Exploring the potential of hyperspectral imaging to detect Esca disease complex in asymptomatic grapevine leaves

Pérez Roncal, Claudia; Arazuri Garín, Silvia; López Molina, Carlos; Jarén Ceballos, Carmen; Santesteban García, Gonzaga; López Maestresalas, Ainara

Publication:
Exploring the potential of hyperspectral imaging to detect Esca disease complex in asymptomatic grapevine leaves

dc.contributor.author	Pérez Roncal, Claudia
dc.contributor.author	Arazuri Garín, Silvia
dc.contributor.author	López Molina, Carlos
dc.contributor.author	Jarén Ceballos, Carmen
dc.contributor.author	Santesteban García, Gonzaga
dc.contributor.author	López Maestresalas, Ainara
dc.contributor.department	Ingeniaritza	eu
dc.contributor.department	Estatistika, Informatika eta Matematika	eu
dc.contributor.department	Agronomia, Bioteknologia eta Elikadura	eu
dc.contributor.department	Institute on Innovation and Sustainable Development in Food Chain - ISFOOD	en
dc.contributor.department	Ingeniería	es_ES
dc.contributor.department	Estadística, Informática y Matemáticas	es_ES
dc.contributor.department	Agronomía, Biotecnología y Alimentación	es_ES
dc.contributor.funder	Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa	es
dc.date.accessioned	2022-05-25T12:19:22Z
dc.date.available	2022-05-25T12:19:22Z
dc.date.issued	2022
dc.date.updated	2022-05-25T12:17:50Z
dc.description.abstract	Precise and reliable identification of specific plant diseases is a challenge within precision agriculture nowadays. This is the case of esca, a complex grapevine trunk disease, that represents a major threat to modern viticulture as it is responsible for large economic losses annually. The lack of effective control strategies and the complexity of esca disease expression make essential the identification of affected plants, before symptoms become evident, for a better management of the vineyard. This study evaluated the suitability of a near-infrared hyperspectral imaging (HSI) system to detect esca disease in asymptomatic grapevine leaves of Tempranillo red-berried cultivar. For this, 72 leaves from an experimental vineyard, naturally infected with esca, were collected and scanned with a lab-scale HSI system in the 900-1700 nm spectral range. Then, effective image processing and multivariate analysis techniques were merged to develop pixel-based classification models for the distinction of healthy, asymptomatic and symptomatic leaves. Automatic and interval partial least squares variable selection methods were tested to identify the most relevant wavelengths for the detection of esca-affected vines using partial least squares discriminant analysis and different pre-processing techniques. Three-class and two-class classifiers were carried out to differentiate healthy, asymptomatic and symptomatic leaf pixels, and healthy from asymptomatic pixels, respectively. Both variable selection methods performed similarly, achieving good classification rates in the range of 82.77-97.17% in validation datasets for either three-class or two-class classifiers. The latter results demonstrated the capability of hyperspectral imaging to distinguish two groups of seemingly identical leaves (healthy and asymptomatic). These findings would ease the annual monitoring of disease incidence in the vineyard and, therefore, better crop management and decision making.	en
dc.description.sponsorship	This research was supported by Public University of Navarre postgraduate scholarships (FPI-UPNA-2017), by the Spanish Ministry of Economy and Competitiveness (AGL2017-83738-C3-1R, AEI/EU-FEDER), and by the Spanish Ministry of Science, Innovation and Universities (PID2019-108392GB-I00, AEI/10.13039/ 501100011033).	en
dc.format.extent	12 p.
dc.format.mimetype	application/pdf	en
dc.identifier.citation	Pérez-Roncal, C.; Arazuri, S.; Lopez-Molina, C.; Jarén, C.; Santesteban, L. G.; López-Maestresalas, A.. (2022). Exploring the potential of hyperspectral imaging to detect Esca disease complex in asymptomatic grapevine leaves. Computers and Electronics in Agriculture. 196,106863 1-12 .	en
dc.identifier.doi	10.1016/j.compag.2022.106863
dc.identifier.issn	0168-1699
dc.identifier.uri	https://academica-e.unavarra.es/handle/2454/43022
dc.language.iso	eng	en
dc.publisher	Elsevier	en
dc.relation.ispartof	Computers And Electronics In Agriculture, 2022, 196 (106863),1-12	en
dc.relation.projectID	info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/AGL2017-83738-C3-1-R/ES/	en
dc.relation.projectID	info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108392GB-I00/ES/	en
dc.relation.publisherversion	https://doi.org/10.1016/j.compag.2022.106863
dc.rights	© 2022 The Authors. This is an open access article under the CC BY-NC-ND license	en
dc.rights.accessRights	info:eu-repo/semantics/openAccess	en
dc.rights.accessRights	Acceso abierto / Sarbide irekia	es
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject	Hyperspectral imaging	en
dc.subject	Disease detection	en
dc.subject	Grapevine trunk disease	en
dc.subject	Precision viticulture	en
dc.subject	Pixel-based classification	en
dc.title	Exploring the potential of hyperspectral imaging to detect Esca disease complex in asymptomatic grapevine leaves	en
dc.type	info:eu-repo/semantics/article	en
dc.type	Artículo / Artikulua	es
dc.type.version	info:eu-repo/semantics/publishedVersion	en
dc.type.version	Versión publicada / Argitaratu den bertsioa	es
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