About This Architecture

AI IoT Plant Health Monitoring System integrates field sensors, edge computing, and machine learning to detect crop diseases in real time. Soil moisture, temperature, and humidity sensors feed data through an ESP32 microcontroller and MQTT broker to an AI disease detection model that analyzes images from ESP32-CAM units. The system stores sensor telemetry and images in cloud object storage, runs ETL pipelines for model training, and surfaces actionable health alerts and dashboards to farmers via web and mobile interfaces. This architecture demonstrates how edge-to-cloud IoT pipelines enable precision agriculture at scale, reducing crop loss and optimizing resource use. Fork this diagram on Diagrams.so to customize sensor types, add additional field zones, or integrate with your farm management platform.

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How do I build an IoT system to detect crop diseases in real time using sensors and AI?

This diagram shows a complete architecture: soil moisture, temperature, and camera sensors connect to an ESP32 microcontroller, which routes data via MQTT to a cloud AI disease detection model. The model analyzes images and sensor readings, stores results in a database, and sends alerts and dashboards to farmers via web and mobile apps.