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Application of Remote Sensing for Phenotyping Tar Spot Complex Resistance in Maize
Tar spot complex (TSC), caused by at least two fungal pathogens, Phyllachora maydis and Monographella maydis, is one of the major foliar diseases of maize in Central and South America. P. maydis was also detected in the United States of America in 2015 and since then the pathogen has spread in the m...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6503115/ https://www.ncbi.nlm.nih.gov/pubmed/31114603 http://dx.doi.org/10.3389/fpls.2019.00552 |
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author | Loladze, Alexander Rodrigues, Francelino Augusto Toledo, Fernando San Vicente, Felix Gérard, Bruno Boddupalli, Maruthi Prasanna |
author_facet | Loladze, Alexander Rodrigues, Francelino Augusto Toledo, Fernando San Vicente, Felix Gérard, Bruno Boddupalli, Maruthi Prasanna |
author_sort | Loladze, Alexander |
collection | PubMed |
description | Tar spot complex (TSC), caused by at least two fungal pathogens, Phyllachora maydis and Monographella maydis, is one of the major foliar diseases of maize in Central and South America. P. maydis was also detected in the United States of America in 2015 and since then the pathogen has spread in the maize growing regions of the country. Although remote sensing (RS) techniques are increasingly being used for plant phenotyping, they have not been applied to phenotyping TSC resistance in maize. In this study, several multispectral vegetation indices (VIs) and thermal imaging of maize plots under disease pressure and disease-free conditions were tested using an unmanned aerial vehicle (UAV) over two crop seasons. A strong relationship between grain yield, a vegetative index (MCARI2), and canopy temperature was observed under disease pressure. A strong relationship was also observed between the area under the disease progress curve of TSC and three vegetative indices (RDVI, MCARI1, and MCARI2). In addition, we demonstrated that TSC could cause up to 58% yield loss in the most susceptible maize hybrids. Our results suggest that the RS techniques tested in this study could be used for high throughput phenotyping of TSC resistance and potentially for other foliar diseases of maize. This may help reduce the cost and time required for the development of improved maize germplasm. Challenges and opportunities in the use of RS technologies for disease resistance phenotyping are discussed. |
format | Online Article Text |
id | pubmed-6503115 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-65031152019-05-21 Application of Remote Sensing for Phenotyping Tar Spot Complex Resistance in Maize Loladze, Alexander Rodrigues, Francelino Augusto Toledo, Fernando San Vicente, Felix Gérard, Bruno Boddupalli, Maruthi Prasanna Front Plant Sci Plant Science Tar spot complex (TSC), caused by at least two fungal pathogens, Phyllachora maydis and Monographella maydis, is one of the major foliar diseases of maize in Central and South America. P. maydis was also detected in the United States of America in 2015 and since then the pathogen has spread in the maize growing regions of the country. Although remote sensing (RS) techniques are increasingly being used for plant phenotyping, they have not been applied to phenotyping TSC resistance in maize. In this study, several multispectral vegetation indices (VIs) and thermal imaging of maize plots under disease pressure and disease-free conditions were tested using an unmanned aerial vehicle (UAV) over two crop seasons. A strong relationship between grain yield, a vegetative index (MCARI2), and canopy temperature was observed under disease pressure. A strong relationship was also observed between the area under the disease progress curve of TSC and three vegetative indices (RDVI, MCARI1, and MCARI2). In addition, we demonstrated that TSC could cause up to 58% yield loss in the most susceptible maize hybrids. Our results suggest that the RS techniques tested in this study could be used for high throughput phenotyping of TSC resistance and potentially for other foliar diseases of maize. This may help reduce the cost and time required for the development of improved maize germplasm. Challenges and opportunities in the use of RS technologies for disease resistance phenotyping are discussed. Frontiers Media S.A. 2019-04-30 /pmc/articles/PMC6503115/ /pubmed/31114603 http://dx.doi.org/10.3389/fpls.2019.00552 Text en Copyright © 2019 Loladze, Rodrigues, Toledo, San Vicente, Gérard and Boddupalli. 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) 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 Loladze, Alexander Rodrigues, Francelino Augusto Toledo, Fernando San Vicente, Felix Gérard, Bruno Boddupalli, Maruthi Prasanna Application of Remote Sensing for Phenotyping Tar Spot Complex Resistance in Maize |
title | Application of Remote Sensing for Phenotyping Tar Spot Complex Resistance in Maize |
title_full | Application of Remote Sensing for Phenotyping Tar Spot Complex Resistance in Maize |
title_fullStr | Application of Remote Sensing for Phenotyping Tar Spot Complex Resistance in Maize |
title_full_unstemmed | Application of Remote Sensing for Phenotyping Tar Spot Complex Resistance in Maize |
title_short | Application of Remote Sensing for Phenotyping Tar Spot Complex Resistance in Maize |
title_sort | application of remote sensing for phenotyping tar spot complex resistance in maize |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6503115/ https://www.ncbi.nlm.nih.gov/pubmed/31114603 http://dx.doi.org/10.3389/fpls.2019.00552 |
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