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.