EcoQuest

A gamified carbon footprint monitoring app leveraging AI

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Inspiration

It all began with a simple question: How much impact can every citizen have on sustainability, and is there a way to measure and collectively raise awareness of our choices? This curiosity ignited the creation of EcoQuest. We believe that as more people opt for sustainable practices, more companies from every industry will eventually pivot their manufacturing and business models toward sustainable choices. This ripple effect has the potential to create a substantial and lasting impact, far beyond what any one person could achieve alone. EcoQuest is not just a platform; it's a catalyst for collective change and a more sustainable future.

What it does

EcoQuest is a mobile app that uses AI to detect, calculate and track users' carbon footprints on a daily basis based on actions such as food consumption, transportation choices, energy usage, trash & recycling choices and so on. Users are rewarded points for more engagement which can then be exchanged for coupons and cashbacks. Users are also incentivized to make more sustainable lifestyle choices with in-app features such as carpooling, a Wiki to figure out if a product is recyclable or not and so on. Furthermore, the platform provides users a sustainability score and offers a leaderboard to incentivize eco-friendly choices as a community.

How we built it

  • Database: We used MySQL 8 for our database and used the necessary indexing
  • Backend: We used Python and Django to build the APIs
  • Firebase: For Authentication (email/password) and Object storage
  • ML: FastAI for machine learning with COCO (Common objects and context dataset)
  • Mobile: Flutter to build the mobile app using Material design

Challenges we ran into

  • Primarily faced a shortage of time. We had a set of features in mind that we wanted to build and we weren't able to build them all because of time restrictions.
  • We also didn't find any free pre-trained models for our specfic use cases (For ex: detecting items in an image of a trash can) so we had to spend a significant amount of time building our own model
  • In the car-pooling feature of our app, we found it hard to identify pickups who are in the direction of the carpool host going from point A to B

Accomplishments that we are proud of

  • Being able to pull of a mature mobile application that slightly surpasses the level of an MVP
  • Spending a good amount of time as a team to figure out the problem and coming up with a feasible solution together instead of being hyperfocused on the tech and coding all the time.
  • Stayed up the whole night on both days

What we learned

  • We learned that even seemingly small choices, such as daily commutes or meal choices, can collectively contribute significantly to carbon emissions. For instance, if a person chooses to eat one meal of beef every day. Then in a month he/she released CO2 emissions equivalent to around 250 miles of electric car drive.
  • Also happened to learn the value of a good night's sleep the hard way.
  • We also understood and naivgational tech problems are not easy tasks to solve.

What's next for EcoQuest

We plan to refine and expand the platform, incorporating user feedback and adding more features like a clothing feature. We aim to partner with more sustainable brands to offer coupons and cashbacks. Our next steps include forging partnerships with state governments and environmental organizations to scale the impact of the platform

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