Real-Time Environmental Monitoring and Nutrient Estimation in Controlled Nursery Environments for Precision Agriculture

Abstract

Modern nursery management faces significant challenges in maintaining optimal micro-climatic conditions and managing soil health efficiently. This research presents an automated, Internet of Things (IoT)-enabled Wireless Sensor Network (WSN) designed for real-time monitoring and autonomous control in nursery farming. Utilizing an ESP32 microcontroller, the system integrates a DHT11 sensor for atmospheric thermohydrometric data, a Light Dependent Resistor (LDR) for luminosity tracking, and soil moisture sensors for irrigation management. A unique aspect of this study is the development of a mathematical heuristic to estimate Nitrogen (N), Phosphorus (P), and Potassium (K) levels derived from soil conductivity and moisture trends, providing a low-cost alternative to expensive spectrophotometric sensors. The system architecture employs a local control loop for immediate actuator response activating ventilation fans during high-temperature excursions ($>40°C$) and submersible pumps during moisture deficits while simultaneously transmitting data to a remote PHPbased cloud server via HTTP protocols. Experimental results indicate that the system significantly reduces manual labor while preventing plant stress through precise threshold-based interventions. The data logged reveals a high correlation between automated irrigation and the stabilization of estimated soil nutrient leaching. This research concludes that the proposed low-cost WSN framework offers a scalable solution for small-to-medium-scale nurseries in developing regions, optimizing resource utilization and promoting sustainable agricultural practices through data-driven precision.