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Satellite imagery for high-throughput phenotyping in breeding plots

Advances in breeding efforts to increase the rate of genetic gains and enhance crop resilience to climate change have been limited by the procedure and costs of phenotyping methods. The recent rapid development of sensors, image-processing technology, and data-analysis has provided opportunities for...

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Autores principales: Pinto, Francisco, Zaman-Allah, Mainassara, Reynolds, Matthew, Schulthess, Urs
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10227446/
https://www.ncbi.nlm.nih.gov/pubmed/37260941
http://dx.doi.org/10.3389/fpls.2023.1114670
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author Pinto, Francisco
Zaman-Allah, Mainassara
Reynolds, Matthew
Schulthess, Urs
author_facet Pinto, Francisco
Zaman-Allah, Mainassara
Reynolds, Matthew
Schulthess, Urs
author_sort Pinto, Francisco
collection PubMed
description Advances in breeding efforts to increase the rate of genetic gains and enhance crop resilience to climate change have been limited by the procedure and costs of phenotyping methods. The recent rapid development of sensors, image-processing technology, and data-analysis has provided opportunities for multiple scales phenotyping methods and systems, including satellite imagery. Among these platforms, satellite imagery may represent one of the ultimate approaches to remotely monitor trials and nurseries planted in multiple locations while standardizing protocols and reducing costs. However, the deployment of satellite-based phenotyping in breeding trials has largely been limited by low spatial resolution of satellite images. The advent of a new generation of high-resolution satellites may finally overcome these limitations. The SkySat constellation started offering multispectral images at a 0.5 m resolution since 2020. In this communication we present a case study on the use of time series SkySat images to estimate NDVI from wheat and maize breeding plots encompassing different sizes and spacing. We evaluated the reliability of the calculated NDVI and tested its capacity to detect seasonal changes and genotypic differences. We discuss the advantages, limitations, and perspectives of this approach for high-throughput phenotyping in breeding programs.
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spelling pubmed-102274462023-05-31 Satellite imagery for high-throughput phenotyping in breeding plots Pinto, Francisco Zaman-Allah, Mainassara Reynolds, Matthew Schulthess, Urs Front Plant Sci Plant Science Advances in breeding efforts to increase the rate of genetic gains and enhance crop resilience to climate change have been limited by the procedure and costs of phenotyping methods. The recent rapid development of sensors, image-processing technology, and data-analysis has provided opportunities for multiple scales phenotyping methods and systems, including satellite imagery. Among these platforms, satellite imagery may represent one of the ultimate approaches to remotely monitor trials and nurseries planted in multiple locations while standardizing protocols and reducing costs. However, the deployment of satellite-based phenotyping in breeding trials has largely been limited by low spatial resolution of satellite images. The advent of a new generation of high-resolution satellites may finally overcome these limitations. The SkySat constellation started offering multispectral images at a 0.5 m resolution since 2020. In this communication we present a case study on the use of time series SkySat images to estimate NDVI from wheat and maize breeding plots encompassing different sizes and spacing. We evaluated the reliability of the calculated NDVI and tested its capacity to detect seasonal changes and genotypic differences. We discuss the advantages, limitations, and perspectives of this approach for high-throughput phenotyping in breeding programs. Frontiers Media S.A. 2023-05-16 /pmc/articles/PMC10227446/ /pubmed/37260941 http://dx.doi.org/10.3389/fpls.2023.1114670 Text en Copyright © 2023 Pinto, Zaman-Allah, Reynolds and Schulthess https://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) and the copyright owner(s) 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
Pinto, Francisco
Zaman-Allah, Mainassara
Reynolds, Matthew
Schulthess, Urs
Satellite imagery for high-throughput phenotyping in breeding plots
title Satellite imagery for high-throughput phenotyping in breeding plots
title_full Satellite imagery for high-throughput phenotyping in breeding plots
title_fullStr Satellite imagery for high-throughput phenotyping in breeding plots
title_full_unstemmed Satellite imagery for high-throughput phenotyping in breeding plots
title_short Satellite imagery for high-throughput phenotyping in breeding plots
title_sort satellite imagery for high-throughput phenotyping in breeding plots
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10227446/
https://www.ncbi.nlm.nih.gov/pubmed/37260941
http://dx.doi.org/10.3389/fpls.2023.1114670
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