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High-Throughput Field Phenotyping of Leaves, Leaf Sheaths, Culms and Ears of Spring Barley Cultivars at Anthesis and Dough Ripeness
To optimize plant architecture (e.g., photosynthetic active leaf area, leaf-stem ratio), plant physiologists and plant breeders rely on destructively and tediously harvested biomass samples. A fast and non-destructive method for obtaining information about different plant organs could be vehicle-bas...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5681945/ https://www.ncbi.nlm.nih.gov/pubmed/29163629 http://dx.doi.org/10.3389/fpls.2017.01920 |
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author | Barmeier, Gero Schmidhalter, Urs |
author_facet | Barmeier, Gero Schmidhalter, Urs |
author_sort | Barmeier, Gero |
collection | PubMed |
description | To optimize plant architecture (e.g., photosynthetic active leaf area, leaf-stem ratio), plant physiologists and plant breeders rely on destructively and tediously harvested biomass samples. A fast and non-destructive method for obtaining information about different plant organs could be vehicle-based spectral proximal sensing. In this 3-year study, the mobile phenotyping platform PhenoTrac 4 was used to compare the measurements from active and passive spectral proximal sensors of leaves, leaf sheaths, culms and ears of 34 spring barley cultivars at anthesis and dough ripeness. Published vegetation indices (VI), partial least square regression (PLSR) models and contour map analysis were compared to assess these traits. Contour maps are matrices consisting of coefficients of determination for all of the binary combinations of wavelengths and the biomass parameters. The PLSR models of leaves, leaf sheaths and culms showed strong correlations (R(2) = 0.61–0.76). Published vegetation indices depicted similar coefficients of determination; however, their RMSEs were higher. No wavelength combination could be found by the contour map analysis to improve the results of the PLSR or published VIs. The best results were obtained for the dry weight and N uptake of leaves and culms. The PLSR models yielded satisfactory relationships for leaf sheaths at anthesis (R(2) = 0.69), whereas only a low performance for all of sensors and methods was observed at dough ripeness. No relationships with ears were observed. Active and passive sensors performed comparably, with slight advantages observed for the passive spectrometer. The results indicate that tractor-based proximal sensing in combination with optimized spectral indices or PLSR models may represent a suitable tool for plant breeders to assess relevant morphological traits, allowing for a better understanding of plant architecture, which is closely linked to the physiological performance. Further validation of PLSR models is required in independent studies. Organ specific phenotyping represents a first step toward breeding by design. |
format | Online Article Text |
id | pubmed-5681945 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-56819452017-11-21 High-Throughput Field Phenotyping of Leaves, Leaf Sheaths, Culms and Ears of Spring Barley Cultivars at Anthesis and Dough Ripeness Barmeier, Gero Schmidhalter, Urs Front Plant Sci Plant Science To optimize plant architecture (e.g., photosynthetic active leaf area, leaf-stem ratio), plant physiologists and plant breeders rely on destructively and tediously harvested biomass samples. A fast and non-destructive method for obtaining information about different plant organs could be vehicle-based spectral proximal sensing. In this 3-year study, the mobile phenotyping platform PhenoTrac 4 was used to compare the measurements from active and passive spectral proximal sensors of leaves, leaf sheaths, culms and ears of 34 spring barley cultivars at anthesis and dough ripeness. Published vegetation indices (VI), partial least square regression (PLSR) models and contour map analysis were compared to assess these traits. Contour maps are matrices consisting of coefficients of determination for all of the binary combinations of wavelengths and the biomass parameters. The PLSR models of leaves, leaf sheaths and culms showed strong correlations (R(2) = 0.61–0.76). Published vegetation indices depicted similar coefficients of determination; however, their RMSEs were higher. No wavelength combination could be found by the contour map analysis to improve the results of the PLSR or published VIs. The best results were obtained for the dry weight and N uptake of leaves and culms. The PLSR models yielded satisfactory relationships for leaf sheaths at anthesis (R(2) = 0.69), whereas only a low performance for all of sensors and methods was observed at dough ripeness. No relationships with ears were observed. Active and passive sensors performed comparably, with slight advantages observed for the passive spectrometer. The results indicate that tractor-based proximal sensing in combination with optimized spectral indices or PLSR models may represent a suitable tool for plant breeders to assess relevant morphological traits, allowing for a better understanding of plant architecture, which is closely linked to the physiological performance. Further validation of PLSR models is required in independent studies. Organ specific phenotyping represents a first step toward breeding by design. Frontiers Media S.A. 2017-11-07 /pmc/articles/PMC5681945/ /pubmed/29163629 http://dx.doi.org/10.3389/fpls.2017.01920 Text en Copyright © 2017 Barmeier and Schmidhalter. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Barmeier, Gero Schmidhalter, Urs High-Throughput Field Phenotyping of Leaves, Leaf Sheaths, Culms and Ears of Spring Barley Cultivars at Anthesis and Dough Ripeness |
title | High-Throughput Field Phenotyping of Leaves, Leaf Sheaths, Culms and Ears of Spring Barley Cultivars at Anthesis and Dough Ripeness |
title_full | High-Throughput Field Phenotyping of Leaves, Leaf Sheaths, Culms and Ears of Spring Barley Cultivars at Anthesis and Dough Ripeness |
title_fullStr | High-Throughput Field Phenotyping of Leaves, Leaf Sheaths, Culms and Ears of Spring Barley Cultivars at Anthesis and Dough Ripeness |
title_full_unstemmed | High-Throughput Field Phenotyping of Leaves, Leaf Sheaths, Culms and Ears of Spring Barley Cultivars at Anthesis and Dough Ripeness |
title_short | High-Throughput Field Phenotyping of Leaves, Leaf Sheaths, Culms and Ears of Spring Barley Cultivars at Anthesis and Dough Ripeness |
title_sort | high-throughput field phenotyping of leaves, leaf sheaths, culms and ears of spring barley cultivars at anthesis and dough ripeness |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5681945/ https://www.ncbi.nlm.nih.gov/pubmed/29163629 http://dx.doi.org/10.3389/fpls.2017.01920 |
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