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High-Throughput Phenotyping of Canopy Cover and Senescence in Maize Field Trials Using Aerial Digital Canopy Imaging
In the crop breeding process, the use of data collection methods that allow reliable assessment of crop adaptation traits, faster and cheaper than those currently in use, can significantly improve resource use efficiency by reducing selection cost and can contribute to increased genetic gain through...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI AG
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7745117/ https://www.ncbi.nlm.nih.gov/pubmed/33489316 http://dx.doi.org/10.3390/rs10020330 |
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author | Makanza, Richard Zaman-Allah, Mainassara Cairns, Jill E. Magorokosho, Cosmos Tarekegne, Amsal Olsen, Mike Prasanna, Boddupalli M. |
author_facet | Makanza, Richard Zaman-Allah, Mainassara Cairns, Jill E. Magorokosho, Cosmos Tarekegne, Amsal Olsen, Mike Prasanna, Boddupalli M. |
author_sort | Makanza, Richard |
collection | PubMed |
description | In the crop breeding process, the use of data collection methods that allow reliable assessment of crop adaptation traits, faster and cheaper than those currently in use, can significantly improve resource use efficiency by reducing selection cost and can contribute to increased genetic gain through improved selection efficiency. Current methods to estimate crop growth (ground canopy cover) and leaf senescence are essentially manual and/or by visual scoring, and are therefore often subjective, time consuming, and expensive. Aerial sensing technologies offer radically new perspectives for assessing these traits at low cost, faster, and in a more objective manner. We report the use of an unmanned aerial vehicle (UAV) equipped with an RGB camera for crop cover and canopy senescence assessment in maize field trials. Aerial-imaging-derived data showed a moderately high heritability for both traits with a significant genetic correlation with grain yield. In addition, in some cases, the correlation between the visual assessment (prone to subjectivity) of crop senescence and the senescence index, calculated from aerial imaging data, was significant. We concluded that the UAV-based aerial sensing platforms have great potential for monitoring the dynamics of crop canopy characteristics like crop vigor through ground canopy cover and canopy senescence in breeding trial plots. This is anticipated to assist in improving selection efficiency through higher accuracy and precision, as well as reduced time and cost of data collection. |
format | Online Article Text |
id | pubmed-7745117 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI AG |
record_format | MEDLINE/PubMed |
spelling | pubmed-77451172021-01-22 High-Throughput Phenotyping of Canopy Cover and Senescence in Maize Field Trials Using Aerial Digital Canopy Imaging Makanza, Richard Zaman-Allah, Mainassara Cairns, Jill E. Magorokosho, Cosmos Tarekegne, Amsal Olsen, Mike Prasanna, Boddupalli M. Remote Sens (Basel) Article In the crop breeding process, the use of data collection methods that allow reliable assessment of crop adaptation traits, faster and cheaper than those currently in use, can significantly improve resource use efficiency by reducing selection cost and can contribute to increased genetic gain through improved selection efficiency. Current methods to estimate crop growth (ground canopy cover) and leaf senescence are essentially manual and/or by visual scoring, and are therefore often subjective, time consuming, and expensive. Aerial sensing technologies offer radically new perspectives for assessing these traits at low cost, faster, and in a more objective manner. We report the use of an unmanned aerial vehicle (UAV) equipped with an RGB camera for crop cover and canopy senescence assessment in maize field trials. Aerial-imaging-derived data showed a moderately high heritability for both traits with a significant genetic correlation with grain yield. In addition, in some cases, the correlation between the visual assessment (prone to subjectivity) of crop senescence and the senescence index, calculated from aerial imaging data, was significant. We concluded that the UAV-based aerial sensing platforms have great potential for monitoring the dynamics of crop canopy characteristics like crop vigor through ground canopy cover and canopy senescence in breeding trial plots. This is anticipated to assist in improving selection efficiency through higher accuracy and precision, as well as reduced time and cost of data collection. MDPI AG 2018-02-23 /pmc/articles/PMC7745117/ /pubmed/33489316 http://dx.doi.org/10.3390/rs10020330 Text en © 2018 by the authors http://creativecommons.org/licenses/by/4.0/ This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Makanza, Richard Zaman-Allah, Mainassara Cairns, Jill E. Magorokosho, Cosmos Tarekegne, Amsal Olsen, Mike Prasanna, Boddupalli M. High-Throughput Phenotyping of Canopy Cover and Senescence in Maize Field Trials Using Aerial Digital Canopy Imaging |
title | High-Throughput Phenotyping of Canopy Cover and Senescence in Maize Field Trials Using Aerial Digital Canopy Imaging |
title_full | High-Throughput Phenotyping of Canopy Cover and Senescence in Maize Field Trials Using Aerial Digital Canopy Imaging |
title_fullStr | High-Throughput Phenotyping of Canopy Cover and Senescence in Maize Field Trials Using Aerial Digital Canopy Imaging |
title_full_unstemmed | High-Throughput Phenotyping of Canopy Cover and Senescence in Maize Field Trials Using Aerial Digital Canopy Imaging |
title_short | High-Throughput Phenotyping of Canopy Cover and Senescence in Maize Field Trials Using Aerial Digital Canopy Imaging |
title_sort | high-throughput phenotyping of canopy cover and senescence in maize field trials using aerial digital canopy imaging |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7745117/ https://www.ncbi.nlm.nih.gov/pubmed/33489316 http://dx.doi.org/10.3390/rs10020330 |
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