Play 80
Biodiversity Monitor
High✅ Ready
AI-powered species identification from camera trap, drone, and acoustic sensor data.
AI-powered species identification from camera trap, drone, and acoustic sensor data with ecosystem health scoring, population trend tracking, and conservation priority alerts. AI Vision classifies species from camera trap and drone photos, OpenAI synthesizes ecological reports from multi-modal observations, IoT Hub connects remote sensor networks in protected areas, Cosmos DB stores species sighting records with geospatial indexing, and Functions trigger conservation alerts when population thresholds are breached.
Architecture Pattern
Multi-modal biodiversity: vision + acoustic classification - population tracking - conservation alerts
Azure Services
Azure AI VisionAzure OpenAIAzure IoT HubAzure Cosmos DBAzure Functions
DevKit (.github Agentic OS)
- agent.md — root orchestrator with builder→reviewer→tuner handoffs
- 3 agents — Biodiversity Builder (gpt-4o), Reviewer (gpt-4o-mini), Tuner (gpt-4o-mini)
- 3 skills — deploy (197 lines), evaluate (121 lines), tune (222 lines)
- 4 prompts — /deploy, /test, /review, /evaluate with agent routing
- .vscode/mcp.json — FrootAI MCP with Custom Vision + OpenAI inputs + envFile
TuneKit (AI Config)
- config/openai.json - ecological analysis and synthesis prompts
- config/biodiversity.json - species databases, ecosystem models, alert thresholds
- config/guardrails.json - conservation ethics, data sensitivity
- evaluation/eval.py - Species ID accuracy >90%, Population trend detection >85%
Tuning Parameters
Species confidence thresholdCamera trap scheduleAcoustic sampling frequencyPopulation trend windowConservation alert severity
Estimated Cost
Dev/Test
$80-200/mo
Production
$2K-8K/mo