Smart irrigation-based internet of things and cloud computing technologies for sustainable farming

Area of Science:

• Agricultural Engineering
• Environmental Science
• Computer Science

Background:

• Sustainable water management is critical due to agricultural water scarcity and climate change impacts.
• Traditional irrigation methods are inefficient, leading to water waste and reduced crop productivity.
• Innovative solutions are needed to optimize water use in agriculture while maintaining performance.

Purpose of the Study:

• To develop and evaluate a smart irrigation system using the Internet of Things (IoT) and cloud computing.
• To optimize water usage in agriculture through real-time data analysis and automated control.
• To assess the system's effectiveness in reducing water waste and improving agricultural sustainability.

Main Methods:

• Implemented a smart irrigation system with sensors for temperature, humidity, soil moisture, and water level.
• Utilized an ESP32 microcontroller to collect and transmit sensor data to the ThingsBoard cloud platform.
• Developed an algorithm for real-time data analysis to automate irrigation pump activation/deactivation.

Main Results:

• The system significantly reduced water waste by optimizing irrigation based on actual crop needs.
• Real-time measurements and automated decisions ensured efficient irrigation adaptable to environmental fluctuations.
• Performance analysis indicated substantial improvements in water resource management compared to traditional methods.

Conclusions:

• The proposed IoT and cloud-based smart irrigation system is economically feasible (approx. $44.00) and adaptable for various farms.
• The system enhances farmer resilience to climate change and water scarcity, contributing to food security.
• This study offers a replicable model for large-scale smart and sustainable agricultural solutions.