GreenIQ

GreenIQ empowers daily climate action by managing waste and directly linking users with recycling and collection agents.

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Inspiration

Our inspiration came from a clear gap between climate awareness and real climate action, especially in developing countries. While climate change discussions often focus on large-scale policies, everyday actions like waste disposal remain poorly supported—even though they have a direct impact on carbon emissions (CO_2).

From our own communities, we observed that people often want to recycle but lack clear information, practical guidance, and access to functional collection systems. Even when waste is sorted, it frequently ends up in landfills because there is no direct connection between individuals and recycling agents. This motivated us to build GreenIQ — a platform that transforms environmental awareness into immediate, real-world action.

What it does

GreenIQ is an AI-powered mobile application that helps users manage waste and take meaningful climate action.

The app works in two core ways (among many other features):

1. Waste scanning

Users scan waste items, which are classified using our own pretrained AI model as biodegradable or non-biodegradable. The app then provides clear disposal tips and automatically contacts nearby waste collection companies that handle that specific waste type.

2. Product barcode scanning

Users scan the barcode of food and beverage products. The app fetches product information from Open Food Facts and then uses DeepSeek AI to analyze this data and generate:

  • Eco-friendliness insights
  • Disposal recommendations
  • Sustainability guidance in an easy-to-understand format

Through these core features, GreenIQ ensures that both knowledge and action are delivered in one seamless experience.

How we built it

GreenIQ was built using a mobile-first, scalable architecture.

Frontend

  • React Native — cross-platform mobile development

Backend

  • Node.js — handles authentication, user data, ecoPoints, and company notifications

AI & Intelligence

  • Custom pretrained waste classification model for identifying biodegradable vs non-biodegradable waste
  • DeepSeek AI for analyzing product data retrieved from Open Food Facts and generating disposal and eco-friendliness insights

APIs & Data Sources

  • Open Food Facts API — product information via barcode
  • Location services — to identify nearby waste collection companies

Database & Deployment

  • MongoDB — data storage
  • Render — backend hosting and deployment

The system connects digital scanning directly to real-world waste collection, creating a functional recycling supply chain.

# Example logic flow (simplified)
scan_item
→ classify_waste
→ generate_disposal_tips
→ notify_nearby_company

Challenges we ran into

One of the main challenges was training and deploying our own waste classification model with limited localized datasets. Ensuring reliable classification across different waste types and environments required careful tuning.

We also faced challenges in:

  • Integrating DeepSeek AI with real-time product data from Open Food Facts
  • Automatically matching waste types to the correct nearby collection companies
  • Keeping the user experience simple while managing complex AI and backend workflows

Accomplishments that we're proud of

We are proud that:

  • We successfully trained and deployed a custom waste classification model
  • We integrated DeepSeek AI to transform raw product data into actionable sustainability insights
  • Waste scanning triggers automatic contact with nearby collection companies
  • The app bridges the gap between digital AI systems and real-world recycling infrastructure
  • GreenIQ aligns strongly with SDG 13 (Climate Action) and circular economy principles

What we learned

Through building GreenIQ, we learned that effective climate solutions must be practical, localized, and action-oriented.

Key lessons include:

  • AI is most impactful when paired with real-world execution
  • Clear guidance and automation increase participation in recycling
  • Combining custom AI models with large knowledge models like DeepSeek creates more powerful systems
  • Small daily actions can scale into meaningful climate impact

Mathematically, impact scales as: $$ Impact = Users \times Daily\ Actions $$

What's next for GreenIQ

Next, we plan to:

  • Improve our pretrained waste classification model with more localized data
  • Expand DeepSeek AI capabilities for deeper sustainability insights
  • Onboard more recycling companies and waste collection agents
  • Add personalized eco-missions and community challenges
  • Pilot GreenIQ in schools and local communities

Our long-term vision is to scale GreenIQ into a city-wide climate action platform, enabling individuals to collectively build a more sustainable future.

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