Optimizing micro irrigation efficiency in water-scarce agroecosystems using AI and remote sensing technologies

In dryland farming, water shortages require creative irrigation solutions. In dry and semi-arid areas, drip, sprinkler, and subsurface irrigation are becoming more popular to maximize water use. These systems still struggle to distribute water efficiently, use a lot of energy, and make real-time decisions. Using AI and remote sensing to overcome these restrictions is revolutionary. AI-driven irrigation systems use machine learning algorithms, predictive analytics, and IoT-enabled smart sensors to maximize water use, minimize waste, and increase crop yields. UAVs, satellites, and multispectral imaging are remote sensing technologies. These technologies enable real-time soil moisture, evapotranspiration, and plant health monitoring. For site-by-site water management, GIS, AI, and remote sensing improve precise irrigation. This review examines the pros, cons, and future uses of artificial intelligence (AI) for remote sensing and automated irrigation in dryland farming using recent studies. Comparing current studies shows that wide adoption requires better policy frameworks, sensor networks, and AI algorithms. It also identifies key research gaps. The results show that we need better water management technology and approaches from different fields to sustain farming. Future research should expand remote sensing in small-scale farming, incorporate blockchain for data security, and develop affordable AI-driven irrigation models. To implement policies and infrastructure widely, policymakers and stakeholders must collaborate. Researchers, agronomists, and lawmakers who want to improve irrigation efficiency with AI and remote sensing will benefit from this study.