Researchers at Colorado State University have developed an automated system for high-throughput sampling of crop roots directly in the field, enabling scalable and consistent phenotyping for breeding and genetics research. The system is capable of analyzing hundreds to thousands of plots per day, addressing a major bottleneck in linking root traits to genetic variation. By standardizing and accelerating data collection, this technology significantly enhances the ability to identify root phenotypes that influence crop productivity and soil processes.
Feeding a global population projected to reach 9.7 billion by 2050 requires major advances in agricultural productivity, especially under increasing climate variability, water scarcity, and soil degradation. Root systems play a critical role in water and nutrient uptake, as well as carbon storage in soils, yet they remain one of the least understood aspects of plant biology in real field conditions. Traditional root phenotyping methods are slow, inconsistent, and difficult to scale, limiting their usefulness for breeding programs. This technology addresses a critical gap by enabling efficient, field-based measurement of root traits at scale. Improving root system architecture has the potential to simultaneously boost yields, enhance soil health, and reduce greenhouse gas emissions.
The system is built around an automated Root Pulling Force (RPF) mechanism that measures the force required to extract a plant from the soil, a metric strongly correlated with root system architecture. Mounted on a tractor-based high-throughput phenotyping (HTP) platform, the device navigates between crop rows and systematically samples plants across research plots.
A grasping mechanism positions itself around the plant stalk, securely grips it, and applies a controlled vertical force to extract the plant while continuously recording resistance. After extraction, the system releases the plant and moves to the next sampling location, enabling rapid, repeatable measurements across large field areas. This integration of automation and field mobility allows researchers to collect high-quality root data at an unprecedented scale.
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John McKay
Caleb Alvarado
Guy Babbitt
Kyle Palmiscno
Christopher Turner
Bryce Whitehill
Jessy McGowan
Jessy.McGowan@colostate.edu
970-491-7100