Literature DB >> 35895204

Design Considerations for In-Field Measurement of Plant Architecture Traits Using Ground-Based Platforms.

Piyush Pandey1, Sierra Young2.   

Abstract

This work provides a high-level overview of system design considerations for measuring plant architecture traits in row crops using ground-based, mobile platforms. High-throughput phenotyping technologies are commonly deployed in isolated growth chambers or greenhouses; however, there is a need for field-based systems to measure large quantities of plants exposed to natural climates throughout a growing season. High-throughput methods using ground-based mobile systems collect valuable phenotypic information at higher temporal resolutions compared to manual methods (e.g., handheld calipers and measuring sticks). Additionally, the close proximity to plants when using ground-based systems compared to aerial platforms enables plant phenotyping at the organ level. While there is no single best platform for obtaining ground-based plant measurements across crop varieties with different planting configurations, there are a wide range of off-the-shelf systems and sensors that can be integrated to accommodate varying row widths, plant spacing, plant heights, and plot sizes, in addition to emerging commercially available platforms. This chapter will provide an overview of sensor types suitable for phenotyping plant size and shape, as well as provide guidance for deployment with ground-based systems, including push carts or buggies, modified tractors, and robotic platforms.
© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Field-based phenotyping; High-throughput phenotyping; Plant architecture; Row crops

Mesh:

Year:  2022        PMID: 35895204     DOI: 10.1007/978-1-0716-2537-8_15

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  3 in total

1.  Towards recommendations for metadata and data handling in plant phenotyping.

Authors:  Paweł Krajewski; Dijun Chen; Hanna Ćwiek; Aalt D J van Dijk; Fabio Fiorani; Paul Kersey; Christian Klukas; Matthias Lange; Augustyn Markiewicz; Jan Peter Nap; Jan van Oeveren; Cyril Pommier; Uwe Scholz; Marco van Schriek; Björn Usadel; Stephan Weise
Journal:  J Exp Bot       Date:  2015-06-04       Impact factor: 6.992

2.  Measures for interoperability of phenotypic data: minimum information requirements and formatting.

Authors:  Hanna Ćwiek-Kupczyńska; Thomas Altmann; Daniel Arend; Elizabeth Arnaud; Dijun Chen; Guillaume Cornut; Fabio Fiorani; Wojciech Frohmberg; Astrid Junker; Christian Klukas; Matthias Lange; Cezary Mazurek; Anahita Nafissi; Pascal Neveu; Jan van Oeveren; Cyril Pommier; Hendrik Poorter; Philippe Rocca-Serra; Susanna-Assunta Sansone; Uwe Scholz; Marco van Schriek; Ümit Seren; Björn Usadel; Stephan Weise; Paul Kersey; Paweł Krajewski
Journal:  Plant Methods       Date:  2016-11-09       Impact factor: 4.993

3.  GPhenoVision: A Ground Mobile System with Multi-modal Imaging for Field-Based High Throughput Phenotyping of Cotton.

Authors:  Yu Jiang; Changying Li; Jon S Robertson; Shangpeng Sun; Rui Xu; Andrew H Paterson
Journal:  Sci Rep       Date:  2018-01-19       Impact factor: 4.379

  3 in total

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