In‐Field Whole‐Plant Maize Architecture Characterized by Subcanopy Rovers and Latent Space Phenotyping

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.

• Publication year

  2019

• Venue

  The Plant Phenome Journal

• Publication date

  2019-01-01

• Fields of study

  Agricultural and Food Sciences, Computer Science, Biology

• Identifiers
  DOI 10.2135/tppj2019.07.0011
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Latent Space Phenotyping: Automatic Image-Based Phenotyping for Treatment Studies

  2019influential reference
• In-field whole plant maize architecture characterized by Latent Space Phenotyping

  2019cited by this paper
• Evaluating maize phenotype dynamics under drought stress using terrestrial lidar

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• Idea Factory: the Maize Genomes to Fields Initiative

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  2018cited by this paper
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  2018influential reference
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• Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring.

  2016cited by this paper
• Image Analysis: The New Bottleneck in Plant Phenotyping [Applications Corner]

  2015cited by this paper
• R: A language and environment for statistical computing.

  2014cited by this paper
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  2013cited by this paper
• Development and evaluation of a field-based high-throughput phenotyping platform.

  2013cited by this paper
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• Multivariate Density Estimation

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