Play 78

Precision Agriculture Agent

Very High✅ Ready

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

Azure IoT HubAzure AI VisionAzure OpenAIAzure Digital TwinsAzure Machine Learning

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

Sensor sampling rateSatellite imagery frequencyIrrigation thresholdsFertilizer dosage modelsYield prediction horizon

Estimated Cost

Dev/Test

$150–350/mo

Production

$5K–15K/mo

User Guide Open in VS Code View on GitHub Setup Guide Configurator Ask Agent FAI Back to FrootAI