Breaking the field phenotyping bottleneck in maize with autonomous robots

Understanding phenotypic plasticity in maize (Zea mays L.) is a current
grand challenge for continued crop improvement.  Measuring the
interactive effects of genetics, environmental factors, and management
practices such as nitrogen rate (GxExM) on crop performance is
time-consuming, expensive, and a major bottleneck to continued yield
advancement. We demonstrate that a novel autonomous robotic platform,
capable of collecting biologically relevant and commonly measured
phenotypes, within a maize canopy at high-throughput, low-cost, and
high-volume is now a reality. Field teams used multiple TerraSentia
autonomous ground robots developed by EarthSense, Inc. (Champaign, IL) to
capture data using a suite of low-cost sensors from nearly 200,000
experimental units, located at 142 unique research fields in the USA and
Canada, across five years. Novel computer vision and machine learning
algorithms, developed by EarthSense, Inc., analyzed these in-canopy
multi-sensor data to deliver ground-truth validated plant height, ear
height, stem diameter, and leaf area index at multiple time points during
each season. We show the robot measured these phenotypes with high
accuracy and reliability, at scales sufficient to functionally dissect
interactions between genotypes and nitrogen rates in several environments.
The results show that within-row, autonomous field robots hold great
promise to increase understanding of GxExM interactions in maize research
and decrease the amount of human labor required for plant phenotyping.