🌍 GreenLens — AI That Tells You Exactly Where to Plant Trees

What I Built

I built GreenLens — an AI-powered urban greening intelligence platform that turns any location into real, actionable environmental decisions.

Instead of just showing air quality or climate metrics, GreenLens answers:

“Given this exact place — what should we do to make it greener and healthier?”

🌳 The Breakthrough: From Data → Action

Most tools stop at:

AQI numbers
dashboards
reports

GreenLens goes further.

It pinpoints exactly where trees should be planted and whether a dense plantation is feasible.

🌿 Smart Plantation Engine

Using location + environmental signals, the app:

📍 analyzes a real-world location
🌫️ evaluates air quality
🌱 calculates tree deficit (green gap)
🌳 identifies plantation zones
🧠 generates AI-powered recommendations
🌲 Miyawaki Micro-Forest Detection

GreenLens specifically identifies where high-impact micro-forests can be planted using the
Miyawaki method

This allows:

dense forests in small spaces
faster urban greening
measurable environmental improvement
💡 Why This Matters

This transforms the app from:

❌ a data dashboard
➡️ into
✅ a real-world environmental decision engine

🚀 Vision: From Insights → Real Impact

GreenLens is designed to go beyond analysis.

The next phase turns insights into execution:

🌍 Community Tree Tagging & Tracking
users can tag planted trees
upload real images
track growth over time
build local environmental communities

👉 Think: GitHub for trees 🌱

💰 Crowdfunding Greening Projects
launch plantation campaigns
raise funds for specific locations
track progress transparently

👉 Example:
“500 trees needed here → fund → plant → track impact”

🔥 Why This Is Powerful

This connects:

AI → Community → Funding → Execution → Impact

Demo

👉 🎥 Watch Full Demo Video:
Watch Video

👉 🌐 Live App:
http://104.207.64.25/earth-health/

👉 🧪 Try Assessment Flow:
http://104.207.64.25/earth-health/assess

🏗️ How I Built It
⚙️ Tech Stack
Laravel + Blade → backend + UI
Bootstrap → responsive design
Google Maps API → location + coordinates
Google Air Quality API → environmental data
Google Gemini → AI reasoning engine
SQLite → lightweight storage
Nginx + PHP-FPM → deployment
🔄 Workflow
User selects a location
App retrieves environmental data
System calculates tree opportunity
AI generates recommendations
Dashboard shows insights
🤖 Why Google Gemini Matters

Instead of acting like a chatbot, Gemini is used as an intelligence layer.

It translates raw data into:

actionable insights
clear recommendations
human-readable explanations
🧩 Core Principle

Data provides facts. AI provides direction.

🎨 Product Thinking

I focused on making this feel like a real SaaS product, not just a demo:

clean dashboard UI
structured output
simple workflow
stakeholder-friendly insights
⚡ Deployment Insight

The app runs as a subfolder Laravel app:

/earth-health/

This required handling:

routing adjustments
base path issues
nginx configuration
💰 Call for Collaboration / Funding

I’m actively looking to take GreenLens further.

If you’re interested in:

🌍 environmental impact
🏙️ urban planning
💻 building scalable tools
💰 funding climate tech

👉 Let’s connect.

Prize Categories
🥇 Best Use of Google Gemini

GreenLens uses Gemini as a core part of the product:

interpreting environmental data
generating actionable insights
enabling decision-making

This is not a chatbot — it’s an AI-powered reasoning engine.

Final Thoughts

AI should not just show information.
It should help us fix real-world problems.

GreenLens bridges:

data → decisions → action → impact

💬 Feedback welcome:
How would you use this in your city?

⭐ If you like it, consider starring the repo!