VeggieWaste

Unsure about your vegetables or fruits freshness? This app allows you to get a second opinion on the state of your veggies! I'm a high school student who built this project for the 2026 Hack For Humanity Competition. This project uses MobileNetV3 as a base model, then further trained to recognize the mold on vegetables and fruits. Using ONNX to export the model into a C# backend for quicker loading and access times.

The Problem

Food waste is a major global issue as a whole humanity throws out 1+ Billion tons of edible food each year. As food is thrown out just because it looks questionable (e.g bruising or looks cheap) this leads to:

Unnecessary food waste
Increased environment impact
Higher grocery costs

The Solution

VeggieWaste uses machine learning to analyze images of vegetables to determine whether they are fresh or rotten, helping users make educated decisions.

Features

Image upload via browser
Freshness classification via AI
Confidence scoring for transparency
ONNX model inference in the backend

Tech Stack

Frontend

React + Typescript
Vite
TailwindCSS
Axios

Backend

ASP.NET Core Web API
ONNX runtime
Imagesharp

Machine Learning

MobileNetV3 Convolutional Neural Network
Light weight and high accuracy-to-performance ratio
Exported to ONNX for cross-platform inference
Real-time image classification

How It Works

The user sends an image through the web interface, where it is then sent to the backend.
The backend receives the image then:
- Preprocesses the image
- Runs inference using MobileNetV3-based ONNX model that was trained using 10,000+ images.
The API returns the classification and confidence score.
The frontend displays the result instantly.