Revolutionizing Agriculture: A Comprehensive Review of AI-Enabled Precision Irrigation and Water Quality Forecasting - Hajar Hamdaoui; Youness Hsana; Inass Hamdi; Hanae Al kaddouri; Nour-Eddine Kouddane (Maǧallaẗ al-baṣraẗ al-ʻulūm al-zirāʻiyyaẗ)
Climate change poses significant challenges to the agricultural sector, exacerbating water scarcity and intensifying the irrational utilization of water reserves.  In response to these pressing issues, Artificial Intelligence (AI) optimizes irrigation, predicting water quantity and quality to ensure optimal crop yields. AI-driven approaches mitigate the challenges of water scarcity, enhancing precision in irrigation management. This review explores recent AI applications in irrigation, focusing on three areas: AI-powered estimation of Crop Evapotranspiration (ETo), integration of AI with Interet of Things (IoT) for Smart Irrigation Systems (Smart-IS), and AI's role in forecasting water quality for irrigation. AI algorithms optimize water usage by quantifying water needs, enabling real-time monitoring, autonomous decision-making, and mitigating risks associated with poor water quality, thus enhancing crop productivity while minimizing environmental impacts. This review emphasizes AI's role in addressing water scarcity and optimizing irrigation in agriculture by utilizing different technologies to ensure sustainable water management and food security. Future researchers will find this review valuable for understanding AI's current impact on irrigation and identifying avenues for further innovation.