Scientists at Oak Ridge National Laboratory (ORNL) introduced a robotic system that autonomously collects soil samples and analyses plant-soil interactions, advancing environmental research.
Researchers at Oak Ridge National Laboratory (ORNL), under the U.S. Department of Energy, have developed a robotic platform to autonomously gather soil samples and monitor plant-environment interactions in real-time. This development aims to enhance understanding of carbon cycling and improve predictions of bioenergy crop productivity.
The platform, named sensors, machine vision, automation, and robotics for transforming plants field series (SMART Plant F-Series), integrates latest technologies like GPS and LiDAR for precise navigation and data collection. Demonstrated at ORNL’s SMART field site, the robot efficiently collects soil samples without disturbing plants, supporting efforts to explore plant-soil dynamics critical for carbon storage and biomass production.
This system holds significant potential for researchers in environmental science, agriculture, and bioenergy. Scientists focused on climate change can use its real-time data to refine carbon models, while bioenergy researchers may utilise it to improve crop yields and sustainability. Farmers and ecological managers could also benefit from its ability to monitor soil health and identify factors that influence plant resilience.
“We’ve gathered vast data on aboveground plant structures, but belowground interactions remain elusive,” said Udaya Kalluri, project lead and senior biosciences researcher. The system bridges this gap by linking laboratory analysis to field data through automated sampling at designated coordinates, resulting in clear data integration.
The robot, adapted from a commercial platform, features advanced sensors, 3D-printed components, and electromechanical systems for sample collection. Engineers customised its capabilities to autonomously navigate and return with up to four soil samples. “This technology supports scientists and farmers by automating data gathering, allowing them to focus on analysis,” said Chris Masuo, a robotics engineer, ORNL.
ORNL’s advanced plant phenotyping laboratory (APPL) complements this system by providing insights into plant traits through imaging technologies. Future upgrades include underground imaging for root studies, further aligning lab and field research.
The ultimate vision involves deploying fleets of these robots across ecosystems to create interconnected networks for real-time monitoring and smart management. Enhanced sensors may also detect early signs of environmental stress or disease, bolstering sustainable farming and ecosystem resilience.
This cross-disciplinary project underscores ORNL’s commitment to advancing bioenergy research and providing practical tools for sustainable land management.
