Exploring artificial intelligence applications in agricultural water management in South Asia: Techniques, challenges, and future directions

Abstract

South Asia faces severe water stress from rapid population growth, uneven rainfall and outdated irrigation systems, with efficiency often below 40%. Integrating Artificial Intelligence (AI) into water management can improve irrigation efficiency and decision-making. This systematic literature review examines the transformative role of AI in agricultural water management, highlighting cutting-edge practices and the challenges impeding its widespread application between 2009- 2025, focusing on India, Bangladesh, Sri Lanka and Pakistan. The review involved a Google scholar search using the keywords “Artificial Intelligence,” “Agricultural Water Management,” “Irrigation” and “South Asia”, screening publications from 2009 to 2025. Studies were categorized based on AI techniques, challenges and future directions. Most studies employed machine learning models such as Support Vector Machines (SVM), Random Forest (RF), Artificial Neural Networks (ANNs) and deep learning models like Long Short-Term Memory (LSTM) to optimize irrigation scheduling and predict critical parameters. AI-driven tools, including drones, robotics, remote sensing and systems like Arduino and Raspberry Pi, enable real-time monitoring of soil moisture and water distribution. Nonetheless, technical and socio political challenges remain. The adoption of AI technologies is constrained by inadequate infrastructure, including limited sensor networks, poor internet coverage and a dearth of data analytics platforms, along with financial limitations, low digital literacy, historical inequalities, weak institutional capacity and ethical concerns. AI's role in agricultural water management is advancing towards more effective, data-driven practices. To enhance productivity, efforts must focus on overcoming systemic obstacles through strategic investments, regional partnership, policy innovation and inclusive digital capacity building.