An AI-Driven Precision Irrigation Framework for Enhanced Water Efficiency in Iraqi Agriculture

Water scarcity poses a critical challenge to Iraqi agriculture, threatening food security and economic stability. This study develops an AI-driven precision irrigation framework for Iraq using real climate data from World Bank (2018-2023) and agricultural statistics from FAO. By integrating MODIS vegetation patterns with climate variables, we trained a Random Forest model (R² = 0.946) to optimize irrigation scheduling. Proposed analysis demonstrates that AI-driven irrigation can achieve 60% water savings compared to traditional methods while improving water use efficiency by 200%. The model identifies temperature (r=0.716) and NDVI (r=-0.713) as primary drivers of crop water stress, enabling precise irrigation timing during critical May-July periods. Economic analysis reveals potential annual benefits of $245 million through reduced water costs and maintained crop yields. This research provides a scalable framework for sustainable water management in arid regions, offering Iraq-specific solutions to address worsening water scarcity while maintaining agricultural productivity. The methodology demonstrates how AI can transform traditional agriculture using readily available satellite and climate data, with implications for water-stressed regions globally.