DOI: 10.5061/dryad.m905qfvb6
title: Breaking the field phenotyping bottleneck in maize with autonomous robots

description: 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.
description: The TerraSentia rover collected RGB video images and LIDAR point
 clouds from corn plants as it moved between rows in the field. Video data
 was processed by algorithms described in the paper to estimate either ear
 height, stem width, or leaf area index. Ground-truth phenotypes were
 collected from a subset of the same plots using established methods: plot
 combine to estimate grain yield, plant and ear height using a
 centimeter-scaled measuring pole, stem width with calipers, and leaf area
 index by manual measurement and a LICOR LI-3100C scanner. Both rover and
 manual measurements were from multiple plants per plot, and reported as
 means of individual measures per plot.
description: # Breaking the field phenotyping bottleneck in maize with autonomous
 robots
 [https://doi.org/10.5061/dryad.m905qfvb6](https://doi.org/10.5061/dryad.m905qfvb6) ## Description of the data and file structure These files contain data collected either by the TerraSentia robot or by manual measurements for validation.  Data is organized by the Figures presented in paper.  Each dataset includes metadata on year, location and date of collection, field coordinates (X-y grid) for each plot, the maize hybrid planted in each plot, and the phenotypic data values. ### Files and variables #### File: Data\_for\_Figure\_S2\_Trait\_Validation.xlsx **Description:** Data output from TerraSentia rover and ground-truth measures from the same plots, used to demonstrate accuracy of rover-estimated phenotypes.  ##### Variables * Leaf Area Index (LAI), Stem Diameter, Plant Height, Ear Height, timestamp, location of plot in field column (x) and range (y) grid. #### File: Data\_for\_Case\_studies\_1\_to\_5.xlsx **Description:** Data for each figure in the article.   Data corresponding to Table 1 and Figures 2, 3, 4 and 5 are provided in a separate spreadsheet tab.   Table 1 = Observations for Ear Height from a population of pre-commercial hybrids using the TerraSentia robot at five locations in 2022. Figure 2 = Plant canopy traits collected by the TerraSentia robot from an experiment investigating hybrid by plant density by level of nitrogen fertilization. Figure 3 = Measures of nitrogen utilization and its component traits among maize hybrids grown at the Illinois N-responsive field site. Figure 4 = Data collection from an on-farm experiment assessing variation in hybrids supplied different rates of nitrogen fertilizer, at two experimental locations. Figure 5: Robot data collection from precommercial breeding trials of different hybrids grown at multiple locations in 2022. ##### Variables * Descriptions of data variables are provided in each tab.  Each data table contains information about maize hybrids, location of trial, location of plot within field as  x-y grid of range-plot, field management treatments (N fertilizer rate, plant density), and phenotypic values. * Blank cells under individual traits represents missing data