Smart sensing of Leptinotarsa decemlineata infestations using a portable optical array

[EN] Leptinotarsa decemlineata is considered one of the most destructive coleopteran pests worldwide, due to its remarkable ability to adapt to diverse environmental conditions. This study presents the design and validation of an innovative, low-cost tool based on an optical array of 12 chemical sen...

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Detalles Bibliográficos
Autores: Gaviña, Pablo, Garcia-Robles, Inmaculada, Lopez-Galiano, M. Jose, Rausell, Carolina, Ros-Lis, José Vicente, Amoros, Pedro, Sánchez-Artero, Sheila
Tipo de recurso: artículo
Fecha de publicación:2026
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:dnet:riunet______::e524a7221a6c145148686bd372938f57
Acceso en línea:https://riunet.upv.es/handle/10251/233548
Access Level:acceso abierto
Palabra clave:E -nose
Pest,Insect
Leptinotarsa decemlineata
Optical array
ANN
PLS-DA
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spelling Smart sensing of Leptinotarsa decemlineata infestations using a portable optical arrayGaviña, PabloGarcia-Robles, InmaculadaLopez-Galiano, M. JoseRausell, CarolinaRos-Lis, José VicenteAmoros, PedroSánchez-Artero, SheilaE -nosePest,InsectLeptinotarsa decemlineataOptical arrayANNPLS-DA[EN] Leptinotarsa decemlineata is considered one of the most destructive coleopteran pests worldwide, due to its remarkable ability to adapt to diverse environmental conditions. This study presents the design and validation of an innovative, low-cost tool based on an optical array of 12 chemical sensors supported on materials for the specific detection of L. decemlineata infestation on Solanum tuberosum. Principal component analysis (PCA) evidenced the potential discriminative capacity of the system, showing a clear separation between infested and noninfested samples. Furthermore, models developed by Partial Least Squares Discriminant Analysis (PLS-DA) achieved optimal classification performance (accuracy, precision and sensitivity = 1.000), confirming the high efficiency of the system under controlled experimental conditions. Further validation using artificial neural network (ANN) models reinforced these findings, also obtaining satisfactory performance parameters and no evidence of overfitting, although data would benefit from a larger dataset. Overall, the results obtained support the potential of the optical array as a tool to support early detection of L. decemlineata, facilitating its implementation in integrated pest management (IPM) strategies in potato crops, and contributing to more accurate and sustainable decision-making in agriculture.This research was funded by the AGROALNEXT/2022/065 grant from Conselleria d'Innovacio, Universitats, Ciencia i Societat Digital (Generalitat Valenciana, Spain) and MCIN, NextGenerationEU (PRTR-C17.I1) Funds.ElsevierEuropean CommissionGeneralitat ValencianaRepositorio Institucional de la Universitat Politècnica de València Riunet20262026-03-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/233548reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengGeneralitat Valenciana https://doi.org/10.13039/501100003359 AGROALNEXT%2F2022%2F065open accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:dnet:riunet______::e524a7221a6c145148686bd372938f572026-06-13T07:49:27Z
dc.title.none.fl_str_mv Smart sensing of Leptinotarsa decemlineata infestations using a portable optical array
title Smart sensing of Leptinotarsa decemlineata infestations using a portable optical array
spellingShingle Smart sensing of Leptinotarsa decemlineata infestations using a portable optical array
Gaviña, Pablo
E -nose
Pest,Insect
Leptinotarsa decemlineata
Optical array
ANN
PLS-DA
title_short Smart sensing of Leptinotarsa decemlineata infestations using a portable optical array
title_full Smart sensing of Leptinotarsa decemlineata infestations using a portable optical array
title_fullStr Smart sensing of Leptinotarsa decemlineata infestations using a portable optical array
title_full_unstemmed Smart sensing of Leptinotarsa decemlineata infestations using a portable optical array
title_sort Smart sensing of Leptinotarsa decemlineata infestations using a portable optical array
dc.creator.none.fl_str_mv Gaviña, Pablo
Garcia-Robles, Inmaculada
Lopez-Galiano, M. Jose
Rausell, Carolina
Ros-Lis, José Vicente
Amoros, Pedro
Sánchez-Artero, Sheila
author Gaviña, Pablo
author_facet Gaviña, Pablo
Garcia-Robles, Inmaculada
Lopez-Galiano, M. Jose
Rausell, Carolina
Ros-Lis, José Vicente
Amoros, Pedro
Sánchez-Artero, Sheila
author_role author
author2 Garcia-Robles, Inmaculada
Lopez-Galiano, M. Jose
Rausell, Carolina
Ros-Lis, José Vicente
Amoros, Pedro
Sánchez-Artero, Sheila
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv European Commission
Generalitat Valenciana
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv E -nose
Pest,Insect
Leptinotarsa decemlineata
Optical array
ANN
PLS-DA
topic E -nose
Pest,Insect
Leptinotarsa decemlineata
Optical array
ANN
PLS-DA
description [EN] Leptinotarsa decemlineata is considered one of the most destructive coleopteran pests worldwide, due to its remarkable ability to adapt to diverse environmental conditions. This study presents the design and validation of an innovative, low-cost tool based on an optical array of 12 chemical sensors supported on materials for the specific detection of L. decemlineata infestation on Solanum tuberosum. Principal component analysis (PCA) evidenced the potential discriminative capacity of the system, showing a clear separation between infested and noninfested samples. Furthermore, models developed by Partial Least Squares Discriminant Analysis (PLS-DA) achieved optimal classification performance (accuracy, precision and sensitivity = 1.000), confirming the high efficiency of the system under controlled experimental conditions. Further validation using artificial neural network (ANN) models reinforced these findings, also obtaining satisfactory performance parameters and no evidence of overfitting, although data would benefit from a larger dataset. Overall, the results obtained support the potential of the optical array as a tool to support early detection of L. decemlineata, facilitating its implementation in integrated pest management (IPM) strategies in potato crops, and contributing to more accurate and sustainable decision-making in agriculture.
publishDate 2026
dc.date.none.fl_str_mv 2026
2026-03-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/233548
url https://riunet.upv.es/handle/10251/233548
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Generalitat Valenciana https://doi.org/10.13039/501100003359 AGROALNEXT%2F2022%2F065
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.812429

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