(untitled) (SAARC Journal of Agriculture, 2026)
Automated irrigation systems utilize artificial intelligence (AI) to improve water usage efficiency and increase crop yields. In contrast to traditional methods that rely on fixed schedules or sensor-triggered thresholds, these systems can adjust to real-time environmental changes. This research investigates the application of Transformer Networks in automated irrigation, using Vision Transformers (ViTs) to assess soil moisture and Temporal Transformers to predict weather. By integrating AI-driven forecasts with IoT-enabled irrigation systems, this approach fosters efficient water management and reduces waste. Experimental results show significant improvements in irrigation efficiency, precision of yield predictions, and overall water conservation. SAARC J. Agric., 23(2): 35-42 (2025)