V et al. (2026) AI-Enabled Precision Irrigation for Water-Efficient and Sustainable Agriculture

Identification

• Journal: International Journal for Research in Applied Science and Engineering Technology
• Year: 2026
• Date: 2026-09-08
• Authors: Priyadharshini V
• DOI: 10.22214/ijraset.2026.84732

Research Groups

• Sona College of Technology, Anna University Coimbatore India (Department of Computer Science and Engineering)

Short Summary

This research introduces an AI Driven Arduino and Raspberry Pi irrigation system that maximizes agricultural water use by real time sensing, intelligent decision making, and automated irrigation control. The proposed system reduces unnecessary irrigation and the utilization of resources.

Objective

• To design a precision irrigation system based on Arduino and Raspberry Pi with AI for automated monitoring and watering of soil and environment.
• To maximize water outputs and minimize water losses for efficient irrigation, and to aid sustainable farming.

Study Configuration

• Spatial Scale: Field scale (agricultural field)
• Temporal Scale: Real-time (continuous monitoring)

Methodology and Data

• Models used:
  • Machine learning models (e.g., LSTM) for predicting irrigation needs
  • AI/ML model for analyzing sensor data and making decisions
• Data sources:
  • Soil moisture sensors
  • Temperature and humidity sensors
  • Rainfall sensors
  • Water level sensors

Main Results

• The proposed system shows good performance in predicting irrigation needs with an accuracy of 94.2%.
• The system reduces water consumption by 35% compared to conventional irrigation methods.
• The average response time is 2.4 seconds, allowing for near real-time irrigation control.

Contributions

• Original value of the article: The integration of AI and low-cost embedded platforms (Arduino and Raspberry Pi) creates a scalable, water-efficient, and sustainable smart agriculture solution.
• Contribution to existing literature: This research fills the gap by combining low-cost sensing and actuation with edge-based artificial intelligence processing for precision irrigation.

Funding

• This research was funded by [insert funding agency/project name/reference code].

Citation

@article{V2026AIEnabled,
  author = {V, Priyadharshini and ., Hemapriya},
  title = {AI-Enabled Precision Irrigation for Water-Efficient and Sustainable Agriculture},
  journal = {International Journal for Research in Applied Science and Engineering Technology},
  year = {2026},
  doi = {10.22214/ijraset.2026.84732},
  url = {https://doi.org/10.22214/ijraset.2026.84732}
}