Predicting the Risk of Verticillium Wilt in Olive Orchards Using Fuzzy Logic

Developing models to understand disease dynamics and predict the risk of disease outbreaks to facilitate decision making is an integral component of plant disease management. However, these models have not yet been developed for one of the most damaging diseases in Mediterranean olive-growing areas...

ver descrição completa

Detalhes bibliográficos
Autores: López Escudero, Francisco Javier, Romero Rodríguez, Joaquín, Bocanegra Caro, Rocío, Santos Rufo, Antonio
Formato: artículo
Fecha de publicación:2023
País:España
Recursos:Universidad de Huelva (UHU)
Repositorio:Arias Montano. Repositorio Institucional de la Universidad de Huelva
Idioma:inglés
OAI Identifier:oai:ariasmontano.uhu.es:10272/23927
Acesso em linha:https://hdl.handle.net/10272/23927
Access Level:acceso abierto
Palavra-chave:Disease risk prediction
Fuzzy logic
Inoculum density
Isothermality
Machine learning
Resistance
Verticillium dahliae
3108 Fitopatología
id ES_ac7087e110f1cd24a827dc3cfaf7784e
oai_identifier_str oai:ariasmontano.uhu.es:10272/23927
network_acronym_str ES
network_name_str España
repository_id_str
spelling Predicting the Risk of Verticillium Wilt in Olive Orchards Using Fuzzy LogicLópez Escudero, Francisco JavierRomero Rodríguez, JoaquínBocanegra Caro, RocíoSantos Rufo, AntonioDisease risk predictionFuzzy logicInoculum densityIsothermalityMachine learningResistanceVerticillium dahliae3108 FitopatologíaDeveloping models to understand disease dynamics and predict the risk of disease outbreaks to facilitate decision making is an integral component of plant disease management. However, these models have not yet been developed for one of the most damaging diseases in Mediterranean olive-growing areas (verticillium wilt (VW), caused by the fungus Verticillium dahliae Kleb.), although there are parameters (e.g., level of V. dahliae inoculum in the soil, level of susceptibility of the olive cultivar, isothermality, coefficient of variation of seasonal precipitation, etc.) that have previously been correlated with the severity of the disease. Using the data from previous VW studies conducted in the Guadalquivir Valley of Andalusia (one of the most damaged areas worldwide), in this work, a set of fuzzy logic (FL) models is developed with the aforementioned disease and climatic parameters, and the results are compared with machine learning (ML) models, of known effectiveness, to predict the risk levels of VW appearance in an olive orchard. Under these conditions, both groups of models were less effective than those previously studied with simpler models or models used under controlled conditions. However, the accuracy achieved with the most efficient FL model (60%; classification system based on fuzzy rules using the Ishibuchi method with a weighting factor) was somewhat greater than the efficiency achieved with the most efficient ML model (59.0%; decision tree classifier), in addition to being more appropriate (from a practical point of view) for the incorporation into a decision support system by allowing the risk of appearance of each observation to be known by providing rules for each of the combinations of the different parameters with similar precision. Therefore, in this study, we propose the FL methodology as suitable to act as an expert system for the future creation of a decision support system for VW in olives.MDPI20232023-11-0120232023-11-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10272/23927reponame:Arias Montano. Repositorio Institucional de la Universidad de Huelvainstname:Universidad de Huelva (UHU)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Atribución-NoComercial-SinDerivadas 3.0 Españahttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:ariasmontano.uhu.es:10272/239272026-06-02T14:58:11Z
dc.title.none.fl_str_mv Predicting the Risk of Verticillium Wilt in Olive Orchards Using Fuzzy Logic
title Predicting the Risk of Verticillium Wilt in Olive Orchards Using Fuzzy Logic
spellingShingle Predicting the Risk of Verticillium Wilt in Olive Orchards Using Fuzzy Logic
López Escudero, Francisco Javier
Disease risk prediction
Fuzzy logic
Inoculum density
Isothermality
Machine learning
Resistance
Verticillium dahliae
3108 Fitopatología
title_short Predicting the Risk of Verticillium Wilt in Olive Orchards Using Fuzzy Logic
title_full Predicting the Risk of Verticillium Wilt in Olive Orchards Using Fuzzy Logic
title_fullStr Predicting the Risk of Verticillium Wilt in Olive Orchards Using Fuzzy Logic
title_full_unstemmed Predicting the Risk of Verticillium Wilt in Olive Orchards Using Fuzzy Logic
title_sort Predicting the Risk of Verticillium Wilt in Olive Orchards Using Fuzzy Logic
dc.creator.none.fl_str_mv López Escudero, Francisco Javier
Romero Rodríguez, Joaquín
Bocanegra Caro, Rocío
Santos Rufo, Antonio
author López Escudero, Francisco Javier
author_facet López Escudero, Francisco Javier
Romero Rodríguez, Joaquín
Bocanegra Caro, Rocío
Santos Rufo, Antonio
author_role author
author2 Romero Rodríguez, Joaquín
Bocanegra Caro, Rocío
Santos Rufo, Antonio
author2_role author
author
author
dc.contributor.none.fl_str_mv
dc.subject.none.fl_str_mv Disease risk prediction
Fuzzy logic
Inoculum density
Isothermality
Machine learning
Resistance
Verticillium dahliae
3108 Fitopatología
topic Disease risk prediction
Fuzzy logic
Inoculum density
Isothermality
Machine learning
Resistance
Verticillium dahliae
3108 Fitopatología
description Developing models to understand disease dynamics and predict the risk of disease outbreaks to facilitate decision making is an integral component of plant disease management. However, these models have not yet been developed for one of the most damaging diseases in Mediterranean olive-growing areas (verticillium wilt (VW), caused by the fungus Verticillium dahliae Kleb.), although there are parameters (e.g., level of V. dahliae inoculum in the soil, level of susceptibility of the olive cultivar, isothermality, coefficient of variation of seasonal precipitation, etc.) that have previously been correlated with the severity of the disease. Using the data from previous VW studies conducted in the Guadalquivir Valley of Andalusia (one of the most damaged areas worldwide), in this work, a set of fuzzy logic (FL) models is developed with the aforementioned disease and climatic parameters, and the results are compared with machine learning (ML) models, of known effectiveness, to predict the risk levels of VW appearance in an olive orchard. Under these conditions, both groups of models were less effective than those previously studied with simpler models or models used under controlled conditions. However, the accuracy achieved with the most efficient FL model (60%; classification system based on fuzzy rules using the Ishibuchi method with a weighting factor) was somewhat greater than the efficiency achieved with the most efficient ML model (59.0%; decision tree classifier), in addition to being more appropriate (from a practical point of view) for the incorporation into a decision support system by allowing the risk of appearance of each observation to be known by providing rules for each of the combinations of the different parameters with similar precision. Therefore, in this study, we propose the FL methodology as suitable to act as an expert system for the future creation of a decision support system for VW in olives.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-11-01
2023
2023-11-01
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://hdl.handle.net/10272/23927
url https://hdl.handle.net/10272/23927
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
Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
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
Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Arias Montano. Repositorio Institucional de la Universidad de Huelva
instname:Universidad de Huelva (UHU)
instname_str Universidad de Huelva (UHU)
reponame_str Arias Montano. Repositorio Institucional de la Universidad de Huelva
collection Arias Montano. Repositorio Institucional de la Universidad de Huelva
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869416354857091072
score 15,812455

Registos relacionados