Deep Learning for Vespa Velutina Detection

[EN]Vespa velutina, an invasive insect introduced to Europe from Asia, is the primary predator of honeybees, significantly contributing to the decline of their populations. Additionally, Vespa velutina has become a considerable threat to human health, as its sting can be lethal to individuals with a...

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Detalles Bibliográficos
Autores: Pérez-Delgado, María-Luisa, Román Gallego, Jesús Ángel
Tipo de recurso: capítulo de libro
Fecha de publicación:2024
País:España
Institución:Universidad de Salamanca (USAL)
Repositorio:GREDOS. Repositorio Institucional de la Universidad de Salamanca
OAI Identifier:oai:gredos.usal.es:10366/164733
Acceso en línea:http://hdl.handle.net/10366/164733
Access Level:acceso embargado
Palabra clave:Vespa Velutina
Convolutional neural networks
Artificial intelligence
Image recognition
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spelling Deep Learning for Vespa Velutina DetectionPérez-Delgado, María-LuisaRomán Gallego, Jesús ÁngelVespa VelutinaConvolutional neural networksArtificial intelligenceImage recognition[EN]Vespa velutina, an invasive insect introduced to Europe from Asia, is the primary predator of honeybees, significantly contributing to the decline of their populations. Additionally, Vespa velutina has become a considerable threat to human health, as its sting can be lethal to individuals with allergies. The invasion of Vespa velutina disrupts ecosystems by threatening biodiversity and preventing pollination processes, and it also incurs socioeconomic costs, including negative impacts on apiculture and associated management expenses. To address these challenges, it is essential to develop fast and user-friendly automatic identification tools for Vespa velutina. This study proposes to design an artificial intelligence model capable of recognizing and identifying Vespa velutina among various insects. Such a model would enable the creation of devices that can automatically transmit images and geolocations in real-time, thereby enhancing the response efficiency of relevant authorities. The results of this work demonstrate the feasibility of accurately recognizing Vespa velutina using artificial intelligence technology, which supports the implementation of automated systems that slow the spread of this invasive species and protect the beekeeping ecosysteminfo202520252024info:eu-repo/semantics/bookParthttp://hdl.handle.net/10366/164733reponame:GREDOS. Repositorio Institucional de la Universidad de Salamancainstname:Universidad de Salamanca (USAL)Inglésinfo:eu-repo/semantics/embargoedAccessoai:gredos.usal.es:10366/1647332026-06-07T06:28:51Z
dc.title.none.fl_str_mv Deep Learning for Vespa Velutina Detection
title Deep Learning for Vespa Velutina Detection
spellingShingle Deep Learning for Vespa Velutina Detection
Pérez-Delgado, María-Luisa
Vespa Velutina
Convolutional neural networks
Artificial intelligence
Image recognition
title_short Deep Learning for Vespa Velutina Detection
title_full Deep Learning for Vespa Velutina Detection
title_fullStr Deep Learning for Vespa Velutina Detection
title_full_unstemmed Deep Learning for Vespa Velutina Detection
title_sort Deep Learning for Vespa Velutina Detection
dc.creator.none.fl_str_mv Pérez-Delgado, María-Luisa
Román Gallego, Jesús Ángel
author Pérez-Delgado, María-Luisa
author_facet Pérez-Delgado, María-Luisa
Román Gallego, Jesús Ángel
author_role author
author2 Román Gallego, Jesús Ángel
author2_role author
dc.subject.none.fl_str_mv Vespa Velutina
Convolutional neural networks
Artificial intelligence
Image recognition
topic Vespa Velutina
Convolutional neural networks
Artificial intelligence
Image recognition
description [EN]Vespa velutina, an invasive insect introduced to Europe from Asia, is the primary predator of honeybees, significantly contributing to the decline of their populations. Additionally, Vespa velutina has become a considerable threat to human health, as its sting can be lethal to individuals with allergies. The invasion of Vespa velutina disrupts ecosystems by threatening biodiversity and preventing pollination processes, and it also incurs socioeconomic costs, including negative impacts on apiculture and associated management expenses. To address these challenges, it is essential to develop fast and user-friendly automatic identification tools for Vespa velutina. This study proposes to design an artificial intelligence model capable of recognizing and identifying Vespa velutina among various insects. Such a model would enable the creation of devices that can automatically transmit images and geolocations in real-time, thereby enhancing the response efficiency of relevant authorities. The results of this work demonstrate the feasibility of accurately recognizing Vespa velutina using artificial intelligence technology, which supports the implementation of automated systems that slow the spread of this invasive species and protect the beekeeping ecosystem
publishDate 2024
dc.date.none.fl_str_mv 2024
2025
2025
info
dc.type.none.fl_str_mv info:eu-repo/semantics/bookPart
format bookPart
dc.identifier.none.fl_str_mv http://hdl.handle.net/10366/164733
url http://hdl.handle.net/10366/164733
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/embargoedAccess
eu_rights_str_mv embargoedAccess
dc.source.none.fl_str_mv reponame:GREDOS. Repositorio Institucional de la Universidad de Salamanca
instname:Universidad de Salamanca (USAL)
instname_str Universidad de Salamanca (USAL)
reponame_str GREDOS. Repositorio Institucional de la Universidad de Salamanca
collection GREDOS. Repositorio Institucional de la Universidad de Salamanca
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.811543

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