DRILL: Deployment & Reading of In-Ground Low-Cost Soil Moisture Logging Sensors Using an Autonomous Ground Robot
Aarya Deb, Joseph Norwood, Martin Vassilev, Sean MacDonald, Kitae Kim, David Cappelleri
AI summary
Problem
Manual deployment of soil moisture sensors is labor-intensive and costly, while passive biodegradable sensors lack reliable robotic integration for large-scale agricultural monitoring.
Approach
The authors developed DRILL, an unmanned ground vehicle that mechanically drills holes to deploy sensors and uses vision-guided navigation with a vector network analyzer to autonomously read them.
Key results
- 93.75% success rate for autonomous sensor deployment
- 100% success rate for sensor reading cycles
- Mean alignment error of 1.3 cm (X) and 0.6 cm (Y), within the 4 cm tolerance
- 73.3% overall valid reading rate over six weeks, with >95% success in the first half
Why it matters
Provides a scalable, labor-free solution for real-time soil moisture monitoring, enabling more efficient irrigation and sustainable precision agriculture.
Abstract
Accurate soil moisture data is crucial to precise irrigation and manual deployment of existing sensors is labor- intensive and expensive, especially in cornfield environments. We present DRILL, an unmanned ground vehicle (UGV) for autonomously deploying and reading low-cost biodegradable soil moisture sensors. The platform consists of a mechanical drilling head (linear actuator, auger drill, 16-slot encoded sensor dispenser, and a chute for guiding) and a reading head with a vector network analyzer (VNA), combined with a vison-guided navigation system for logging soil moisture data without human intervention. The robot platform has been experimentally validated in real-world farm environments over an extensive period and achieved a success rate of 93.75% for the deployment cycle and 100% for the reading cycle, with a mean cycle time of under a minute per sensor. Out of 330 sensor readings with the VNA, overall 73.3% produced valid peaks in 100-160 MHz range indicating a valid soil moisture reading and over 95.3% during the first half of the study, suggesting sensor aging. With a mean in-ground plane alignment error of 1.3 cm in X and 0.6 cm in Y , well within the 4 cm tolerance in each axis, DRILL demonstrates a scalable platform for autonomous soil monitoring and timely data collection in precision agriculture.