🌍 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!