Design and Development of an ESP32-Based Smart Nursery System for Oil Palm Seedling Cultivation Using IoT Sensors

Abstract

Oil palm seedlings require proper environmental conditions and soil nutrient availability to support optimal growth. Conventional monitoring methods are generally performed manually, making it difficult to obtain real-time information and timely recommendations. This study aims to design and implement an ESP32-based Smart Nursery system utilizing Internet of Things (IoT) technology for monitoring environmental and soil conditions in oil palm seedling cultivation. The system integrates a DHT22 sensor for air temperature measurement, a BH1750 sensor for light intensity monitoring, and an RS485 7-in-1 soil sensor for measuring soil moisture, soil temperature, pH, electrical conductivity (EC), nitrogen (N), phosphorus (P), and potassium (K) levels. Sensor data are processed by the ESP32 microcontroller and transmitted to the Blynk IoT platform for real-time monitoring. A rule-based decision-making approach is implemented to analyze sensor readings and provide recommendations regarding irrigation, shading, soil pH adjustment, and nutrient management. System performance was evaluated using three testing conditions: dry soil, moist soil, and fertilized soil. The results demonstrate that the developed system successfully monitored environmental and soil parameters and generated recommendations according to predefined rules. The Smart Nursery system can support efficient monitoring and decision-making processes in oil palm seedling management through real-time IoT-based technology.