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Utilization of Spectral Indices for High-Throughput Phenotyping

The conventional plant breeding evaluation of large sets of plant phenotypes with precision and speed is very challenging. Thus, consistent, automated, multifaceted, and high-throughput phenotyping (HTP) technologies are becoming increasingly significant as tools to aid conventional breeding program...

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Autores principales: Tayade, Rupesh, Yoon, Jungbeom, Lay, Liny, Khan, Abdul Latif, Yoon, Youngnam, Kim, Yoonha
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9268975/
https://www.ncbi.nlm.nih.gov/pubmed/35807664
http://dx.doi.org/10.3390/plants11131712
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author Tayade, Rupesh
Yoon, Jungbeom
Lay, Liny
Khan, Abdul Latif
Yoon, Youngnam
Kim, Yoonha
author_facet Tayade, Rupesh
Yoon, Jungbeom
Lay, Liny
Khan, Abdul Latif
Yoon, Youngnam
Kim, Yoonha
author_sort Tayade, Rupesh
collection PubMed
description The conventional plant breeding evaluation of large sets of plant phenotypes with precision and speed is very challenging. Thus, consistent, automated, multifaceted, and high-throughput phenotyping (HTP) technologies are becoming increasingly significant as tools to aid conventional breeding programs to develop genetically improved crops. With rapid technological advancement, various vegetation indices (VIs) have been developed. These VI-based imaging approaches, linked with artificial intelligence and a variety of remote sensing applications, provide high-throughput evaluations, particularly in the field of precision agriculture. VIs can be used to analyze and predict different quantitative and qualitative aspects of vegetation. Here, we provide an overview of the various VIs used in agricultural research, focusing on those that are often employed for crop or vegetation evaluation, because that has a linear relationship to crop output, which is frequently utilized in crop chlorophyll, health, moisture, and production predictions. In addition, the following aspects are here described: the importance of VIs in crop research and precision agriculture, their utilization in HTP, recent photogrammetry technology, mapping, and geographic information system software integrated with unmanned aerial vehicles and its key features. Finally, we discuss the challenges and future perspectives of HTP technologies and propose approaches for the development of new tools to assess plants’ agronomic traits and data-driven HTP resolutions for precision breeding.
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spelling pubmed-92689752022-07-09 Utilization of Spectral Indices for High-Throughput Phenotyping Tayade, Rupesh Yoon, Jungbeom Lay, Liny Khan, Abdul Latif Yoon, Youngnam Kim, Yoonha Plants (Basel) Review The conventional plant breeding evaluation of large sets of plant phenotypes with precision and speed is very challenging. Thus, consistent, automated, multifaceted, and high-throughput phenotyping (HTP) technologies are becoming increasingly significant as tools to aid conventional breeding programs to develop genetically improved crops. With rapid technological advancement, various vegetation indices (VIs) have been developed. These VI-based imaging approaches, linked with artificial intelligence and a variety of remote sensing applications, provide high-throughput evaluations, particularly in the field of precision agriculture. VIs can be used to analyze and predict different quantitative and qualitative aspects of vegetation. Here, we provide an overview of the various VIs used in agricultural research, focusing on those that are often employed for crop or vegetation evaluation, because that has a linear relationship to crop output, which is frequently utilized in crop chlorophyll, health, moisture, and production predictions. In addition, the following aspects are here described: the importance of VIs in crop research and precision agriculture, their utilization in HTP, recent photogrammetry technology, mapping, and geographic information system software integrated with unmanned aerial vehicles and its key features. Finally, we discuss the challenges and future perspectives of HTP technologies and propose approaches for the development of new tools to assess plants’ agronomic traits and data-driven HTP resolutions for precision breeding. MDPI 2022-06-28 /pmc/articles/PMC9268975/ /pubmed/35807664 http://dx.doi.org/10.3390/plants11131712 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Tayade, Rupesh
Yoon, Jungbeom
Lay, Liny
Khan, Abdul Latif
Yoon, Youngnam
Kim, Yoonha
Utilization of Spectral Indices for High-Throughput Phenotyping
title Utilization of Spectral Indices for High-Throughput Phenotyping
title_full Utilization of Spectral Indices for High-Throughput Phenotyping
title_fullStr Utilization of Spectral Indices for High-Throughput Phenotyping
title_full_unstemmed Utilization of Spectral Indices for High-Throughput Phenotyping
title_short Utilization of Spectral Indices for High-Throughput Phenotyping
title_sort utilization of spectral indices for high-throughput phenotyping
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9268975/
https://www.ncbi.nlm.nih.gov/pubmed/35807664
http://dx.doi.org/10.3390/plants11131712
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