🌿 WildLens AI: Open-Source AI That Gets You Outside
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass.
What I Built
WildLens AI is an AI-powered nature exploration application designed to turn everyday outdoor walks into opportunities for discovery.
Have you ever spotted an interesting flower, plant, or unfamiliar species while walking outside and wondered what it was?
WildLens AI helps users explore that curiosity by allowing them to capture or upload an image and discover possible species matches, including scientific names and relevant information.
The project aims to make nature exploration more interactive and accessible for students, nature enthusiasts, hikers, gardeners, and anyone curious about the biodiversity around them.
🌱 The idea is simple: use AI as a reason to step away from the screen, explore the outdoors, and learn something about the natural world.
Beyond identification, WildLens AI includes features such as Field Missions and a Field Journal to encourage users to observe, discover, and document their experiences.
Demo
🌐 Live Website: WildLens AI
🎥 Video Demo: Add your published video link here.
The frontend is deployed on GitHub Pages. Production AI identification depends on a separately hosted and correctly configured backend.
Code
💻 GitHub Repository: WildLens AI
The project is being developed with an emphasis on open-source AI, modular architecture, and a practical nature-exploration experience.
Contributions, feedback, bug reports, and ideas for improving the project are welcome!
How I Built It
WildLens AI combines a web application, an AI-powered identification pipeline, and nature-focused exploration features.
Open-Source AI
The core identification model is BioCLIP-2, an open-weight vision-language model developed for biological imagery.
Using the open_clip library, the identification pipeline encodes an image and compares its visual representation against text representations of candidate species. Similarity scores help rank possible matches.
These scores are not calibrated probabilities, and the highest-ranked candidate is not necessarily correct. Reliable identification requires appropriate candidate coverage, evaluation, and honest communication of uncertainty.
Application Architecture
The project combines:
- Frontend: A web interface for image-based exploration and nature-related features.
- Backend: Python and FastAPI for server-side functionality and AI inference.
- AI model: BioCLIP-2 for image–text similarity and candidate ranking.
- Knowledge base: Curated taxonomy information to support species-related information.
- Nature exploration: Field Missions and a Field Journal to encourage observation beyond the screen.
The frontend and backend have separate responsibilities, allowing the user interface and inference pipeline to be maintained independently.
GitHub Pages serves the static frontend; live AI identification requires a separately reachable backend.
Why Does Open Innovation Matter?
Biodiversity is enormous, and access to biological knowledge should not be limited to researchers with expensive tools or proprietary AI services.
Open innovation makes projects like WildLens AI possible through open-weight models, reusable libraries, public model documentation, and community-driven development.
Using BioCLIP-2 provides a foundation for experimenting with biological image–text matching without building a vision model from scratch.
Open-source development also makes the project easier for others to inspect, learn from, test, improve, and extend. Contributors can help improve the taxonomy knowledge base, evaluation datasets, user experience, performance, and support for additional species.
For me, open innovation is not just about making code available. It is about making experimentation, learning, and collaboration more accessible.
Prize Categories
Include a partner category here only if your project meets its official eligibility requirements.
Prize Category
Render — Best Use of Render ($200)
WildLens AI uses a Python FastAPI backend to support AI-powered nature identification and educational features. I plan to use Render to deploy and host the backend API, connecting it to the GitHub Pages frontend. The application combines BioCLIP-2 for biological image identification with Gemini for online nature explanations and question answering.
The goal is to build a reliable, accessible nature exploration platform with clear online/offline functionality and a smooth user experience.
The goal is not to spend more time staring at a screen. It is to use technology to notice the world around us, identify something unfamiliar, and become curious enough to go outside again.
Identify something. Learn something. Look up. Touch grass. 🌿
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