Integrating innovative technologies for sustainable agricultural water management

Abstract

Agricultural water scarcity poses an escalating threat to global food production, driven by population growth, climate variability, and the inherent inefficiencies of conventional irrigation systems. This paper analyzes the transformational capacity of smart technologies like the Internet of Things (IoT), Artificial Intelligence (AI) and Machine Learning (ML), and Remote Sensing (RS) integrated with Geographic Information Systems (GIS) for sustainable agricultural water management. By synthesizing recent empirical studies, the paper analyzes the integrated effect of sensor-based monitoring, predictive analytics, automated irrigation control, and satellite and drone surveillance on water-use efficiency, irrigation optimization, and climate-resilient agriculture. Key findings indicate that IoT-based smart irrigation systems reduce water consumption by 20–55% and improve crop yields by 10-30% under optimized conditions, particularly when used with AI-driven decision-support platforms. RS and GIS technologies enable scalable, cost-effective estimation of evapotranspiration and crop water stress across field to regional scales. Despite these demonstrated benefits, substantial barriers to adoption persist, including high implementation costs, inadequate rural digital infrastructure, limited technical literacy among smallholder farmers, and insufficient policy frameworks. This review emphasized the need for supportive policies, cost-reduction strategies, and capacity-building initiatives to bridge the gap between technological innovation and field-level implementation. Overall, smart water management technologies offered a promising pathway toward sustainable agriculture, improved food security, and responsible water stewardship in an increasingly water-scarce world.