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Tytuł pozycji:

Evaluating the suitability of hyper- and multispectral imaging to detect foliar symptoms of the grapevine trunk disease Esca in vineyards

Tytuł :
Evaluating the suitability of hyper- and multispectral imaging to detect foliar symptoms of the grapevine trunk disease Esca in vineyards
Autorzy :
Nele Bendel
Anna Kicherer
Andreas Backhaus
Hans-Christian Klück
Udo Seiffert
Michael Fischer
Ralf T. Voegele
Reinhard Töpfer
Pokaż więcej
Temat :
Plant phenotyping
Grapevine trunk disease
Disease detection
Spectral imaging
Phenoliner
Phenotyping platform
Plant culture
SB1-1110
Biology (General)
QH301-705.5
Źródło :
Plant Methods, Vol 16, Iss 1, Pp 1-18 (2020)
Wydawca :
BMC, 2020.
Rok publikacji :
2020
Kolekcja :
LCC:Plant culture
LCC:Biology (General)
Typ dokumentu :
article
Opis pliku :
electronic resource
Język :
English
ISSN :
1746-4811
Relacje :
http://link.springer.com/article/10.1186/s13007-020-00685-3; https://doaj.org/toc/1746-4811
DOI :
10.1186/s13007-020-00685-3
Dostęp URL :
https://doaj.org/article/7cd7036c05b24b5884d966354a31d07b
Numer akcesji :
edsdoj.7cd7036c05b24b5884d966354a31d07b
Czasopismo naukowe
Abstract Background Grapevine trunk diseases (GTDs) such as Esca are among the most devastating threats to viticulture. Due to the lack of efficient preventive and curative treatments, Esca causes severe economic losses worldwide. Since symptoms do not develop consecutively, the true incidence of the disease in a vineyard is difficult to assess. Therefore, an annual monitoring is required. In this context, automatic detection of symptoms could be a great relief for winegrowers. Spectral sensors have proven to be successful in disease detection, allowing a non-destructive, objective, and fast data acquisition. The aim of this study is to evaluate the feasibility of the in-field detection of foliar Esca symptoms over three consecutive years using ground-based hyperspectral and airborne multispectral imaging. Results Hyperspectral disease detection models have been successfully developed using either original field data or manually annotated data. In a next step, these models were applied on plant scale. While the model using annotated data performed better during development, the model using original data showed higher classification accuracies when applied in practical work. Moreover, the transferability of disease detection models to unknown data was tested. Although the visible and near-infrared (VNIR) range showed promising results, the transfer of such models is challenging. Initial results indicate that external symptoms could be detected pre-symptomatically, but this needs further evaluation. Furthermore, an application specific multispectral approach was simulated by identifying the most important wavelengths for the differentiation tasks, which was then compared to real multispectral data. Even though the ground-based multispectral disease detection was successful, airborne detection remains difficult. Conclusions In this study, ground-based hyperspectral and airborne multispectral approaches for the detection of foliar Esca symptoms are presented. Both sensor systems seem to be suitable for the in-field detection of the disease, even though airborne data acquisition has to be further optimized. Our disease detection approaches could facilitate monitoring plant phenotypes in a vineyard.
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