IoT-IIRS: Internet of Things based intelligent-irrigation recommendation system using machine learning approach for...

Area of Science:

• Agricultural Engineering
• Computer Science
• Environmental Science

Background:

• Traditional irrigation methods result in significant water wastage.
• There is a critical need for intelligent systems to optimize water usage in agriculture.
• Advancements in Machine Learning (ML) and the Internet of Things (IoT) offer solutions for automation and efficiency.

Purpose of the Study:

• To develop an IoT-enabled, ML-trained recommendation system for efficient agricultural water management.
• To minimize water wastage and reduce manual intervention in irrigation processes.
• To create an adaptive and robust irrigation recommendation system.

Main Methods:

• Deployment of Internet of Things (IoT) devices in crop fields for real-time data collection (ground and environmental details).
• Data transmission to a cloud-based server for storage and analysis.
• Application of Machine Learning (ML) algorithms to analyze collected data and generate irrigation recommendations.
• Integration of a feedback mechanism for system adaptability and robustness.

Main Results:

• The proposed IoT-enabled ML recommendation system demonstrated effective performance in optimizing water usage.
• Experimental results validated the system's efficiency using both custom and existing crop datasets.
• The system successfully provided data-driven irrigation suggestions with minimal farmer input.

Conclusions:

• The developed intelligent irrigation system significantly enhances water use efficiency in agriculture.
• The integration of IoT and ML provides a viable solution for sustainable farming practices.
• The system's adaptive nature ensures reliable performance in diverse agricultural settings.