction The model was fine-tuned using food image datasets and integrated into our backend API. 2. Serving Estimator Model: Random Forest Regressor Inputs: Food type Weight Container size Output: Estimated number of servings Performance: R² Score: 0.976 The model helps generate practical estimates for NGOs without requiring manual calculations. 3. Expiry Risk Predictor Model: Random Forest Classifier Inputs: Food category Hours remaining Storage temperature Output: High / Medium / Low Risk Performance: Accuracy: 98% The model provides a simple but effective priority system for food rescue operations. How We Built It Our backend was developed using FastAPI, providing high-speed API endpoints for machine learning predictions. The workflow is: Donor uploads food image. Food Recognition model identifies category. Serving Estimator predicts meal count. Expiry Predictor calculates urgency level. Results are returned instantly to the platform. NGOs can prioritize collection based on AI recommendations. The system was designed to be modular, allowing future integration with mobile applications and NGO dashboards. Technology Stack Machine Learning TensorFlow Scikit-learn MobileNetV2 Random Forest Backend Python FastAPI Deployment Render Cloud Platform Development Tools Git GitHub Challenges We Faced One of the biggest challenges was creating meaningful predictions using limited dataset