About This Architecture

AI-powered IoT plant health monitoring system integrating DHT22 atmospheric sensors, soil moisture probes, and ESP32-CAM image capture across healthy and unhealthy crop sections. Data flows from edge sensors through an ESP32 microcontroller via Wi-Fi/MQTT to a cloud API endpoint, where an ML disease detection model processes images and sensor telemetry against a plant disease model registry. Processed insights stream to Blynk IoT dashboards (mobile and web) and monitoring logs, enabling real-time crop health alerts and intervention decisions. Fork this architecture to customize sensor types, swap cloud providers, or integrate alternative ML frameworks for precision agriculture deployments. The dual-section design (healthy vs. unhealthy) demonstrates comparative analysis for training and validating disease detection models in production environments.

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How do I build an IoT system to monitor crop health and detect plant diseases in real time?

This diagram shows a complete five-tier architecture: physical sensors (DHT22, soil moisture, ESP32-CAM) feed data via MQTT to a cloud ML model that detects diseases and logs alerts to Blynk dashboards. The dual-section design (healthy vs. unhealthy crops) enables comparative analysis for model training and validation.