About This Architecture

AI-powered plant health monitoring system combining IoT sensors, edge processing, and AWS serverless architecture to detect crop diseases in real time. Soil moisture and temperature sensors on two plant sections feed ESP32-CAM devices through Wi-Fi to an AWS VPC with API Gateway, WAF, and CloudFront protecting the ingestion layer. Lambda functions orchestrate data ingest, AI inference, and alert generation, storing sensor readings in DynamoDB, camera images in S3, and analysis results in RDS for farmer dashboards. This architecture demonstrates secure, scalable IoT-to-cloud patterns with separation of concerns across public and private subnets, enabling farmers to identify unhealthy crops before yield loss. Fork and customize this diagram on Diagrams.so to adapt sensor types, add additional plant sections, or integrate alternative ML inference engines. The multi-Lambda pipeline design allows independent scaling of ingestion, inference, and alerting workloads based on farm size and sensor density.

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How do I build an IoT plant health monitoring system that sends sensor data to AWS and runs AI inference to detect crop diseases?

This diagram shows a complete architecture where soil moisture and temperature sensors (DHT11, Soil Moisture Sensor) connect via ESP32-CAM through Wi-Fi to AWS. API Gateway and WAF protect the ingestion layer, Lambda functions handle data ingest and AI inference, while DynamoDB stores readings, S3 stores camera images, and RDS stores analysis results for farmer dashboards.