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Detection of Laurel Wilt Disease in Avocado Using Low Altitude Aerial Imaging

Laurel wilt is a lethal disease of plants in the Lauraceae plant family, including avocado (Persea americana). This devastating disease has spread rapidly along the southeastern seaboard of the United States and has begun to affect commercial avocado production in Florida. The main objective of this...

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Autores principales: de Castro, Ana I., Ehsani, Reza, Ploetz, Randy C., Crane, Jonathan H., Buchanon, Sherrie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4415916/
https://www.ncbi.nlm.nih.gov/pubmed/25927209
http://dx.doi.org/10.1371/journal.pone.0124642
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author de Castro, Ana I.
Ehsani, Reza
Ploetz, Randy C.
Crane, Jonathan H.
Buchanon, Sherrie
author_facet de Castro, Ana I.
Ehsani, Reza
Ploetz, Randy C.
Crane, Jonathan H.
Buchanon, Sherrie
author_sort de Castro, Ana I.
collection PubMed
description Laurel wilt is a lethal disease of plants in the Lauraceae plant family, including avocado (Persea americana). This devastating disease has spread rapidly along the southeastern seaboard of the United States and has begun to affect commercial avocado production in Florida. The main objective of this study was to evaluate the potential to discriminate laurel wilt-affected avocado trees using aerial images taken with a modified camera during helicopter surveys at low-altitude in the commercial avocado production area. The ability to distinguish laurel wilt-affected trees from other factors that produce similar external symptoms was also studied. R(mod)GB digital values of healthy trees and laurel wilt-affected trees, as well as fruit stress and vines covering trees were used to calculate several vegetation indices (VIs), band ratios, and VI combinations. These indices were subjected to analysis of variance (ANOVA) and an M-statistic was performed in order to quantify the separability of those classes. Significant differences in spectral values among laurel wilt affected and healthy trees were observed in all vegetation indices calculated, although the best results were achieved with Excess Red (ExR), (Red–Green) and Combination 1 (COMB1) in all locations. B/G showed a very good potential for separate the other factors with symptoms similar to laurel wilt-affected trees, such as fruit stress and vines covering trees, from laurel wilt-affected trees. These consistent results prove the usefulness of using a modified camera (R(mod)GB) to discriminate laurel wilt-affected avocado trees from healthy trees, as well as from other factors that cause the same symptoms and suggest performing the classification in further research. According to our results, ExR and B/G should be utilized to develop an algorithm or decision rules to classify aerial images, since they showed the highest capacity to discriminate laurel wilt-affected trees. This methodology may allow the rapid detection of laurel wilt-affected trees using low altitude aerial images and be a valuable tool in mitigating this important threat to Florida avocado production.
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spelling pubmed-44159162015-05-07 Detection of Laurel Wilt Disease in Avocado Using Low Altitude Aerial Imaging de Castro, Ana I. Ehsani, Reza Ploetz, Randy C. Crane, Jonathan H. Buchanon, Sherrie PLoS One Research Article Laurel wilt is a lethal disease of plants in the Lauraceae plant family, including avocado (Persea americana). This devastating disease has spread rapidly along the southeastern seaboard of the United States and has begun to affect commercial avocado production in Florida. The main objective of this study was to evaluate the potential to discriminate laurel wilt-affected avocado trees using aerial images taken with a modified camera during helicopter surveys at low-altitude in the commercial avocado production area. The ability to distinguish laurel wilt-affected trees from other factors that produce similar external symptoms was also studied. R(mod)GB digital values of healthy trees and laurel wilt-affected trees, as well as fruit stress and vines covering trees were used to calculate several vegetation indices (VIs), band ratios, and VI combinations. These indices were subjected to analysis of variance (ANOVA) and an M-statistic was performed in order to quantify the separability of those classes. Significant differences in spectral values among laurel wilt affected and healthy trees were observed in all vegetation indices calculated, although the best results were achieved with Excess Red (ExR), (Red–Green) and Combination 1 (COMB1) in all locations. B/G showed a very good potential for separate the other factors with symptoms similar to laurel wilt-affected trees, such as fruit stress and vines covering trees, from laurel wilt-affected trees. These consistent results prove the usefulness of using a modified camera (R(mod)GB) to discriminate laurel wilt-affected avocado trees from healthy trees, as well as from other factors that cause the same symptoms and suggest performing the classification in further research. According to our results, ExR and B/G should be utilized to develop an algorithm or decision rules to classify aerial images, since they showed the highest capacity to discriminate laurel wilt-affected trees. This methodology may allow the rapid detection of laurel wilt-affected trees using low altitude aerial images and be a valuable tool in mitigating this important threat to Florida avocado production. Public Library of Science 2015-04-30 /pmc/articles/PMC4415916/ /pubmed/25927209 http://dx.doi.org/10.1371/journal.pone.0124642 Text en © 2015 de Castro 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
de Castro, Ana I.
Ehsani, Reza
Ploetz, Randy C.
Crane, Jonathan H.
Buchanon, Sherrie
Detection of Laurel Wilt Disease in Avocado Using Low Altitude Aerial Imaging
title Detection of Laurel Wilt Disease in Avocado Using Low Altitude Aerial Imaging
title_full Detection of Laurel Wilt Disease in Avocado Using Low Altitude Aerial Imaging
title_fullStr Detection of Laurel Wilt Disease in Avocado Using Low Altitude Aerial Imaging
title_full_unstemmed Detection of Laurel Wilt Disease in Avocado Using Low Altitude Aerial Imaging
title_short Detection of Laurel Wilt Disease in Avocado Using Low Altitude Aerial Imaging
title_sort detection of laurel wilt disease in avocado using low altitude aerial imaging
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4415916/
https://www.ncbi.nlm.nih.gov/pubmed/25927209
http://dx.doi.org/10.1371/journal.pone.0124642
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