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Detection of Powdery Mildew in Two Winter Wheat Plant Densities and Prediction of Grain Yield Using Canopy Hyperspectral Reflectance

To determine the influence of plant density and powdery mildew infection of winter wheat and to predict grain yield, hyperspectral canopy reflectance of winter wheat was measured for two plant densities at Feekes growth stage (GS) 10.5.3, 10.5.4, and 11.1 in the 2009–2010 and 2010–2011 seasons. Refl...

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Autores principales: Cao, Xueren, Luo, Yong, Zhou, Yilin, Fan, Jieru, Xu, Xiangming, West, Jonathan S., Duan, Xiayu, Cheng, Dengfa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4376796/
https://www.ncbi.nlm.nih.gov/pubmed/25815468
http://dx.doi.org/10.1371/journal.pone.0121462
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author Cao, Xueren
Luo, Yong
Zhou, Yilin
Fan, Jieru
Xu, Xiangming
West, Jonathan S.
Duan, Xiayu
Cheng, Dengfa
author_facet Cao, Xueren
Luo, Yong
Zhou, Yilin
Fan, Jieru
Xu, Xiangming
West, Jonathan S.
Duan, Xiayu
Cheng, Dengfa
author_sort Cao, Xueren
collection PubMed
description To determine the influence of plant density and powdery mildew infection of winter wheat and to predict grain yield, hyperspectral canopy reflectance of winter wheat was measured for two plant densities at Feekes growth stage (GS) 10.5.3, 10.5.4, and 11.1 in the 2009–2010 and 2010–2011 seasons. Reflectance in near infrared (NIR) regions was significantly correlated with disease index at GS 10.5.3, 10.5.4, and 11.1 at two plant densities in both seasons. For the two plant densities, the area of the red edge peak (Σdr (680–760 nm)), difference vegetation index (DVI), and triangular vegetation index (TVI) were significantly correlated negatively with disease index at three GSs in two seasons. Compared with other parameters Σdr (680–760 nm) was the most sensitive parameter for detecting powdery mildew. Linear regression models relating mildew severity to Σdr (680–760 nm) were constructed at three GSs in two seasons for the two plant densities, demonstrating no significant difference in the slope estimates between the two plant densities at three GSs. Σdr (680–760 nm) was correlated with grain yield at three GSs in two seasons. The accuracies of partial least square regression (PLSR) models were consistently higher than those of models based on Σdr (680760 nm) for disease index and grain yield. PLSR can, therefore, provide more accurate estimation of disease index of wheat powdery mildew and grain yield using canopy reflectance.
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spelling pubmed-43767962015-04-04 Detection of Powdery Mildew in Two Winter Wheat Plant Densities and Prediction of Grain Yield Using Canopy Hyperspectral Reflectance Cao, Xueren Luo, Yong Zhou, Yilin Fan, Jieru Xu, Xiangming West, Jonathan S. Duan, Xiayu Cheng, Dengfa PLoS One Research Article To determine the influence of plant density and powdery mildew infection of winter wheat and to predict grain yield, hyperspectral canopy reflectance of winter wheat was measured for two plant densities at Feekes growth stage (GS) 10.5.3, 10.5.4, and 11.1 in the 2009–2010 and 2010–2011 seasons. Reflectance in near infrared (NIR) regions was significantly correlated with disease index at GS 10.5.3, 10.5.4, and 11.1 at two plant densities in both seasons. For the two plant densities, the area of the red edge peak (Σdr (680–760 nm)), difference vegetation index (DVI), and triangular vegetation index (TVI) were significantly correlated negatively with disease index at three GSs in two seasons. Compared with other parameters Σdr (680–760 nm) was the most sensitive parameter for detecting powdery mildew. Linear regression models relating mildew severity to Σdr (680–760 nm) were constructed at three GSs in two seasons for the two plant densities, demonstrating no significant difference in the slope estimates between the two plant densities at three GSs. Σdr (680–760 nm) was correlated with grain yield at three GSs in two seasons. The accuracies of partial least square regression (PLSR) models were consistently higher than those of models based on Σdr (680760 nm) for disease index and grain yield. PLSR can, therefore, provide more accurate estimation of disease index of wheat powdery mildew and grain yield using canopy reflectance. Public Library of Science 2015-03-27 /pmc/articles/PMC4376796/ /pubmed/25815468 http://dx.doi.org/10.1371/journal.pone.0121462 Text en © 2015 Cao et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Cao, Xueren
Luo, Yong
Zhou, Yilin
Fan, Jieru
Xu, Xiangming
West, Jonathan S.
Duan, Xiayu
Cheng, Dengfa
Detection of Powdery Mildew in Two Winter Wheat Plant Densities and Prediction of Grain Yield Using Canopy Hyperspectral Reflectance
title Detection of Powdery Mildew in Two Winter Wheat Plant Densities and Prediction of Grain Yield Using Canopy Hyperspectral Reflectance
title_full Detection of Powdery Mildew in Two Winter Wheat Plant Densities and Prediction of Grain Yield Using Canopy Hyperspectral Reflectance
title_fullStr Detection of Powdery Mildew in Two Winter Wheat Plant Densities and Prediction of Grain Yield Using Canopy Hyperspectral Reflectance
title_full_unstemmed Detection of Powdery Mildew in Two Winter Wheat Plant Densities and Prediction of Grain Yield Using Canopy Hyperspectral Reflectance
title_short Detection of Powdery Mildew in Two Winter Wheat Plant Densities and Prediction of Grain Yield Using Canopy Hyperspectral Reflectance
title_sort detection of powdery mildew in two winter wheat plant densities and prediction of grain yield using canopy hyperspectral reflectance
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4376796/
https://www.ncbi.nlm.nih.gov/pubmed/25815468
http://dx.doi.org/10.1371/journal.pone.0121462
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