This is a submission for Weekend Challenge: Earth Day Edition
What I Built
Every day, millions of gardeners spray pesticides on insects they don't recognize — unknowingly killing the very creatures that keep their gardens alive. Ladybugs, lacewings, ground beetles, and dozens of other beneficial insects die silently because people can't tell the difference between a garden ally and a garden pest.
EarthBug changes that. Point your phone at any insect, tap a button, and within seconds you'll know exactly what it is, what role it plays in your local ecosystem, and whether you should protect it or remove it — with full ecological context, not just a label.
🌍 Earth Day Angle: Pesticide overuse is one of the leading drivers of pollinator collapse and soil degradation. EarthBug gives everyday people the knowledge to make smarter decisions — reducing chemical runoff and protecting biodiversity one scan at a time.
Demo
Watch the demo - https://youtu.be/pLOUmKnfX-Y
try it - https://earthbug.vercel.app/?demo=true
Code
Review the code here - https://github.com/divyabairavarasu/earthbug.git
How I Built It
I'll be honest: this app would have taken me three times as long without AI assistance. Here's how Google Gemini and GitHub Copilot each played a distinct, essential role.
Google Gemini AI — The Brain
Gemini is doing the heavy lifting here. When a user captures or uploads a photo, the image goes through a client-side compression pipeline (to optimize the API payload), then is sent to Gemini's vision model with a carefully engineered prompt that requests a structured JSON response.
What Gemini returns for every scan:
Common name and scientific classification
Ecological role — what this insect actually does in its habitat
Benefits and harms — nuanced, not black-and-white
A contextual verdict: Mostly Helpful / Mostly Harmful / Context-Dependent / Neutral Visitor
Eco-actions — specific things the user can do to help or manage this species
Confidence level — so users know how certain the identification is
What makes this integration more than a basic API call:
Structured JSON output with schema validation — Gemini returns a typed object, not free text
Safety filter handling — graceful degradation when images don't contain insects
Multi-turn conversations — users can ask follow-up questions like "How do I attract more of these?"
Error recovery with retry logic — transient API failures don't crash the experience
Image compression pipeline — canvas-based resize before the API call keeps costs low and latency tight
Gemini doesn't just tell you what the bug is — it tells you what it means for your garden and what you should do about it. That's the difference between trivia and actionable intelligence.
GitHub Copilot — The Development Partner
GitHub Copilot was present at every stage of development — not as an autocomplete tool, but as a genuine pair programmer. Here's where it made the biggest difference:
Prompt Engineering: Copilot helped me iterate on the Gemini system prompt, suggesting structured output schemas and edge case handling I hadn't considered
React Architecture: The state machine in App.jsx — managing the API_KEY → CAMERA → ANALYZING → RESULTS flow — was co-designed with Copilot suggestions
E2E Test Coverage: The entire Playwright test suite was built with Copilot, including the Gemini route interceptors that let tests run without real API calls
Image Compression: The canvas-based compression pipeline that handles GIF/SVG edge cases came from a Copilot suggestion I wouldn't have reached on my own
Accessibility: Copilot flagged missing alt text and duplicate ARIA roles that I'd overlooked — the kind of detail that matters but is easy to miss under deadline pressure
The honest reflection: AI-assisted development isn't about writing less code. It's about writing better code faster — catching blind spots, exploring options you wouldn't have considered, and keeping momentum when you're stuck.
Prize Categories
Best Use of Google Gemini API · Earth Day Impact · Open Source · Mobile Experience
Team-members:@divya_bairavarasu_93ffe55
I started this project because I genuinely didn't know if the spider living in my garden was a friend or a problem. Now I know it's a garden ally. I hope EarthBug helps a lot of people discover the same thing about the creatures sharing their space.
If you've made it this far — go outside, find a bug, and scan it. You might be surprised who your neighbors are.Thank you for the opportunity.
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