Inspiration

Food wastage is a global issue, about 1.3 billion tonnes of food is wasted each year and 28% of the world’s agricultural land is used to produce food that is wasted. In In India, millions survive at subsistence level and tonnes of food is wasted. Our intention to build this project was to help counter starvation as well as food wastage.

What it does

Our project will mainly focus on bridging the two organizations - the Restaurant and the NGO. It is a B2B model. These will be the 2 users. The supplier will put up the quantity of food in hand and the NGO will claim the most suitable choice. The choices NGOs will have, to choose from will be filtered based on location, quantity, age of the actual user. The distribution of the food to the end-user will be the responsibility of the NGO.

How we built it

Tech Stack Used:

  • Tensorflow.Js - Food recognition machine learning model
  • Keras - Food recognition machine learning model
  • Javascript - Website Frontend + ML model
  • NodeJS - Website backend
  • ExpressJS - Website backend
  • MongoDB - Backend Database
  • HTML & CSS - Website Frontend

API Used:

  • Mapbox (Maps API) - Geolocation and Routing
  • Twilio (OTP API) - Authorization Verification
  • Cloudinary (Cloud Storage API) - Storing Images in the cloud and retrieving them from the cloud

Challenges we ran into

Some of the challenges while making this project were:

  1. Integration of the frontend, backend, and the machine learning model
  2. Training our ML model to correctly predict the type of food
  3. Track the orders correctly and authentication using OTP

Accomplishments that we're proud of

We are proud of various accomplishments like:

  1. We have built a complete end-to-end website for connecting the restaurants as well as the NGOs.
  2. Our main aim of reducing food wastage and helping poor people has been fulfilled
  3. Our website is fully authenticated and the UI is very consistent

What we learned

We learned many new things such as:

  1. Integrating Mapbox in our project to correctly track the orders and show the shortest distance possible
  2. How to login 2 different kinds of users into the same website i.e. the restaurant and the NGO
  3. How to authenticate users by sending them an OTP
  4. How to perform CRUD operations

What's next for Zero-Hunger

A recommendation system to recommend Restaurants to the NGO based on previously claimed donations is beneficial in helping us understand more about the NGO. A chatbot system to solve queries or give additional instructions would help too. Acceptable donations could include food ingredients, grains along cooked food to help the needy. Training our ML model to identify more food types and predict approximate quantities of food would be the next step to go for. A large amount of food is wasted during festivities and celebrations, these event organizers can also be connected to the NGOs using our website

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