AI and IoT-based Smart Irrigation Systems: A Cost and Water Efficiency Analysis in Semi-Arid Regions

Abstract

Water scarcity and increasing operational costs are significant challenges in modern agriculture, particularly in semi-arid regions. This research paper investigates the integration of Artificial Intelligence (AI) and the Internet of Things (IoT) into smart irrigation systems to assess improvements in water efficiency and cost savings. Field trials performed on maize and tomato crops in semi-arid environments have provided comprehensive data on water consumption patterns, crop yield variations, and economic benefits of deploying AI-driven irrigation scheduling along with real-time sensor monitoring. The study builds upon recent scholarly works that document a reduction in irrigation water consumption by as much as 35.78% in grain corn and a water usage reduction of up to 30% across various crops . Furthermore, these systems have introduced significant cost benefits by reducing water bills, energy costs, and labor through automation. This paper reviews the literature, outlines the methodology used in field deployment, discusses data from the trials, and concludes with a discussion of the broader implications for sustainable agriculture. Ultimately, the integration of AI and IoT in irrigation practices not only addresses water scarcity but also enhances crop health and agricultural productivity, thereby supporting economically sustainable farming practices.