AgriTech Infrastructure Challenges
Challenges
- Farms have poor or no internet connectivity in remote areas
- Satellite and drone imagery creates terabytes of unstructured data
- Soil, weather, and crop sensor data needs real-time processing
- ML models must make planting and irrigation decisions within minutes
- Seasonal compute bursts — harvest and planting windows
- Data sovereignty: farmer data ownership and privacy regulations
CloudFormation Solves
- AWS Greengrass edge compute works offline and syncs when connected
- S3 + SageMaker Ground Truth for satellite imagery labelling pipelines
- IoT Core + Timestream for sensor data ingest and time-series queries
- SageMaker for crop yield prediction and disease detection ML models
- Spot Fleet for seasonal compute — train models only during peak windows
- Lake Formation for farmer data governance and consent management
Precision Agriculture Architecture
Farm Edge Gateway
Raspberry Pi / industrial PC with AWS Greengrass. Collects soil sensors (NPK, moisture, pH), weather station data, and drone telemetry locally. Runs ML inference offline. Syncs to cloud when connected.
Satellite & Drone Imagery Pipeline
S3 → Lambda trigger → SageMaker Processing Job (NDVI calculation, crop health classification) → results stored in RDS PostGIS for spatial querying. GIS dashboards via QuickSight.
Yield Prediction Models
Historical yield + soil + weather + satellite data → SageMaker XGBoost training pipeline → deployed to edge and cloud endpoints. Irrigation and fertilisation recommendations generated per field.
Farm Management Platform
API Gateway → Lambda → Aurora PostgreSQL (farm records, operations, compliance). Mobile app (offline-first) syncs via AppSync. Supply chain traceability via IoT events on blockchain.
Business Outcomes
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