DESIGN AND IMPLEMENTATION OF AN INTERNET OF THINGS BASED PRECISION AGRICULTURE SYSTEM FOR MAIZE GROWTH
Abstract
ABSTRACT This study developed an IoT-based precision agriculture system to optimize maize cultivation by addressing key challenges of inefficient resource use and low yields. The system integrates soil moisture (SM150T), NPK nutrient (JXCT-IoT), and environmental sensors (DHT22, DS18B20) with ESP32/ESP8266 microcontrollers, transmitting real-time data via Wi-Fi/ESP-NOW to a Firebase cloud platform for analysis and decision support. Field tests demonstrated a 30% reduction in water usage, 20-25% yield improvement, and reliable monitoring with ±3% soil moisture accuracy, powered autonomously by a 200W solar system. Results validate the system's effectiveness in enhancing maize productivity through data-driven irrigation and fertilization, while recommendations include integrating machine learning for predictive analytics, developing self-calibrating sensors, and optimizing costs for smallholder adoption. The study contributes to sustainable agriculture by combining IoT technology with precision farming principles to improve resource efficiency and crop yields in maize cultivation. Keywords: Precision agriculture, IoT farming, smart irrigation, maize cultivation, soil monitoring, sustainable agriculture, cloud analytics.
Full-Text Access Notice
In accordance with the NERD Policy on promoting peer-reviewed publication, public access to the full text of a project, thesis or dissertation is restricted for three years, allowing the author and supervisors sufficient time to pursue peer-reviewed publication.
During this period, researchers with legitimate academic or research purposes may request authorisation directly from the author to enable NERD to release the indexed full texts of the work using the form below.