Automating Intelligent Irrigation System Using AI

Abstract

This study presents the development of an AI-powered intelligent irrigation system designed to optimize mineral salt levels in green bean cultivation. The system integrates a Raspberry Pi 5 as the main processing unit. TensorFlow is used for AI-based decision-making, and OpenCV handles image analysis. The system continuously collects data from soil moisture, temperature, and nutrient sensors, while employing image processing techniques to detect plant deficiencies. A machine learning model analyzes this data to determine optimal irrigation schedules and necessary mineral adjustments, ensuring efficient water and nutrient management. The research follows a structured methodology that includes system design, AI model training, implementation, and real-world testing. Results show improved water efficiency, reduced manual intervention, and enhanced crop health. This work contributes to precision agriculture by offering an automated and intelligent solution for sustainable irrigation management.