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Title: In-Field Whole-Plant Maize Architecture Characterized by Subcanopy Rovers and Latent Space Phenotyping

Abstract

Core Ideas * Subcanopy rovers enabled 3D characterization of thousands of hybrid maize plots. * Machine learning produces heritable latent traits that describe plant architecture. * Rover-based phenotyping is far more efficient than manual phenotyping. * Latent phenotypes from rovers are ready for application to plant biology and breeding. Collecting useful, interpretable, and biologically relevant phenotypes in a resource-efficient manner is a bottleneck to plant breeding, genetic mapping, and genomic prediction. Autonomous and affordable subcanopy rovers are an efficient and scalable way to generate sensor-based datasets of in-field crop plants. Rovers equipped with lidar can produce three-dimensional reconstructions of entire hybrid maize (Zea mays L.) fields. In this study, we collected 2103 lidar scans of hybrid maize field plots and extracted phenotypic data from them by latent space phenotyping. We performed latent space phenotyping by two methods, principal component analysis and a convolutional autoencoder, to extract meaningful, quantitative latent space phenotypes (LSPs) describing whole-plant architecture and biomass distribution. The LSPs had heritabilities of up to 0.44, similar to some manually measured traits, indicating that they can be selected on or genetically mapped. Manually measured traits can be successfully predicted by using LSPs as explanatory variables in partial least squares regression,more » indicating that the LSPs contain biologically relevant information about plant architecture. These techniques can be used to assess crop architecture at a reduced cost and in an automated fashion for breeding, research, or extension purposes, as well as to create or inform crop growth models.« less

Authors:
 [1];  [2];  [3];  [4];  [5];  [6];  [4];  [1]
  1. Cornell Univ., Ithaca, NY (United States). Inst. for Genomic Diversity
  2. Drexel Univ., Philadelphia, PA (United States). Dept. of Mechanical Engineering
  3. US Dept. of Agriculture (USDA), Ithaca, NY (United States). Agricultural Research Service
  4. Cornell Univ., Ithaca, NY (United States)
  5. EarthSense, Inc., Champaign, IL (United States)
  6. Univ. of Illinois, Champaign, IL (United States). Dept. of Agricultural and Biological Engineering
Publication Date:
Research Org.:
Univ. of Illinois at Urbana-Champaign, IL (United States)
Sponsoring Org.:
USDOE Advanced Research Projects Agency - Energy (ARPA-E)
OSTI Identifier:
1603650
Grant/Contract Number:  
AR0000598
Resource Type:
Accepted Manuscript
Journal Name:
The Plant Phenome Journal
Additional Journal Information:
Journal Volume: 2; Journal Issue: 1; Journal ID: ISSN 2578-2703
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES

Citation Formats

Gage, Joseph L., Richards, Elliot, Lepak, Nicholas, Kaczmar, Nicholas, Soman, Chinmay, Chowdhary, Girish, Gore, Michael A., and Buckler, Edward S. In-Field Whole-Plant Maize Architecture Characterized by Subcanopy Rovers and Latent Space Phenotyping. United States: N. p., 2019. Web. doi:10.2135/tppj2019.07.0011.
Gage, Joseph L., Richards, Elliot, Lepak, Nicholas, Kaczmar, Nicholas, Soman, Chinmay, Chowdhary, Girish, Gore, Michael A., & Buckler, Edward S. In-Field Whole-Plant Maize Architecture Characterized by Subcanopy Rovers and Latent Space Phenotyping. United States. doi:https://doi.org/10.2135/tppj2019.07.0011
Gage, Joseph L., Richards, Elliot, Lepak, Nicholas, Kaczmar, Nicholas, Soman, Chinmay, Chowdhary, Girish, Gore, Michael A., and Buckler, Edward S. Fri . "In-Field Whole-Plant Maize Architecture Characterized by Subcanopy Rovers and Latent Space Phenotyping". United States. doi:https://doi.org/10.2135/tppj2019.07.0011. https://www.osti.gov/servlets/purl/1603650.
@article{osti_1603650,
title = {In-Field Whole-Plant Maize Architecture Characterized by Subcanopy Rovers and Latent Space Phenotyping},
author = {Gage, Joseph L. and Richards, Elliot and Lepak, Nicholas and Kaczmar, Nicholas and Soman, Chinmay and Chowdhary, Girish and Gore, Michael A. and Buckler, Edward S.},
abstractNote = {Core Ideas * Subcanopy rovers enabled 3D characterization of thousands of hybrid maize plots. * Machine learning produces heritable latent traits that describe plant architecture. * Rover-based phenotyping is far more efficient than manual phenotyping. * Latent phenotypes from rovers are ready for application to plant biology and breeding. Collecting useful, interpretable, and biologically relevant phenotypes in a resource-efficient manner is a bottleneck to plant breeding, genetic mapping, and genomic prediction. Autonomous and affordable subcanopy rovers are an efficient and scalable way to generate sensor-based datasets of in-field crop plants. Rovers equipped with lidar can produce three-dimensional reconstructions of entire hybrid maize (Zea mays L.) fields. In this study, we collected 2103 lidar scans of hybrid maize field plots and extracted phenotypic data from them by latent space phenotyping. We performed latent space phenotyping by two methods, principal component analysis and a convolutional autoencoder, to extract meaningful, quantitative latent space phenotypes (LSPs) describing whole-plant architecture and biomass distribution. The LSPs had heritabilities of up to 0.44, similar to some manually measured traits, indicating that they can be selected on or genetically mapped. Manually measured traits can be successfully predicted by using LSPs as explanatory variables in partial least squares regression, indicating that the LSPs contain biologically relevant information about plant architecture. These techniques can be used to assess crop architecture at a reduced cost and in an automated fashion for breeding, research, or extension purposes, as well as to create or inform crop growth models.},
doi = {10.2135/tppj2019.07.0011},
journal = {The Plant Phenome Journal},
number = 1,
volume = 2,
place = {United States},
year = {2019},
month = {11}
}

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