Ramesh et al. (2025) A smart nail platform for wireless subsoil health monitoring via unmanned aerial vehicle-assisted radio frequency interrogation

Identification

• Journal: Nature Communications
• Year: 2025
• Date: 2025-12-27
• Authors: Yashwanth Ramesh, Muhammad Masud Rana, Praveen Srinivasan, Akshay Krishnakumar, Sarath Gopalakrishnan, Jason Adams, Shalamar D. Armstrong, Rahim Rahimi
• DOI: 10.1038/s41467-025-67889-w

Research Groups

• School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA
• Birck Nanotechnology Center, Purdue University, West Lafayette, IN, USA
• Indiana Corn and Soybean Innovation Center, Purdue University, West Lafayette, IN, USA
• Department of Agronomy, Purdue University, West Lafayette, IN, USA
• School of Materials Engineering, Purdue University, West Lafayette, IN, USA

Short Summary

This paper introduces HARVEST, a low-cost, wireless, and battery-free platform for subsoil health monitoring that reliably detects volumetric water content and electrical conductivity with drone-based radio frequency interrogation from altitudes up to 1.8 meters. The system offers a scalable, maintenance-free solution for next-generation precision agriculture.

Objective

• To develop a low-cost, wireless, battery-free, and maintenance-free platform for scalable subsoil health monitoring (volumetric water content and electrical conductivity) using unmanned aerial vehicle-assisted radio frequency interrogation, addressing limitations of existing costly and high-maintenance sensing systems.

Study Configuration

• Spatial Scale: Distributed across agricultural fields, with sensors deployed at 10 meter intervals in a cornfield, and validated for minimum 2 meter spacing to avoid crosstalk.
• Temporal Scale: Full crop growth cycle (early vegetative to reproductive phase) over a full growing season.

Methodology and Data

• Models used: ANSYS HFSS 2024 R1 (3D finite element method solver) for high-frequency electromagnetic simulations, ANSYS Maxwell low-frequency solver for nail probe simulations, Topp equation for relating soil VWC to effective permittivity.
• Data sources:
  • Electromagnetic simulations.
  • Laboratory experiments using controlled soil samples (varying VWC and EC), Horn Reference Antenna (HRA), Portable Reader Antenna (PRA), Keysight E5072A Vector Network Analyzer (VNA), and NanoVNA-H in an anechoic chamber.
  • Field deployment in an active cornfield at Purdue University's Agronomy Center for Research and Education (ACRE) using a custom UAV-mounted reader (DJI Mavic-series drone with XPOL V2 PRA and NanoVNA).
  • Ground-truth measurements using commercial TEROS-12 soil sensors and a wired datalogger.

Main Results

• HARVEST is a passive, chipless RF backscatter platform featuring nail-shaped sensing probes embedded in the subsoil and an above-ground triple split-ring resonator (SRR) antenna.
• The system operates in the low-UHF band (600–1200 MHz) and exhibits rotational symmetry, enabling orientation-insensitive drone-based interrogation.
• Achieved linear sensitivities of 9.45 MHz per % VWC (via frequency shift of the middle SRR) and 4.34 dB per (dS·m⁻¹) EC (via amplitude ratio of the first SRR).
• Demonstrated reliable wireless readout from UAVs at altitudes up to 1.8 meters.
• A minimum lateral spacing of 2 meters between HARVEST sensors is required to prevent electromagnetic crosstalk.
• Field deployment over a full corn growing season showed HARVEST accurately monitored subsoil VWC and EC, capturing up to 35% variability across 10 meter spaced sensors.
• HARVEST measurements showed close agreement with ground-truth data, with deviations remaining within 2.42 ± 1.86% for VWC and 3.12 ± 1.32% for EC.
• The system maintained signal quality (SNR dropped from 5.6 ± 0.2 dB to 3.6 ± 0.3 dB by the VT stage, with a ~2 dB reduction by R1 due to canopy interference) and exhibited polarization-insensitive response, proving robust under varying field conditions and crop canopy growth.
• Self-correcting behavior for air gaps during initial installation ensured stable long-term measurements.

Contributions

• Introduces HARVEST, a novel fully passive, chipless, wireless sensing platform for robust, multiparameter subsoil monitoring (VWC and EC).
• Presents a unique bifurcated sensor–antenna architecture that decouples the sensing probes (buried in soil) from the RF antenna (above ground), significantly reducing soil-induced RF losses and enabling longer read ranges (up to 1.8 m) compared to previous fully buried chipless sensors.
• Eliminates the need for onboard electronics and batteries, leading to an ultra-low-cost (estimated $1–$3 per unit), maintenance-free, and highly scalable solution for large-scale agricultural deployment.
• Provides comprehensive validation through electromagnetic simulations, rigorous in-lab characterization, and successful full-season field deployment with UAV-mounted interrogation, demonstrating practical applicability and agronomic relevance.
• Enables high-resolution spatiotemporal mapping of subsoil conditions, addressing a critical need for dense, distributed, and autonomous sensing infrastructure in precision agriculture to optimize resource use and reduce environmental impact.

Funding

• Wabash Heartland Innovation Network (WHIN)
• Scalable Manufacturing Aware and Responsive Thin Films (R.R.)
• National Institute of Diabetes and Digestive and Kidney Diseases program at the National Institutes of Health (IR21DK128715-01A1) (R.R.)
• National Institute of Food and Agriculture (13699514) (R.R.)

Citation

@article{Ramesh2025smart,
  author = {Ramesh, Yashwanth and Rana, Muhammad Masud and Srinivasan, Praveen and Krishnakumar, Akshay and Gopalakrishnan, Sarath and Adams, Jason and Armstrong, Shalamar D. and Rahimi, Rahim},
  title = {A smart nail platform for wireless subsoil health monitoring via unmanned aerial vehicle-assisted radio frequency interrogation},
  journal = {Nature Communications},
  year = {2025},
  doi = {10.1038/s41467-025-67889-w},
  url = {https://doi.org/10.1038/s41467-025-67889-w}
}