Sustainable Irrigation System for Farming Supported by Machine Learning and Real-Time Sensor Data

Area of Science:

• Agricultural Engineering
• Environmental Science
• Computer Science

Background:

• Increasing global water scarcity necessitates innovative solutions for natural resource conservation.
• The Internet of Things (IoT) offers robust, simple, and low-cost technological advancements for various sectors, including agriculture.
• Efficient water management in agriculture is crucial for sustainability and food security.

Purpose of the Study:

• To design and develop an automatic irrigation control system for agricultural fields.
• To integrate wireless sensor networks, actuators, and a mobile application for real-time data monitoring and control.
• To employ Machine Learning algorithms for optimizing irrigation scheduling and water usage.

Main Methods:

• Development of a wireless sensors and actuators network for data collection.
• Creation of a mobile application for real-time data visualization, historical data access, and system control.
• Implementation and comparison of Machine Learning algorithms (Decision Trees, Random Forest, Neural Networks, Support Vector Machines) for predicting optimal irrigation times.
• Development of a method for calculating precise water requirements for field management.

Main Results:

• The Random Forest algorithm demonstrated the highest accuracy (84.6%) in predicting optimal irrigation times.
• The integrated system effectively monitors field conditions and allows for data-driven irrigation decisions.
• The developed automatic irrigation system achieved up to 60% water savings in implemented trials.

Conclusions:

• The developed automatic irrigation control system is effective in optimizing water usage in agriculture.
• The integration of IoT and Machine Learning provides a sustainable and efficient solution to water scarcity challenges.
• Smart irrigation technology offers significant potential for water conservation and improved agricultural practices.