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

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.