Debangshi et al. (2025) Precision irrigation with artificial intelligence–integrated ground‐penetrating radar reduces water stress in corn

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

Identification

• Journal: Agricultural & Environmental Letters
• Year: 2025
• Date: 2025-12-01
• Authors: Udit Debangshi, Prasad Deshpande, Ignacio A. Ciampitti, Susan Metzger, P. V. Vara Prasad, Vaishali Sharda, Gaurav Jha
• DOI: 10.1002/ael2.70047

Research Groups

Not specified in the provided abstract.

Short Summary

This study evaluates an AI-Radar irrigation system against conventional subsurface drip irrigation in central Kansas to optimize water use. It found that the AI-Radar system significantly reduced crop water stress and applied 23.5%–25.1% less irrigation water.

Objective

• To evaluate the performance of an artificial intelligence (AI) with integrated ground-penetrating radar (AI-Radar) irrigation system compared to a conventional subsurface drip irrigation (SSDI) system for optimizing water use and reducing crop water stress at the field scale.

Study Configuration

• Spatial Scale: Field scale, on-farm trial in central Kansas.
• Temporal Scale: Critical tasseling to dough (VT–R4) growth stages of crops.

Methodology and Data

• Models used: Artificial intelligence (AI) for irrigation management, Water Deficit Index (WDI) calculation.
• Data sources: Ground-penetrating radar (integrated with AI), high-resolution satellite imagery, on-farm trial measurements (for irrigation water application).

Main Results

• The Water Deficit Index (WDI) was significantly lower under AI-Radar-based irrigation (0.15–0.16) compared to subsurface drip irrigation (SSDI) (0.20–0.24) during critical growth stages (p < 0.07), indicating more efficient spatial water distribution and minimal crop water stress.
• The AI-Radar system applied 23.5%–25.1% less irrigation water than the SSDI system.
• The AI-Radar system maintained lower crop water stress despite applying less water.

Contributions

• Provides empirical evidence from an on-farm trial demonstrating the effectiveness of AI-Radar technology in optimizing irrigation water use and reducing crop water stress.
• Highlights the potential of AI-Radar-based irrigation to support groundwater conservation and regulatory compliance in water-stressed regions like the Southern Great Plains.

Funding

Not specified in the provided abstract.

Citation

@article{Debangshi2025Precision,
  author = {Debangshi, Udit and Deshpande, Prasad and Ciampitti, Ignacio A. and Metzger, Susan and Prasad, P. V. Vara and Sharda, Vaishali and Jha, Gaurav},
  title = {Precision irrigation with artificial intelligence–integrated ground‐penetrating radar reduces water stress in corn},
  journal = {Agricultural & Environmental Letters},
  year = {2025},
  doi = {10.1002/ael2.70047},
  url = {https://doi.org/10.1002/ael2.70047}
}