IoT-Based Smart Irrigation and Fertilization System with Realtime Cloud Integration

Dani Rofianto; Eko Win Kenali; Khusnatul Amaliah; Jaka Fitra; Halim Fathoni; Tiara Kurnia Khoerunnisa; Hevia Purnama Sari

doi:10.22146/jnteti.v15i1.23124

Dani Rofianto Department of Software Engineering Technology, Politeknik Negeri Lampung, Bandar Lampung, Lampung 13535, Indonesia
Eko Win Kenali Department of Software Engineering Technology, Politeknik Negeri Lampung, Bandar Lampung, Lampung 13535, Indonesia
Khusnatul Amaliah Department of Software Engineering Technology, Politeknik Negeri Lampung, Bandar Lampung, Lampung 13535, Indonesia
Jaka Fitra Department of Software Engineering Technology, Politeknik Negeri Lampung, Bandar Lampung, Lampung 13535, Indonesia
Halim Fathoni Department of Informatics Management, Politeknik Negeri Lampung, Bandar Lampung, Lampung 13535, Indonesia
Tiara Kurnia Khoerunnisa Department of Food Technology, Politeknik Negeri Lampung, Bandar Lampung, Lampung 13535, Indonesia
Hevia Purnama Sari Department of Horticulture, Politeknik Negeri Lampung, Bandar Lampung, Lampung 13535, Indonesia

DOI: https://doi.org/10.22146/jnteti.v15i1.23124

Keywords: IoT, Smart Irrigation, Realtime Database, Fertilizer Management, Precision Agriculture

Abstract

Internet of Things (IoT)-based smart agriculture provides an innovative solution to enhance the efficiency and sustainability of agricultural production amid challenges such as water scarcity, inefficient fertilization, and climate variability. This study developed an IoT-based smart irrigation and fertilization management system integrated with the Firebase Realtime Database for real-time monitoring and control. The system combined soil moisture, air humidity, and temperature sensors with an ESP32 microcontroller, enabling automatic and manual decision-making based on environmental conditions. Users could interact with the system via a responsive web dashboard that provided both data visualization and manual control. System testing conducted in a greenhouse environment demonstrated stable and accurate data acquisition, with average readings of 27.91°C for temperature, 74.75% RH for air humidity, and 71.31% for soil moisture, within ±2.3% of analogue measurements. The relay actuation response time was less than 1 s, while Firebase synchronization achieved over 98% reliability during continuous operation. Additionally, the system achieved 20% water savings compared to manual irrigation methods and successfully controlled fertilizer distribution and exhaust ventilation to stabilize humidity. These results confirm that the proposed system supports real-time, precise, and energy-efficient control, suitable for small to medium-scale agricultural applications, especially in areas with unstable internet connectivity. This research establishes a strong foundation for future integration with AI-based systems, such as fuzzy logic and machine learning, to enable fully autonomous, adaptive precision agriculture.

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