A comprehensive technical review of IoT-based soil moisture sensing and ML integration for precision agriculture
📌 Project Overview This research investigates the integration of IoT platforms with real-time soil moisture sensors (Capacitive, TDR, FDR) to transition agriculture from manual scheduling to data-driven precision irrigation.
🚀 Key Technical Insights
- Sensor Analysis: Evaluated low-cost capacitive sensors for drip irrigation, noting a potential 29% reduction in water usage. * ML Integration: Investigated the use of Random Forests and Neural Networks for predictive modeling of irrigation triggers. * Architecture: Studied the implementation of MQTT and LoRaWAN protocols for energy-efficient, long-range field communication.
🛠️ Research Focus
- Data Reliability: Analysis of sensor drift and spatial soil variability. * Sustainability: Building agricultural resilience against climate-driven water scarcity. * Innovation: Exploring Digital Twin models for virtual farm simulation.