🌱 EcoPulse — AI Bio-Blitz

Meenakshi — Mon, 12 Oct 2026 05:46:12 +0000

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass

What I Built

I built EcoPulse — AI Bio-Blitz, an AI-powered biodiversity exploration application designed to encourage people to spend more time outdoors and discover the biodiversity around them.

The idea is simple:

Use AI as a reason to go outside, not as a reason to stay on the screen.

EcoPulse allows users to select an exploration environment, difficulty, and type of biodiversity they want to discover. Based on these choices, the application generates five outdoor biodiversity missions.

For example:

🌱 Find a plant with an interesting shape and observe its leaves.
🐝 Look for an insect and observe what it is doing.
🌿 Find two different plants and compare their appearance.
🌸 Look for a small sign of biodiversity that you might normally miss.

Users can then go outdoors, discover something interesting, and upload a photograph of it.

The AI analyzes the image and provides:

🔎 Possible identification
📊 Confidence level
📖 Beginner-friendly explanation
🌿 Biodiversity information

EcoPulse also connects the identified organism to the iNaturalist API for additional biodiversity and taxonomic information.

Users can complete missions and earn BioPoints.

The EcoPulse Loop

Choose exploration preferences
            ↓
AI generates 5 missions
            ↓
Go outside 🌱
            ↓
Find something interesting
            ↓
Take a photo 📸
            ↓
AI analyzes the discovery
            ↓
Possible identification
            ↓
Biodiversity information
            ↓
Complete a mission
            ↓
Earn BioPoints
            ↓
Keep exploring

Demo

🎥 Watch the EcoPulse Demo:

https://youtu.be/TdqCQ9yd_eE

The demo shows the main working flow:

Selecting an exploration environment
Selecting the difficulty
Selecting the biodiversity type
Generating five AI missions
Uploading a discovery photo
Analyzing the discovery
Showing the AI identification and confidence
Retrieving biodiversity information

Code

💻 GitHub Repository:

https://github.com/meenakshi930/ecopulse-ai

How I Built It

I built EcoPulse using Python and Streamlit, with separate components for mission generation, image analysis, and biodiversity data retrieval.

AI Mission Generation

The user selects an environment, difficulty, and biodiversity type. These inputs are used to generate five outdoor biodiversity missions.

The application validates the generated response and includes fallback missions in case of an AI or API failure.

Vision-Based Discovery

Users can upload a photograph of something they discover outdoors.

The vision model analyzes the image and provides:

Possible identification
Confidence level
Beginner-friendly explanation
Biodiversity information

The response is returned in a structured format so that the application can validate and display it reliably.

Biodiversity Information

After getting a possible identification, EcoPulse queries the iNaturalist API to retrieve:

Scientific name
Common name
Taxonomic rank
Observation count

This separates AI-based visual interpretation from external biodiversity data.

Mission Progress

EcoPulse uses Streamlit session state to keep track of:

Generated missions
Completed missions
BioPoints
Discovery results
Biodiversity information

Completing a mission awards BioPoints.

Technology Stack

Technology	Purpose
Python	Main programming language
Streamlit	Web application interface
AI models	Mission generation and image analysis
iNaturalist API	Biodiversity information
Pillow	Image processing
Pydantic	Structured AI response validation
Requests	API communication
python-dotenv	Environment variable management

Why Does Open Innovation Matter?

Open innovation makes it possible for students and independent developers to experiment with AI and build applications around real-world problems.

For EcoPulse, AI is not simply being used as a chatbot. It is part of an outdoor exploration experience.

The project combines AI-based visual understanding with biodiversity data to encourage people to observe and learn about nature around them.

Most importantly, EcoPulse tries to reverse the usual relationship between technology and screen time.

Instead of encouraging people to stay inside and keep scrolling, the application uses technology to encourage them to step outside.

The phone becomes a tool for exploration, not the destination.

What I Learned

Building EcoPulse helped me learn about:

Integrating AI into a Python application
Prompt design
Vision AI
Structured AI responses
JSON and schema validation
REST API integration
Working with external biodiversity data
Streamlit session state
Handling AI and API failures
Building an end-to-end AI application

One important lesson was that AI output should not always be treated as ordinary text.

AI responses are probabilistic, so application logic should validate important outputs before using them.

What I Would Improve Next

EcoPulse is currently an MVP.

One important future improvement would be Mission-to-Photo Verification.