Design and Implementation of an Internet of Things Based Precision Agriculture System for Maize Growth

Student: Divine Ifeoluwa Awe (Project, 2025)
Department of Computer Engineering
Ekiti State University, Ado-Ekiti, Ekiti State

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.

Keywords
Precision agriculture IoT farming smart irrigation maize cultivation soil monitoring sustainable agriculture cloud analytics.
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