Design and Implementation of an IoT-Based Automatic Plant Watering System Using ESP32 and the Blynk Platform

prototype metods

Authors

  • Qois Ridwan universitas boyolali
  • Yusuf Eko Rohmadi universitas boyolali
  • Muhammad Abdul Azis universitas boyolali
  • Taman Ginting universitas boyolali

DOI:

https://doi.org/10.59934/jaiea.v6i1.2696

Keywords:

Automatic Irrigation; Blynk; ESP32; Internet of Things; Prototyping

Abstract

Manual watering of potted plants in household-scale gardening is often inconsistent due to limited time and high mobility of plant owners, which can cause water waste or plant stress from irregular care. This study designs and implements an automatic monitoring and control system for plant watering based on the Internet of Things (IoT), using an ESP32 microcontroller and the Blynk platform. The system uses a resistive soil moisture sensor to continuously read real-time soil conditions and applies a threshold-based approach so that irrigation is triggered only when needed, while a manual override is also available via the Blynk mobile application for flexibility. The system was developed using the Prototyping method, allowing iterative design, construction, and evaluation before final deployment. Testing covered soil moisture sensor accuracy, relay and actuator response, Wi-Fi connectivity, and automatic mode operation, followed by an overall functional test and a user satisfaction survey of 30 respondents. The results show that all hardware components and features operated according to specification, and most respondents reported satisfaction with the system's usability and reliability. The developed system offers a practical solution for household plant owners with limited time for manual watering and supports the concept of a sustainable smart home garden.

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Published

2026-10-15

How to Cite

Ridwan, Q., Yusuf Eko Rohmadi, Muhammad Abdul Azis, & Taman Ginting. (2026). Design and Implementation of an IoT-Based Automatic Plant Watering System Using ESP32 and the Blynk Platform: prototype metods. Journal of Artificial Intelligence and Engineering Applications (JAIEA), 6(1), 287–293. https://doi.org/10.59934/jaiea.v6i1.2696