A comparative study between non-linear regression and artificial neural network approaches for modelling wild oat (Avena fatua) field emergence

Autores
Chantre Balacca, Guillermo Ruben; Blanco, Anibal Manuel; Forcella, F.; Van Acker, R. C.; Sabbatini, Mario Ricardo; González Andújar, J. L.
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Non-linear regression (NLR) techniques are used widely to fit weed field emergence patterns to soil microclimatic indices using S-type functions. Artificial neural networks (ANNs) present interesting and alternative features for such modelling purposes. In the present work, a univariate hydrothermal-time based Weibull model and a bivariate (hydro-time and thermal-time) ANN were developed to study wild oat emergence under non-moisture restriction conditions using data from different locations worldwide. Results indicated a higher accuracy of the neural network in comparison with the NLR approach due to the improved descriptive capacity of thermal-time and the hydro-time as independent explanatory variables. The bivariate ANN model outperformed the con- ventional Weibull approach, in terms of RMSE of the test set, by 70·8%. These outcomes suggest the potential applicability of the proposed modelling approach in the design of weed management decision support systems.
Fil: Chantre Balacca, Guillermo Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida(i); Argentina
Fil: Blanco, Anibal Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Planta Piloto de Ingeniería Química (i); Argentina
Fil: Forcella, F.. United States Department Of Agriculture. Agricultural Research Service; Argentina
Fil: Van Acker, R. C.. University Of Guelph; Canadá
Fil: Sabbatini, Mario Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida(i); Argentina
Fil: González Andújar, J. L.. Consejo Superior de Investigaciones Cientificas. Instituto de Agricultura Sostenible; España
Materia
Weed Emergence Models
Hydrothermal-Time
Hydro-Time
Thermal-Time
Weibull Model
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/12718
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/12718
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A comparative study between non-linear regression and artificial neural network approaches for modelling wild oat (Avena fatua) field emergenceChantre Balacca, Guillermo RubenBlanco, Anibal ManuelForcella, F.Van Acker, R. C.Sabbatini, Mario RicardoGonzález Andújar, J. L.Weed Emergence ModelsHydrothermal-TimeHydro-TimeThermal-TimeWeibull Modelhttps://purl.org/becyt/ford/4.1https://purl.org/becyt/ford/4Non-linear regression (NLR) techniques are used widely to fit weed field emergence patterns to soil microclimatic indices using S-type functions. Artificial neural networks (ANNs) present interesting and alternative features for such modelling purposes. In the present work, a univariate hydrothermal-time based Weibull model and a bivariate (hydro-time and thermal-time) ANN were developed to study wild oat emergence under non-moisture restriction conditions using data from different locations worldwide. Results indicated a higher accuracy of the neural network in comparison with the NLR approach due to the improved descriptive capacity of thermal-time and the hydro-time as independent explanatory variables. The bivariate ANN model outperformed the con- ventional Weibull approach, in terms of RMSE of the test set, by 70·8%. These outcomes suggest the potential applicability of the proposed modelling approach in the design of weed management decision support systems.Fil: Chantre Balacca, Guillermo Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida(i); ArgentinaFil: Blanco, Anibal Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Planta Piloto de Ingeniería Química (i); ArgentinaFil: Forcella, F.. United States Department Of Agriculture. Agricultural Research Service; ArgentinaFil: Van Acker, R. C.. University Of Guelph; CanadáFil: Sabbatini, Mario Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida(i); ArgentinaFil: González Andújar, J. L.. Consejo Superior de Investigaciones Cientificas. Instituto de Agricultura Sostenible; EspañaCambridge University Press2013-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/12718Chantre Balacca, Guillermo Ruben; Blanco, Anibal Manuel; Forcella, F.; Van Acker, R. C.; Sabbatini, Mario Ricardo; et al.; A comparative study between non-linear regression and artificial neural network approaches for modelling wild oat (Avena fatua) field emergence; Cambridge University Press; Journal Of Agricultural Science; 152; 2; 1-2013; 254-2620021-8596enginfo:eu-repo/semantics/altIdentifier/doi/10.1017/S0021859612001098info:eu-repo/semantics/altIdentifier/url/https://www.cambridge.org/core/journals/journal-of-agricultural-science/article/div-classtitlea-comparative-study-between-non-linear-regression-and-artificial-neural-network-approaches-for-modelling-wild-oat-span-classitalicavena-fatuaspan-field-emergencediv/A3592A37A45503BEE582E8CFEFA78313info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:56:25Zoai:ri.conicet.gov.ar:11336/12718instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:56:26.016CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A comparative study between non-linear regression and artificial neural network approaches for modelling wild oat (Avena fatua) field emergence
title A comparative study between non-linear regression and artificial neural network approaches for modelling wild oat (Avena fatua) field emergence
spellingShingle A comparative study between non-linear regression and artificial neural network approaches for modelling wild oat (Avena fatua) field emergence
Chantre Balacca, Guillermo Ruben
Weed Emergence Models
Hydrothermal-Time
Hydro-Time
Thermal-Time
Weibull Model
title_short A comparative study between non-linear regression and artificial neural network approaches for modelling wild oat (Avena fatua) field emergence
title_full A comparative study between non-linear regression and artificial neural network approaches for modelling wild oat (Avena fatua) field emergence
title_fullStr A comparative study between non-linear regression and artificial neural network approaches for modelling wild oat (Avena fatua) field emergence
title_full_unstemmed A comparative study between non-linear regression and artificial neural network approaches for modelling wild oat (Avena fatua) field emergence
title_sort A comparative study between non-linear regression and artificial neural network approaches for modelling wild oat (Avena fatua) field emergence
dc.creator.none.fl_str_mv Chantre Balacca, Guillermo Ruben
Blanco, Anibal Manuel
Forcella, F.
Van Acker, R. C.
Sabbatini, Mario Ricardo
González Andújar, J. L.
author Chantre Balacca, Guillermo Ruben
author_facet Chantre Balacca, Guillermo Ruben
Blanco, Anibal Manuel
Forcella, F.
Van Acker, R. C.
Sabbatini, Mario Ricardo
González Andújar, J. L.
author_role author
author2 Blanco, Anibal Manuel
Forcella, F.
Van Acker, R. C.
Sabbatini, Mario Ricardo
González Andújar, J. L.
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Weed Emergence Models
Hydrothermal-Time
Hydro-Time
Thermal-Time
Weibull Model
topic Weed Emergence Models
Hydrothermal-Time
Hydro-Time
Thermal-Time
Weibull Model
purl_subject.fl_str_mv https://purl.org/becyt/ford/4.1
https://purl.org/becyt/ford/4
dc.description.none.fl_txt_mv Non-linear regression (NLR) techniques are used widely to fit weed field emergence patterns to soil microclimatic indices using S-type functions. Artificial neural networks (ANNs) present interesting and alternative features for such modelling purposes. In the present work, a univariate hydrothermal-time based Weibull model and a bivariate (hydro-time and thermal-time) ANN were developed to study wild oat emergence under non-moisture restriction conditions using data from different locations worldwide. Results indicated a higher accuracy of the neural network in comparison with the NLR approach due to the improved descriptive capacity of thermal-time and the hydro-time as independent explanatory variables. The bivariate ANN model outperformed the con- ventional Weibull approach, in terms of RMSE of the test set, by 70·8%. These outcomes suggest the potential applicability of the proposed modelling approach in the design of weed management decision support systems.
Fil: Chantre Balacca, Guillermo Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida(i); Argentina
Fil: Blanco, Anibal Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Planta Piloto de Ingeniería Química (i); Argentina
Fil: Forcella, F.. United States Department Of Agriculture. Agricultural Research Service; Argentina
Fil: Van Acker, R. C.. University Of Guelph; Canadá
Fil: Sabbatini, Mario Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida(i); Argentina
Fil: González Andújar, J. L.. Consejo Superior de Investigaciones Cientificas. Instituto de Agricultura Sostenible; España
description Non-linear regression (NLR) techniques are used widely to fit weed field emergence patterns to soil microclimatic indices using S-type functions. Artificial neural networks (ANNs) present interesting and alternative features for such modelling purposes. In the present work, a univariate hydrothermal-time based Weibull model and a bivariate (hydro-time and thermal-time) ANN were developed to study wild oat emergence under non-moisture restriction conditions using data from different locations worldwide. Results indicated a higher accuracy of the neural network in comparison with the NLR approach due to the improved descriptive capacity of thermal-time and the hydro-time as independent explanatory variables. The bivariate ANN model outperformed the con- ventional Weibull approach, in terms of RMSE of the test set, by 70·8%. These outcomes suggest the potential applicability of the proposed modelling approach in the design of weed management decision support systems.
publishDate 2013
dc.date.none.fl_str_mv 2013-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/12718
Chantre Balacca, Guillermo Ruben; Blanco, Anibal Manuel; Forcella, F.; Van Acker, R. C.; Sabbatini, Mario Ricardo; et al.; A comparative study between non-linear regression and artificial neural network approaches for modelling wild oat (Avena fatua) field emergence; Cambridge University Press; Journal Of Agricultural Science; 152; 2; 1-2013; 254-262
0021-8596
url http://hdl.handle.net/11336/12718
identifier_str_mv Chantre Balacca, Guillermo Ruben; Blanco, Anibal Manuel; Forcella, F.; Van Acker, R. C.; Sabbatini, Mario Ricardo; et al.; A comparative study between non-linear regression and artificial neural network approaches for modelling wild oat (Avena fatua) field emergence; Cambridge University Press; Journal Of Agricultural Science; 152; 2; 1-2013; 254-262
0021-8596
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1017/S0021859612001098
info:eu-repo/semantics/altIdentifier/url/https://www.cambridge.org/core/journals/journal-of-agricultural-science/article/div-classtitlea-comparative-study-between-non-linear-regression-and-artificial-neural-network-approaches-for-modelling-wild-oat-span-classitalicavena-fatuaspan-field-emergencediv/A3592A37A45503BEE582E8CFEFA78313
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Cambridge University Press
publisher.none.fl_str_mv Cambridge University Press
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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