FloraTrace: Offline Trail Botanist & Plant Identifier for Offline Hikes

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

What I Built

I built FloraTrace to solve a problem my friends and I face every time we go hiking on trails around the Western Ghats: we see incredible wild flora, native trees, and medicinal plants, but we have zero cellular network signal to search for what they are.

FloraTrace is a lightweight, offline-first mobile web app designed to get people exploring outdoors without staring at screens:

• Snap & Go: You take a photo of a leaf, bark, or wildflower while on a trail.
• On-Device Offline Plant Identification: It runs a lightweight open-weight vision model directly in the browser/locally to identify native flora species, edible plants, and ecological facts—without needing cellular connectivity or Wi-Fi.
• Screen-Time Minimizer: Instead of endless scrolling or long textual paragraphs, it delivers a 2-sentence audio summary about the plant and logs your location badge so you can lock your phone and get right back to enjoying nature.

I took it on a 6 km weekend trail hike to a testing spot near Savandurga. I snapped 8 native plant species deep in the forest zone with my phone on Airplane Mode. FloraTrace parsed every sample offline in under 2 seconds and read out the species name via voice, letting me keep moving without eyes glued to a screen.

How I Built It

The core philosophy behind FloraTrace was making the tech entirely self-contained so it works deep in the woods without paid APIs or cloud dependencies.

   +-------------------------------------------------------+
   |             Hiker / User on Outdoor Trail             |
   |                (Captures Plant Snapshot)              |
   +---------------------------+---------------------------+
                               |
                               v
   +-------------------------------------------------------+
   |               FastAPI + Web Application               |
   |                (Hosted on Render / Local)             |
   +-------+-------------------+-------------------+-------+
           |                   |                   |
           v                   v                   v
  +-----------------+  +---------------+  +-------------------+
  | Gemma 2 /       |  | Web Speech /  |  |  SQLite /         |
  | PaliGemma Nano  |  | Edge TTS      |  | Local Trail       |
  | Vision Parser   |  | Audio Engine  |  | Field Database    |
  +-----------------+  +---------------+  +-------------------+

Technical Stack:

• Vision Intelligence (PaliGemma / Gemma 2 Vision via Ollama): Analyzes botanical photos to extract species names, native status, and safety notes.
• Audio Output Engine (edge-tts / Web Speech API): Converts plant facts into spoken audio clips so hikers can listen while walking without looking at their phones.
• Local Storage & Database (SQLite / Browser Cache): Caches offline field guides, regional flora maps, and trail logbooks directly on device.
• Hosting & Telemetry (Render & Sentry): Serves the web interface and handles background sync when back in network coverage.

Why Does Open Innovation Matter?

1. True Offline Independence: Cloud-based proprietary APIs fail the moment you step onto a mountain trail without cell towers. Open-weight vision models allow real AI reasoning on local hardware deep in nature.
2. Zero Operating Expense: Running open-weight models locally or self-hosting on lightweight servers means zero per-query cost, keeping the app completely free for outdoor communities.
3. Privacy First in the Wild: Location tags and personal outdoor activity logs stay strictly on your local device rather than being sold or harvested by central ad networks.

Prize Categories

• Best Use of Gemma: Uses Gemma 2 / PaliGemma open vision models for offline plant identification and instant field guide summaries.
• Best Use of Render: Hosts the web frontend application, service workers, and backend sync pipelines on Render.
• Best Use of Sentry Agent Tracing: Tracks local model inference latency and offline tool invocation performance.