Artificial Intelligence and Remote Sensing for optimizing Agricultural Water Use: A Systematic Mapping Study
TL;DR
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.
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.