Application of Artificial Intelligence in Agricultural Water Management: Methods, Practices, and Challenges

Dear Colleagues,

As global water scarcity becomes increasingly severe, agriculture, as a primary water-consuming sector, urgently requires technological innovation to achieve precise management and efficient utilization of water resources. In recent years, artificial intelligence technologies (including machine learning, deep learning, computer vision, reinforcement learning, etc.) have demonstrated significant potential in data analysis, pattern recognition, and intelligent decision-making, providing revolutionary tools for the modernization and intelligent transformation of agricultural water management. By integrating multi-source information such as sensor data, remote sensing imagery, weather forecasts, and geographic information systems, AI can enable dynamic monitoring and optimized control of key aspects like crop water demand, soil moisture, irrigation scheduling, and water quality monitoring. This, in turn, drives the transformation of agricultural water management from experience-driven to data-driven and intelligent decision-making.

To this end, we are pleased to announce this Special Issue titled "Application of Artificial Intelligence in Agricultural Water Management: Methods, Practices, and Challenges". This issue aims to gather cutting-edge research, innovative applications, and comprehensive reviews in this field, exploring how AI can empower sustainable agricultural water management. We sincerely invite experts and scholars from academia, industry, and related fields to contribute their work.

Topics of interest include, but are not limited to, the following areas:

• Intelligent Irrigation Decision-Making and Control Systems;
• Machine learning-based models for crop water demand and evapotranspiration prediction;
• Variable rate irrigation (VRI) intelligent scheduling integrating remote sensing and sensor data;
• Applications of reinforcement learning in optimizing dynamic irrigation strategies;
• Multi-Source Data Fusion and Farmland State Perception;
• Remote sensing inversion of soil moisture, salinity, and crop water stress based on deep learning;
• Collaborative monitoring and data assimilation using drones, satellites, and ground sensor networks;
• AI applications in dynamic delineation of farmland management zones;
• Water Resource Optimization and Sustainable Management;
• AI-based models for regional/watershed-scale agricultural water supply–demand forecasting and allocation
• Intelligent algorithms for conjunctive use of groundwater and surface water;
• AI-driven evaluation of safe utilization of unconventional water resources (e.g., reclaimed water, brackish water);
• Intelligent Models and Integrated Tool Platforms;
• AI-based parameter calibration and optimization of crop growth–water coupling models (e.g., DSSAT, AquaCrop);
• Integration and enhancement of AI with traditional hydrological models (e.g., SEBAL, METRIC);
• Development of intelligent decision support systems (DSS) and visualization platforms for agricultural water management
• Emerging Technologies and Future Perspectives;
• Applications of edge computing and IoT in field-level intelligent water-saving systems;
• Construction and simulation of digital twins and virtual farms for agricultural water management;
• The role of AI in climate change-adaptive agricultural water management.

• Ethics, Interpretability, and Scalability Challenges
• Interpretability and reliability of AI models in agricultural applications
• Data privacy, algorithmic bias, and applicability for smallholder farmers
• Technology promotion pathways and policy recommendations

Manuscript Requirements:

• Submissions should demonstrate originality, scientific rigor, and highlight innovative applications of AI technologies.
• Contributions incorporating case studies, experimental validation, or practical system development are encouraged.
• Manuscripts featuring high-quality figures, interactive visualizations, or open-source code are welcome.
• Papers should be concise, well-structured, and focused on key findings.

This Special Issue aims to provide a platform for interdisciplinary researchers to exchange ideas and collectively advance the integration of AI and agricultural water management, contributing to global food security and the sustainable use of water resources.

Dr. Ze Liu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the Special Issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.