AI-Based Smart Irrigation System

Water availability, irregular rainfall, changing temperatures, and inefficient irrigation practices create pressure on farmproductivity and operating costs. This paper presents a research-based design for an AI-based smart irrigation systemthat combines soil-moisture sensing, local weather information, crop and soil characteristics, and a decision engine torecommend or automate irrigation. The proposed architecture uses sensor nodes to collect field measurements, acontroller to validate readings, and a rule-based safety layer supported by machine-learning prediction where suitablelabelled data are available. Irrigation is triggered only when estimated crop water need, sensor evidence, and safetyconstraints agree; rainfall forecasts, sensor faults, pump limits, and manual override are considered before actuation. Areview of recent literature indicates that machine learning can support irrigation scheduling, but performance dependson data quality, calibration, crop context, and validation across locations. Unlike an experimental report, this paperdoes not claim measured water savings or crop-yield gains. It defines a practical architecture, control workflow,evaluation protocol, and deployment risks that can guide a student prototype and subsequent field testing.