🌾 Agriculture · Data Flow
Crop Monitoring Data Pipeline
How satellite and drone imagery plus field sensors become crop health alerts and advice for farmers.
Drawing diagram…
What this diagram shows
- Satellite images are processed into vegetation indices (NDVI)
- Field sensors and weather add ground truth
- Advice is sent in the farmer's language by app and SMS
Prompt used
Data flow for crop monitoring: satellite imagery (Sentinel-2) and drone flights are processed into NDVI and moisture maps per field boundary, combined with soil sensors and weather forecasts in a crop model that detects stress, pest risk and irrigation need. Alerts and advisories are generated in the farmer's language and delivered by app, SMS and IVR calls; agronomists review high-risk cases, and farmer feedback improves the model.
Mermaid code
flowchart LR SAT[Satellite Imagery] --> IMG[Image Processing - NDVI, moisture] DRN[Drone Flights] --> IMG FB[(Field Boundaries)] --> IMG IMG --> CM[Crop Model] SOIL[Soil Sensors] --> CM WX[Weather Forecast] --> CM CM -->|Stress, pest risk, irrigation need| ADV[Advisory Generator] ADV -->|High-risk cases| AGR[Agronomist Review] AGR --> ADV ADV -->|Local language| OUT[App, SMS, IVR call] OUT --> F[Farmers] F -->|Feedback| CM
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