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<p>Understanding phenotypic plasticity in maize (<i>Zea mays</i> L.) is a current grand challenge for continued crop improvement. Measuring the interactive effects of genetics, environmental factors, and management practices (GxExM) on crop performance is time-consuming, expensive, and a major bottleneck to&#xa0;yield advancement. We demonstrate that an 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 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. 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. The robot measured these phenotypes with high accuracy and reliability, at scales sufficient to dissect interactions between genotypes and nitrogen rates in several environments. The results show that within-row, autonomous field robots hold great promise to increase GxExM understanding and decrease the amount of human labor required for plant phenotyping.</p>

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Breaking the field phenotyping bottleneck in maize with autonomous robots

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月21日

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  • Jason DeBruin,
  • Thomas Aref,
  • Sara Tirado Tolosa,
  • Rebecca Hensley,
  • Haley Underwood,
  • Michael McGuire,
  • Chinmay Soman,
  • Grace Nystrom,
  • Emma Parkinson,
  • Catherine Li,
  • Stephen Patrick Moose,
  • Girish Chowdhary

摘要

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 (GxExM) on crop performance is time-consuming, expensive, and a major bottleneck to yield advancement. We demonstrate that an 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 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. 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. The robot measured these phenotypes with high accuracy and reliability, at scales sufficient to dissect interactions between genotypes and nitrogen rates in several environments. The results show that within-row, autonomous field robots hold great promise to increase GxExM understanding and decrease the amount of human labor required for plant phenotyping.