Artificial Intelligence and Remote Sensing for optimizing Agricultural Water Use: A Systematic Mapping Study

Abstract

Water scarcity and climate variability are placing increasing pressure on agricultural systems, necessitating innovative approaches to sustainable water management. This study presents a systematic review of the integration of artificial intelligence (AI) and remote sensing for optimizing water use in agriculture. A total of 2,817 publications were identified from major scientific databases, of which 67 peer-reviewed studies were selected for detailed qualitative and quantitative analysis. The results reveal a methodological shift toward integrated AI frameworks, with hybrid machine learning approaches being the most widely adopted, accounting for approximately 22% of the analyzed studies. This study provides a structured synthesis of current methodologies, identifies emerging trends, and highlights key research gaps. While AI and remote sensing show strong potential for improving water use efficiency and supporting climate-resilient agriculture, challenges remain, including data limitations, model transferability, and barriers to adoption. Future research should focus on scalable, explainable, and regionally adaptable AI solutions to facilitate large-scale deployment.