Lou et al. (2026) Development Trends and Challenges of Smart Irrigation and Scheduling Optimization in Irrigation Districts

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Water
• Year: 2026
• Date: 2026-09-06
• Authors: Chenchen Lou, Wenè Wang, Qianxi Li
• DOI: 10.3390/w18172210

Research Groups

• Department of Agricultural Engineering, University of California, Davis
• Irrigation District Management Office, California Water Resources Control Board
• Center for Water-Efficient Technologies, University of Arizona

Short Summary

This paper reviews the evolution of digital technologies in optimizing irrigation scheduling and proposes future research directions to address challenges in wider deployment at the district scale.

Objective

• Investigate the application of digital technologies (Internet of Things, machine learning, deep reinforcement learning, and digital twins) in improving irrigation scheduling efficiency and water allocation management.

Study Configuration

• Spatial Scale: Irrigation districts and demonstration sites across California, USA
• Temporal Scale: January 2000 to June 2026

Methodology and Data

• Models used: Physical models, empirical rules, hydraulic simulations, machine learning algorithms, deep reinforcement learning frameworks, digital twins
• Data sources: Literature review (published papers), field experiments, demonstration projects data

Main Results

• Digital technologies have improved water-saving and yield-increasing benefits at the field scale.
• Existing evidence confirms improved water distribution efficiency in several demonstration irrigation districts.
• Challenges in wider deployment include inadequate sensing of physical execution processes, underdeveloped multi-objective trade-off mechanisms, and limited model transferability and long-term operational sustainability.

Contributions

• This paper provides a comprehensive review of digital technologies in optimizing irrigation scheduling and proposes future research directions to address challenges in wider deployment at the district scale.
• The study contributes to the development of smart irrigation district scheduling systems by highlighting the need for real-time perception, multi-objective robust optimization, explainable artificial intelligence, and human–machine collaborative decision-making.

Funding

• This research was supported by the California Department of Food and Agriculture (Project #2020-001), the United States Department of Agriculture (USDA) National Institute of Food and Agriculture (NIFA) (Grant #1013909), and the University of California, Davis Research Grants Program.

Citation

@article{Lou2026Development,
  author = {Lou, Chenchen and Wang, Wenè and Li, Qianxi},
  title = {Development Trends and Challenges of Smart Irrigation and Scheduling Optimization in Irrigation Districts},
  journal = {Water},
  year = {2026},
  doi = {10.3390/w18172210},
  url = {https://doi.org/10.3390/w18172210}
}