Tree Explorer

Where Tech Meets Nature, One Story at a Time

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🌳 Tree Explorer

What's Tree Explorer?

Tree Explorer is an interactive platform designed to reconnect kids with nature through engaging stories, real-world tasks, and gamified experiences. Inspired by games like Pokémon Go and educational tools like Duolingo, Tree Explorer encourages kids to explore the natural world while learning about trees in a fun and meaningful way.

Why is it needed?

In today's digital age, children spend countless hours glued to their screens, playing games, and browsing apps. This excessive screen time limits their interaction with the real world, especially nature. Tree Explorer addresses this gap by blending the digital and physical worlds, inspiring kids to learn about trees, plant them, and explore the environment while still enjoying a game-like experience.

Inspiration

Tree Explorer was inspired by:

  1. Duolingo's Learning Methodology: Its structured, gamified approach to learning served as a perfect model for making environmental education engaging and interactive.
  2. Pokémon Go's Features: The exploration and outdoor engagement elements influenced the design of real-world tasks and the map-based features.
  3. Personal Experience: Watching my younger brother's addiction to his phone and video games sparked the idea to create a solution that could blend his love for technology with meaningful outdoor activities.

What I Created

I have built the MVP (v1.0) of Tree Explorer as a web app, focusing on the following features:

  1. Interactive Stories: Each story introduces kids to a specific tree in a gamified format.
  2. Real-World Tasks:
    • Capturing a photo of the tree mentioned in the story and earning points based on AI recognition.
  3. Gamification: Points, badges, and a leaderboard to maintain engagement.
  4. User Profiles: Tracks progress and achievements.

How I Did It

The MVP (v1.0) was developed with the following technologies:

  • Frontend:
    • Framework: React.js
    • Styling: CSS and Bootstrap
  • Backend:
    • Framework: Node.js with Express.js
    • Database: mock data was used.
  • AI Integration:
    • Pl@ntNet API: A pre-trained AI model was used for tree image recognition.
    • Everything was vibe-coded with creativity and a focus on rapid execution.

Challenges I Faced

  1. AI Image Recognition:

    • It was challenging to find a suitable pre-trained AI model for tree identification that balanced accuracy and ease of integration.
    • After extensive research, Pl@ntNet API was selected for its reliability and wide database of tree species.

  2. Performance Issues:

    • The leaderboard and story sections initially had slow loading times due to unoptimized database queries.

Next Steps

The current MVP focuses on core features, but here's what's next:

  1. Developing a Mobile App:

    • Transitioning to a mobile-first approach for better usability and portability.
    • Link to App Plan

  2. Enhancing AI Features:

    • Building a custom AI model to identify trees and recognize planted trees through images.
    • Improving recognition accuracy and adding more species to the database.

  3. Integrating New Features:

    • A fully interactive map showing nearby tree locations for exploration.
    • A reward system for completing more difficult tasks, such as planting trees.

  4. Scaling the Platform:

    • Upgrading to a scalable backend to support more users globally.

  5. Long-Term Vision:

    • Incorporate AR (Augmented Reality) to make tasks even more engaging.

Try the MVP

Explore the MVP version of Tree Explorer here: Tree Explorer Web App

Contributors

  • Lingaraj Patil (Developer, Designer, Visionary)

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