Sarkar et al. (2026) Open-Source Artificial Intelligence Tools for Irrigation Management: A Critical Narrative Review of Capabilities, Evidence and Implementation Gaps

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

Identification

• Journal: Journal of Experimental Agriculture International
• Year: 2026
• Date: 2026-09-07
• Authors: Sounak Sarkar, Shubhasish Chatterjee, Suman Roy
• DOI: 10.9734/jeai/2026/v48i94489

Research Groups

• [List the main research groups, labs, or departments involved in the study.]

Short Summary

The paper reviews open-source AI tools for irrigation management, evaluating their technical maturity and potential to improve decision-making processes.

Objective

• Investigate the practical value of combining artificial intelligence (AI) with open-source software in irrigation management systems.

Study Configuration

• Spatial Scale: Global, focusing on crop water demand estimation and soil moisture forecasting.
• Temporal Scale: Long-term, examining the effectiveness of AI tools over multiple seasons.

Methodology and Data

• Models used:
  • Machine-learning libraries (e.g., TensorFlow, PyTorch)
  • Crop-water models (e.g., ISBA, mHM)
  • Geospatial toolkits (e.g., GDAL, GEOS)
  • IoT components
• Data sources: Literature published from 2012 to June 2026, including satellite data, observations, and reanalysis datasets.

Main Results

• Open-source AI tools can be combined into transparent irrigation decision-support workflows.
• Evidence is strongest for reference evapotranspiration estimation, soil-moisture forecasting, crop-water simulation, and proof-of-concept scheduling.
• Weak evidence exists for sustained multiseason water savings, cross-site transfer, autonomous closed-loop control, smallholder usability, and independent comparison with well-tuned conventional scheduling.

Contributions

• This review provides a comprehensive evaluation of open-source AI tools for irrigation management, highlighting their technical maturity and potential to improve decision-making processes.
• The study emphasizes the need for shared benchmarks, multienvironment field trials, reproducible software releases, and implementation research to advance the field.

Funding

• [List projects, programs, and reference codes that funded this research.]

Citation

@article{Sarkar2026OpenSource,
  author = {Sarkar, Sounak and Chatterjee, Shubhasish and Roy, Suman},
  title = {Open-Source Artificial Intelligence Tools for Irrigation Management: A Critical Narrative Review of Capabilities, Evidence and Implementation Gaps},
  journal = {Journal of Experimental Agriculture International},
  year = {2026},
  doi = {10.9734/jeai/2026/v48i94489},
  url = {https://doi.org/10.9734/jeai/2026/v48i94489}
}