IoT Tomato Plant Disease Monitoring & Early Detection
Edge IoT environmental telemetry station and deep learning mobile application for real-time microclimate sensing and automated crop disease diagnosis.
1. Context & Problem Statement
The system combines an IoT environmental monitoring station with a mobile deep learning computer vision model to diagnose plant diseases before visible crop failure occurs.
2. System Architecture & Workflow
[Greenhouse Sensor Array] (DHT22, Soil Moisture, LDR)
│
▼
[ESP32 Edge Microcontroller]
│ (HTTP)
▼
[Cloud Backend & Database]
│
▼
[Android Mobile Application]
├─ Real-Time Sensor Telemetry
├─ On-Device TFLite Leaf Disease Classifier
└─ Microclimate Risk Warning System
3. Technical Highlights & Automation
• Autonomous Sensing: Node wakes every 15 minutes, samples 4 sensors, transmits JSON payload, and enters 15μA deep sleep. • Predictive Alarm: Backend calculates VPD (Vapor Pressure Deficit) and alerts farmer when conditions are optimal for fungal spore germination.
4. Measurable Results & Impact
Demonstrated the practical convergence of embedded electronics, IoT connectivity, and applied machine learning in precision agriculture.
Engineering Retrospective
Edge AI and low-power firmware design require rigorous optimization of memory footprints and compute cycles.
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