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Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
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Author Spotlight: Unraveling the Role of Earthworms in Enhancing Mineral Weathering for CO2 Removal

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Intelligent and automatic irrigation system based on internet of things using fuzzy control technology.

Xinying Liu¹, Zhihuan Zhao², Amin Rezaeipanah³

¹School of Mechanical and Electronic Engineering, Shandong Agricultural and Engineering University, Jinan, 250100, Shandong, China.

Scientific Reports

|April 25, 2025

Summary

This study introduces a smart, low-cost intelligent irrigation system using the Internet of Things (IoT) and fuzzy logic. The system optimizes water use in agriculture, improving efficiency and reducing waste for sustainable farming.

Keywords:

Energy-aware routing Fuzzy system Intelligent agriculture Intelligent irrigation IoT

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Area of Science:

Agricultural Engineering
Computer Science
Environmental Science

Background:

Inefficient irrigation methods in agriculture lead to significant water consumption and waste.
Smart irrigation systems, leveraging the Internet of Things (IoT), offer a solution for optimizing water usage.
Real-time sensor data is crucial for enhancing irrigation efficiency and agricultural output.

Purpose of the Study:

To develop an automatic, low-cost, and intelligent irrigation system.
To optimize water usage and enhance agricultural productivity through smart technology.
To provide a scalable and adaptable solution for modern farming and resource conservation.

Main Methods:

Utilizing a fuzzy rule-based inference approach for optimal irrigation decisions.
Implementing an energy-aware routing algorithm for efficient data transmission.
Integrating sensor data for real-time monitoring and remote control via mobile devices.

Main Results:

The proposed system demonstrated superior performance compared to DLQR, SPIS, and FWIS algorithms.
Achieved significant improvements in network lifetime and reduced power consumption.
Validated the system's effectiveness in optimizing irrigation and conserving resources.

Conclusions:

The developed intelligent irrigation system is effective, cost-efficient, and portable for various agricultural settings.
Contributes to sustainable agriculture by improving water efficiency and minimizing resource wastage.
Offers a scalable solution for enhanced irrigation management in modern farming practices.