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Title of the item:

Detection of Two Different Grapevine Yellows in Vitis vinifera Using Hyperspectral Imaging

Title:
Detection of Two Different Grapevine Yellows in Vitis vinifera Using Hyperspectral Imaging
Authors:
Nele Bendel
Andreas Backhaus
Anna Kicherer
Janine Köckerling
Michael Maixner
Barbara Jarausch
Sandra Biancu
Hans-Christian Klück
Udo Seiffert
Ralf T. Voegele
Reinhard Töpfer
Subject Terms:
disease detection
plant phenotyping
spectral imaging
viticulture
phytoplasma
Bois noir
Science
Source:
Remote Sensing, Vol 12, Iss 24, p 4151 (2020)
Publisher:
MDPI AG, 2020.
Publication Year:
2020
Collection:
LCC:Science
Document Type:
article
File Description:
electronic resource
Language:
English
ISSN:
2072-4292
Relation:
https://www.mdpi.com/2072-4292/12/24/4151; https://doaj.org/toc/2072-4292
DOI:
10.3390/rs12244151
Access URL:
https://doaj.org/article/1fa96efa981643c7a330667c6abddfec  Link opens in a new window
Accession Number:
edsdoj.1fa96efa981643c7a330667c6abddfec
Academic Journal
Grapevine yellows (GY) are serious phytoplasma-caused diseases affecting viticultural areas worldwide. At present, two principal agents of GY are known to infest grapevines in Germany: Bois noir (BN) and Palatinate grapevine yellows (PGY). Disease management is mostly based on prophylactic measures as there are no curative in-field treatments available. In this context, sensor-based disease detection could be a useful tool for winegrowers. Therefore, hyperspectral imaging (400–2500 nm) was applied to identify phytoplasma-infected greenhouse plants and shoots collected in the field. Disease detection models (Radial-Basis Function Network) have successfully been developed for greenhouse plants of two white grapevine varieties infected with BN and PGY. Differentiation of symptomatic and healthy plants was possible reaching satisfying classification accuracies of up to 96%. However, identification of BN-infected but symptomless vines was difficult and needs further investigation. Regarding shoots collected in the field from different red and white varieties, correct classifications of up to 100% could be reached using a Multi-Layer Perceptron Network for analysis. Thus, hyperspectral imaging seems to be a promising approach for the detection of different GY. Moreover, the 10 most important wavelengths were identified for each disease detection approach, many of which could be found between 400 and 700 nm and in the short-wave infrared region (1585, 2135, and 2300 nm). These wavelengths could be used further to develop multispectral systems.
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