Cargando…
Combining self-organizing maps and biplot analysis to preselect maize phenotypic components based on UAV high-throughput phenotyping platform
BACKGROUND: With environmental deterioration, natural resource scarcity, and rapid population growth, mankind is facing severe global food security problems. To meet future needs, it is necessary to accelerate progress in breeding for new varieties with high yield and strong resistance. However, the...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6537385/ https://www.ncbi.nlm.nih.gov/pubmed/31149023 http://dx.doi.org/10.1186/s13007-019-0444-6 |
_version_ | 1783421996016599040 |
---|---|
author | Han, Liang Yang, Guijun Dai, Huayang Yang, Hao Xu, Bo Li, Heli Long, Huiling Li, Zhenhai Yang, Xiaodong Zhao, Chunjiang |
author_facet | Han, Liang Yang, Guijun Dai, Huayang Yang, Hao Xu, Bo Li, Heli Long, Huiling Li, Zhenhai Yang, Xiaodong Zhao, Chunjiang |
author_sort | Han, Liang |
collection | PubMed |
description | BACKGROUND: With environmental deterioration, natural resource scarcity, and rapid population growth, mankind is facing severe global food security problems. To meet future needs, it is necessary to accelerate progress in breeding for new varieties with high yield and strong resistance. However, the traditional phenotypic screening methods have some disadvantages, such as destructive, inefficient, low-dimensional, labor-intensive and cumbersome, which seriously hinder the development of field breeding. Breeders urgently need a high-throughput technique for acquiring and evaluating phenotypic data that can efficiently screen out excellent phenotypic traits from large-scale genotype populations. RESULTS: In the present study, we used an unmanned aerial vehicle (UAV) high-throughput phenotyping (HTP) platform to collect RGB and multispectral images for a breeding program and acquired multiple phenotypic components (or traits), such as plant height, normalized difference vegetation index, biomass accumulation, plant-height growth rate, lodging, and leaf color. By implementing self-organizing maps and principal components analysis biplots to establish phenotypic map and similarity, we proposed an UAV-assisted HTP framework for preselecting maize (Zee mays L.) phenotypic components (or traits). CONCLUSIONS: This framework gives breeders additional information to allow them to quickly identify and preselect plants that have genotypes conferring desirable phenotypic components out of thousands of field plots. The present study also demonstrates that remote sensing is a powerful tool with which to acquire abundant phenotypic components. By using these rich phenotypic components, breeders should be able to more effectively identify and select superior genotypes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13007-019-0444-6) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6537385 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-65373852019-05-30 Combining self-organizing maps and biplot analysis to preselect maize phenotypic components based on UAV high-throughput phenotyping platform Han, Liang Yang, Guijun Dai, Huayang Yang, Hao Xu, Bo Li, Heli Long, Huiling Li, Zhenhai Yang, Xiaodong Zhao, Chunjiang Plant Methods Research BACKGROUND: With environmental deterioration, natural resource scarcity, and rapid population growth, mankind is facing severe global food security problems. To meet future needs, it is necessary to accelerate progress in breeding for new varieties with high yield and strong resistance. However, the traditional phenotypic screening methods have some disadvantages, such as destructive, inefficient, low-dimensional, labor-intensive and cumbersome, which seriously hinder the development of field breeding. Breeders urgently need a high-throughput technique for acquiring and evaluating phenotypic data that can efficiently screen out excellent phenotypic traits from large-scale genotype populations. RESULTS: In the present study, we used an unmanned aerial vehicle (UAV) high-throughput phenotyping (HTP) platform to collect RGB and multispectral images for a breeding program and acquired multiple phenotypic components (or traits), such as plant height, normalized difference vegetation index, biomass accumulation, plant-height growth rate, lodging, and leaf color. By implementing self-organizing maps and principal components analysis biplots to establish phenotypic map and similarity, we proposed an UAV-assisted HTP framework for preselecting maize (Zee mays L.) phenotypic components (or traits). CONCLUSIONS: This framework gives breeders additional information to allow them to quickly identify and preselect plants that have genotypes conferring desirable phenotypic components out of thousands of field plots. The present study also demonstrates that remote sensing is a powerful tool with which to acquire abundant phenotypic components. By using these rich phenotypic components, breeders should be able to more effectively identify and select superior genotypes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13007-019-0444-6) contains supplementary material, which is available to authorized users. BioMed Central 2019-05-28 /pmc/articles/PMC6537385/ /pubmed/31149023 http://dx.doi.org/10.1186/s13007-019-0444-6 Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Han, Liang Yang, Guijun Dai, Huayang Yang, Hao Xu, Bo Li, Heli Long, Huiling Li, Zhenhai Yang, Xiaodong Zhao, Chunjiang Combining self-organizing maps and biplot analysis to preselect maize phenotypic components based on UAV high-throughput phenotyping platform |
title | Combining self-organizing maps and biplot analysis to preselect maize phenotypic components based on UAV high-throughput phenotyping platform |
title_full | Combining self-organizing maps and biplot analysis to preselect maize phenotypic components based on UAV high-throughput phenotyping platform |
title_fullStr | Combining self-organizing maps and biplot analysis to preselect maize phenotypic components based on UAV high-throughput phenotyping platform |
title_full_unstemmed | Combining self-organizing maps and biplot analysis to preselect maize phenotypic components based on UAV high-throughput phenotyping platform |
title_short | Combining self-organizing maps and biplot analysis to preselect maize phenotypic components based on UAV high-throughput phenotyping platform |
title_sort | combining self-organizing maps and biplot analysis to preselect maize phenotypic components based on uav high-throughput phenotyping platform |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6537385/ https://www.ncbi.nlm.nih.gov/pubmed/31149023 http://dx.doi.org/10.1186/s13007-019-0444-6 |
work_keys_str_mv | AT hanliang combiningselforganizingmapsandbiplotanalysistopreselectmaizephenotypiccomponentsbasedonuavhighthroughputphenotypingplatform AT yangguijun combiningselforganizingmapsandbiplotanalysistopreselectmaizephenotypiccomponentsbasedonuavhighthroughputphenotypingplatform AT daihuayang combiningselforganizingmapsandbiplotanalysistopreselectmaizephenotypiccomponentsbasedonuavhighthroughputphenotypingplatform AT yanghao combiningselforganizingmapsandbiplotanalysistopreselectmaizephenotypiccomponentsbasedonuavhighthroughputphenotypingplatform AT xubo combiningselforganizingmapsandbiplotanalysistopreselectmaizephenotypiccomponentsbasedonuavhighthroughputphenotypingplatform AT liheli combiningselforganizingmapsandbiplotanalysistopreselectmaizephenotypiccomponentsbasedonuavhighthroughputphenotypingplatform AT longhuiling combiningselforganizingmapsandbiplotanalysistopreselectmaizephenotypiccomponentsbasedonuavhighthroughputphenotypingplatform AT lizhenhai combiningselforganizingmapsandbiplotanalysistopreselectmaizephenotypiccomponentsbasedonuavhighthroughputphenotypingplatform AT yangxiaodong combiningselforganizingmapsandbiplotanalysistopreselectmaizephenotypiccomponentsbasedonuavhighthroughputphenotypingplatform AT zhaochunjiang combiningselforganizingmapsandbiplotanalysistopreselectmaizephenotypiccomponentsbasedonuavhighthroughputphenotypingplatform |