A novel method based on hybrid deep learning with explainability for olive fruit pest forecasting

Predicting the occurrence of crop pests is becoming a crucial task in modern agriculture to facilitate farmers’ decision-making. One of the most significant pests is the olive fruit fly, a public concern because it causes damage that compromises oil quality, increasing acidity and altering its flavo...

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Detalhes bibliográficos
Autores: Chacón Maldonado, Andrés Manuel, Melgar García, Laura, Asencio Cortés, Gualberto, Troncoso, Alicia
Formato: artículo
Fecha de publicación:2024
País:España
Recursos:Universidad Pablo de Olavide (UPO)
Repositorio:RIO. Repositorio Institucional Olavide
Idioma:inglés
OAI Identifier:oai:rio.upo.es:10433/22170
Acesso em linha:https://hdl.handle.net/10433/22170
Access Level:acceso abierto
Palavra-chave:Deep learning
Explainable artificial intelligence
Agriculture
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spelling A novel method based on hybrid deep learning with explainability for olive fruit pest forecastingChacón Maldonado, Andrés ManuelMelgar García, LauraAsencio Cortés, GualbertoTroncoso, AliciaDeep learningExplainable artificial intelligenceAgriculturePredicting the occurrence of crop pests is becoming a crucial task in modern agriculture to facilitate farmers’ decision-making. One of the most significant pests is the olive fruit fly, a public concern because it causes damage that compromises oil quality, increasing acidity and altering its flavor. This paper proposes a hybrid deep learning model to predict the presence of olive flies in crops. This model is based on an autoencoder and an automated deep feed-forward neural network. First, the autoencoder neural network learns a representation of the data and then the automated deep feed-forward neural network automatically determines the best values for the hyperparameters in order to obtain the prediction of the number of flies caught in traps from the dataset generated by the autoencoder. On the other hand, farmers to trust the proposed deep learning models need these models to be explainable. Thus, explainable artificial intelligence techniques are applied to the produced models to interpret the results. Results using a dataset from different sources such as satellite image band data, vegetation indices, and meteorological variables are reported. The performance of the proposed model has been compared with classical benchmark algorithms and a deep learning model recently published in the literature. In addition, the comparison includes the automated deep feed-forward neural network individually to show how the autoencoder network improves the accuracy of predictions.Springer20242024-12-2320242024-12-1220242024-12-12journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10433/22170reponame:RIO. Repositorio Institucional Olavideinstname:Universidad Pablo de Olavide (UPO)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-ShareAlike 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccessoai:rio.upo.es:10433/221702026-06-13T12:46:27Z
dc.title.none.fl_str_mv A novel method based on hybrid deep learning with explainability for olive fruit pest forecasting
title A novel method based on hybrid deep learning with explainability for olive fruit pest forecasting
spellingShingle A novel method based on hybrid deep learning with explainability for olive fruit pest forecasting
Chacón Maldonado, Andrés Manuel
Deep learning
Explainable artificial intelligence
Agriculture
title_short A novel method based on hybrid deep learning with explainability for olive fruit pest forecasting
title_full A novel method based on hybrid deep learning with explainability for olive fruit pest forecasting
title_fullStr A novel method based on hybrid deep learning with explainability for olive fruit pest forecasting
title_full_unstemmed A novel method based on hybrid deep learning with explainability for olive fruit pest forecasting
title_sort A novel method based on hybrid deep learning with explainability for olive fruit pest forecasting
dc.creator.none.fl_str_mv Chacón Maldonado, Andrés Manuel
Melgar García, Laura
Asencio Cortés, Gualberto
Troncoso, Alicia
author Chacón Maldonado, Andrés Manuel
author_facet Chacón Maldonado, Andrés Manuel
Melgar García, Laura
Asencio Cortés, Gualberto
Troncoso, Alicia
author_role author
author2 Melgar García, Laura
Asencio Cortés, Gualberto
Troncoso, Alicia
author2_role author
author
author
dc.contributor.none.fl_str_mv
dc.subject.none.fl_str_mv Deep learning
Explainable artificial intelligence
Agriculture
topic Deep learning
Explainable artificial intelligence
Agriculture
description Predicting the occurrence of crop pests is becoming a crucial task in modern agriculture to facilitate farmers’ decision-making. One of the most significant pests is the olive fruit fly, a public concern because it causes damage that compromises oil quality, increasing acidity and altering its flavor. This paper proposes a hybrid deep learning model to predict the presence of olive flies in crops. This model is based on an autoencoder and an automated deep feed-forward neural network. First, the autoencoder neural network learns a representation of the data and then the automated deep feed-forward neural network automatically determines the best values for the hyperparameters in order to obtain the prediction of the number of flies caught in traps from the dataset generated by the autoencoder. On the other hand, farmers to trust the proposed deep learning models need these models to be explainable. Thus, explainable artificial intelligence techniques are applied to the produced models to interpret the results. Results using a dataset from different sources such as satellite image band data, vegetation indices, and meteorological variables are reported. The performance of the proposed model has been compared with classical benchmark algorithms and a deep learning model recently published in the literature. In addition, the comparison includes the automated deep feed-forward neural network individually to show how the autoencoder network improves the accuracy of predictions.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-12-23
2024
2024-12-12
2024
2024-12-12
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://hdl.handle.net/10433/22170
url https://hdl.handle.net/10433/22170
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-ShareAlike 4.0 International
http://creativecommons.org/licenses/by-nc-sa/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
Attribution-NonCommercial-ShareAlike 4.0 International
http://creativecommons.org/licenses/by-nc-sa/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:RIO. Repositorio Institucional Olavide
instname:Universidad Pablo de Olavide (UPO)
instname_str Universidad Pablo de Olavide (UPO)
reponame_str RIO. Repositorio Institucional Olavide
collection RIO. Repositorio Institucional Olavide
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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