Play 78
Precision Agriculture Agent
Satellite imagery and IoT sensor fusion for crop health monitoring and yield prediction.
Satellite imagery and IoT sensor fusion for crop health monitoring, irrigation scheduling, fertilization optimization, and yield prediction using digital twin simulation of farmland. Azure IoT Hub ingests real-time soil moisture, temperature, and nutrient sensors, AI Vision processes satellite and drone imagery for crop health classification, OpenAI generates agronomic recommendations, Digital Twins creates virtual farmland models for scenario simulation, and Machine Learning trains yield prediction models from historical harvest data.
Architecture Pattern
IoT + Vision + Digital Twin: satellite/sensor fusion, crop analysis, irrigation/fertilization, yield prediction
Azure Services
DevKit (.github Agentic OS)
- agent.md — root orchestrator with builder→reviewer→tuner handoffs
- 3 agents — Agriculture Builder (gpt-4o), Reviewer (gpt-4o-mini), Tuner (gpt-4o-mini)
- 3 skills — deploy (186 lines), evaluate (123 lines), tune (230 lines)
- 4 prompts — /deploy, /test, /review, /evaluate with agent routing
- .vscode/mcp.json — FrootAI MCP with OpenAI + Maps inputs + envFile
TuneKit (AI Config)
- config/openai.json - agronomic recommendation prompts
- config/agriculture.json - crop models, sensor thresholds, irrigation rules
- config/guardrails.json - environmental safety, resource conservation
- evaluation/eval.py - Yield prediction accuracy >85%, Irrigation efficiency >90%
Tuning Parameters
Machine evidence
FrootAI evidence lifecycle
This is an internal evidence maturity label, not third-party certification, accreditation, legal compliance, or a production guarantee. Missing or expired evidence demotes automatically; catalog claims cannot promote a play.
This play currently has design evidence only. A runnable scenario, endpoint evaluation, and build receipts are the next contiguous gates.
Repo Intelligence
v1A no-clone, revision-pinned map for agents and humans. Observed evidence is separated from inferred flow so the output stays useful without pretending to be a full call graph.