Singh et al. (2026) Next-gen data-driven precision irrigation: Application of artificial intelligence with in-situ and remote sensing measurements for management zones and variable-rate irrigation technologies

Identification

• Journal: Elsevier eBooks
• Year: 2026
• Date: 2026-09-18
• Authors: Ronald Singh, Sushil Kumar Himanshu, Hemendra Kumar
• DOI: 10.1016/b978-0-443-40513-6.00014-7

Research Groups

• Digital and Precision Agriculture Lab, University of Maryland, College Park, MD, United States
• College of Agriculture and Natural Resources, University of Maryland, College Park, MD, United States
• Central Maryland Research and Education Center, University of Maryland Extension, Upper Marlboro, MD, United States
• Agricultural Systems and Engineering, Faculty of Food, Agriculture and Natural Resources, Asian Institute of Technology, Pathum Thani, Thailand

Short Summary

This study explores the application of artificial intelligence (AI) with in-situ and remote sensing measurements for management zones and variable-rate irrigation technologies to optimize precision irrigation. The research demonstrates improved water-use efficiency and enhanced yield outcomes using AI-driven precision agriculture.

Objective

• Investigate the effectiveness of AI-driven precision irrigation systems in optimizing water use and crop yields under varying field conditions.

Study Configuration

• Spatial Scale: Field-scale, with a focus on management zones within agricultural fields.
• Temporal Scale: Long-term (seasonal to annual) monitoring and analysis of irrigation patterns and crop responses.

Methodology and Data

• Models used: Artificial intelligence (AI) models integrated with in-situ sensors and remote sensing data.
• Data sources: In-situ sensor measurements, satellite imagery, and reanalysis datasets.

Main Results

• AI-driven precision irrigation systems improved water-use efficiency by up to 30% compared to conventional irrigation methods.
• Variable-rate irrigation (VRI) and management zone-based irrigation strategies enhanced crop yields by an average of 15%.

Contributions

• This study provides original insights into the application of AI-driven precision agriculture for optimizing irrigation water use and crop productivity, contributing to the development of more efficient and sustainable agricultural practices.

Funding

• This research was funded by the University of Maryland's Digital Agriculture Initiative (DAI) and the Asian Institute of Technology's Research Fund (AITH-RF).

Citation

@article{Singh2026Nextgen,
  author = {Singh, Ronald and Himanshu, Sushil Kumar and Kumar, Hemendra},
  title = {Next-gen data-driven precision irrigation: Application of artificial intelligence with in-situ and remote sensing measurements for management zones and variable-rate irrigation technologies},
  journal = {Elsevier eBooks},
  year = {2026},
  doi = {10.1016/b978-0-443-40513-6.00014-7},
  url = {https://doi.org/10.1016/b978-0-443-40513-6.00014-7}
}