Abstract:
Purpose: To design and implement an AI-based irrigation water distribution and
optimization system for the D.S. Senanayake Samudra reservoir, ensuring efficient
water usage, sustainable agriculture, and improved crop yields by utilizing advanced
AI algorithms and real-time analytics.
Design/methodology/approach: The research involves developing a hybrid AI model
combining Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU).
The system integrates real-time sensor data with weather forecasts to dynamically
optimize irrigation schedules. A mobile application complements the system for real
time monitoring and actionable recommendations. Findings: The system demonstrated
significant reductions in water waste and improved irrigation efficiency. The
CNN+GRU model outperformed other machine learning models, with optimal
performance metrics for predicting water distribution needs.
Practical implications: This AI-driven system empowers farmers with precise
irrigation management tools, enhancing agricultural productivity while conserving
water resources. It also addresses infrastructure vulnerabilities through real-time
monitoring and proactive maintenance.
Originality value: The research pioneers the integration of AI in large-scale irrigation
systems, leveraging hybrid AI models and mobile applications to address real-world
agricultural challenges. This innovation contributes to sustainable farming practices
and efficient water management.
