2025-05-01 イリノイ大学アーバナ・シャンペーン校
Photo Credit: DISTRIBUTED AUTONOMOUS SYSTEMS LABORATORY
A vehicle used by DISTRIBUTED AUTONOMOUS SYSTEMS LABORATORY run by Prof. Chowdhary
＜関連情報＞
- https://aces.illinois.edu/news/breaking-phenotype-bottleneck-autonomous-robots
- https://siebelschool.illinois.edu/news/commm%20biology/chowdhary
- https://www.nature.com/articles/s42003-025-07890-7
自律型ロボットによるトウモロコシの圃場表現型決定のボトルネック打破 Breaking the field phenotyping bottleneck in maize with autonomous robots
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
Communications Biology Published:21 March 2025
DOI:https://doi.org/10.1038/s42003-025-07890-7
Abstract
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.
