Investigating the Optimal Flow Rate for Hydroponic Plants Using IoT and the Decision Tree Model

Tapanee  Treeratanaporn; Thongchai  Phairoh

doi:10.6180/jase.202602_29(2).0009

Investigating the Optimal Flow Rate for Hydroponic Plants Using IoT and the Decision Tree Model

Electrical Engineering Computer Science and Information Engineering

Tapanee Treeratanaporn¹This email address is being protected from spambots. You need JavaScript enabled to view it. and Thongchai Phairoh²

¹Department of Electrical and Computer Engineering, Faculty of Engineering, King Mongkut’s University of Technology North Bangkok, Bangkok, 10800, Thailand

²Department of Applied Engineering Technology, College of Engineering and Technology, Virginia State University, 23806, USA

Received: February 20, 2025
Accepted: May 13, 2025
Publication Date: May 30, 2025

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202602_29(2).0009

Hydroponics has gained significant interest as a vital method for sustainable food production. Various research works have emphasized key factors in hydroponic cultivation, including nutrient composition, solution and air temperatures, humidity, light intensity, and other environmental factors. However, the optimal flow rate of the nutrient solution has received limited attention despite its potentially critical role in plant growth, such as leaf count, size, and height. This study investigates whether the flow rate affects plant growth and which flow rate is optimal. Basil was chosen as the experimental plant due to its widespread culinary use. The research employs IoT technology to automate hydroponic systems. Key hardware components consist of sensors, microcontrollers, and pumps, supported by software including Arduino IDE and an IoT platform for cloud-based data storage. Experimental data collection, including key factors in hydroponics cultivation and plant growth, started from seed germination to harvest and was then analyzed using decision tree-based data analytics techniques. Results indicate that a nutrient solution flow rate has been identified as a key factor influencing plant growth. However, the interaction between flow rate and other environmental factors, such as temperature, light intensity, and pH levels, must also be carefully considered. A holistic approach that accounts for the interdependence of these factors is essential to fully understanding the mechanisms behind plant growth. This research can be a prototype for identifying optimal cultivation features and other factors across various plant types.

Keywords: Flow rate; Hydroponics; Internet of Things; Machine Learning; Solar System

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