Digital platform for experimental and technical support to the cultivation of cactus pear

Detalhes bibliográficos
Autor(a) principal: Guimarães, Bruno Vinícius Castro
Data de Publicação: 2022
Outros Autores: Donato, Sérgio Luiz Rodrigues, Aspiazú, Ignacio, Azevedo, Alcinei Mistico, Lima, Fábio dos Santos, Macêdo, Samuel Victor Medeiros de, Brito, Cleiton Fernando Barbosa, Couto, Hiago Fagundes
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Acta Scientiarum. Agronomy (Online)
Texto Completo: http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/57407
Resumo: Among the forage species, especially in semiarid ecosystems, cactus pear is exceptional because of its high tolerance to adverse conditions and high productivity. Due to this alone, several studies have been conducted to identify the main technologies for this crop. Despite being consolidated and integrated, the cactus pear production system has limited accessibility, technical assistance, and availability of information for those dedicated to its production. This study aimed to present a digital platform, website, and applications to provide technical information on the cactus pear and demonstrate the efficiency of these applications through experimental data. On this digital platform, applications were made available for predicting the productivity of cactus pear using artificial neural networks (ANN) on a computer with routines in the R software and by simple linear regression (SLR) on smartphones on the Android system of the MIT App Inventor 2 platform. In addition, using the smartphone app, it is possible to obtain the cladode area through multiple linear regression (MLR). It is also possible to obtain the estimates of the experimental plot sizes by the maximum modified curvature, linear and quadratic methods with plateau response, relative information, comparison of variances, and convenient plot size. The platform provides technical information associated with the cactus pear crop from different sources (dissertations, theses, articles) and formats (video classes and teaching resources), offline for applications, and online with download for publications, dissertations, theses and articles, video classes, and several didactic resources. The biomathematical models integrated with the applications were highly precise in predicting the phenomena, in which the variation explained by the models in the prediction of responses for future observations had R² values of 0.95, 0.72, and 0.92, respectively, for productivity with computer-ANN and smartphone-SLR, and for the cladode area with a smartphone - MLR.
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spelling Digital platform for experimental and technical support to the cultivation of cactus pearDigital platform for experimental and technical support to the cultivation of cactus peardigital agriculture 4.0; artificial intelligence; smartphone; online and offline models; Opuntia sp.digital agriculture 4.0; artificial intelligence; smartphone; online and offline models; Opuntia sp.Among the forage species, especially in semiarid ecosystems, cactus pear is exceptional because of its high tolerance to adverse conditions and high productivity. Due to this alone, several studies have been conducted to identify the main technologies for this crop. Despite being consolidated and integrated, the cactus pear production system has limited accessibility, technical assistance, and availability of information for those dedicated to its production. This study aimed to present a digital platform, website, and applications to provide technical information on the cactus pear and demonstrate the efficiency of these applications through experimental data. On this digital platform, applications were made available for predicting the productivity of cactus pear using artificial neural networks (ANN) on a computer with routines in the R software and by simple linear regression (SLR) on smartphones on the Android system of the MIT App Inventor 2 platform. In addition, using the smartphone app, it is possible to obtain the cladode area through multiple linear regression (MLR). It is also possible to obtain the estimates of the experimental plot sizes by the maximum modified curvature, linear and quadratic methods with plateau response, relative information, comparison of variances, and convenient plot size. The platform provides technical information associated with the cactus pear crop from different sources (dissertations, theses, articles) and formats (video classes and teaching resources), offline for applications, and online with download for publications, dissertations, theses and articles, video classes, and several didactic resources. The biomathematical models integrated with the applications were highly precise in predicting the phenomena, in which the variation explained by the models in the prediction of responses for future observations had R² values of 0.95, 0.72, and 0.92, respectively, for productivity with computer-ANN and smartphone-SLR, and for the cladode area with a smartphone - MLR.Among the forage species, especially in semiarid ecosystems, cactus pear is exceptional because of its high tolerance to adverse conditions and high productivity. Due to this alone, several studies have been conducted to identify the main technologies for this crop. Despite being consolidated and integrated, the cactus pear production system has limited accessibility, technical assistance, and availability of information for those dedicated to its production. This study aimed to present a digital platform, website, and applications to provide technical information on the cactus pear and demonstrate the efficiency of these applications through experimental data. On this digital platform, applications were made available for predicting the productivity of cactus pear using artificial neural networks (ANN) on a computer with routines in the R software and by simple linear regression (SLR) on smartphones on the Android system of the MIT App Inventor 2 platform. In addition, using the smartphone app, it is possible to obtain the cladode area through multiple linear regression (MLR). It is also possible to obtain the estimates of the experimental plot sizes by the maximum modified curvature, linear and quadratic methods with plateau response, relative information, comparison of variances, and convenient plot size. The platform provides technical information associated with the cactus pear crop from different sources (dissertations, theses, articles) and formats (video classes and teaching resources), offline for applications, and online with download for publications, dissertations, theses and articles, video classes, and several didactic resources. The biomathematical models integrated with the applications were highly precise in predicting the phenomena, in which the variation explained by the models in the prediction of responses for future observations had R² values of 0.95, 0.72, and 0.92, respectively, for productivity with computer-ANN and smartphone-SLR, and for the cladode area with a smartphone - MLR.Universidade Estadual de Maringá2022-11-22info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/5740710.4025/actasciagron.v45i1.57407Acta Scientiarum. Agronomy; Vol 45 (2023): Publicação contínua; e57404Acta Scientiarum. Agronomy; v. 45 (2023): Publicação contínua; e574041807-86211679-9275reponame:Acta Scientiarum. Agronomy (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMenghttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/57407/751375155042Copyright (c) 2023 Acta Scientiarum. Agronomyhttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessGuimarães, Bruno Vinícius CastroDonato, Sérgio Luiz Rodrigues Aspiazú, Ignacio Azevedo, Alcinei Mistico Lima, Fábio dos Santos Macêdo, Samuel Victor Medeiros de Brito, Cleiton Fernando Barbosa Couto, Hiago Fagundes 2023-01-31T19:20:41Zoai:periodicos.uem.br/ojs:article/57407Revistahttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgronPUBhttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/oaiactaagron@uem.br||actaagron@uem.br|| edamasio@uem.br1807-86211679-9275opendoar:2023-01-31T19:20:41Acta Scientiarum. Agronomy (Online) - Universidade Estadual de Maringá (UEM)false
dc.title.none.fl_str_mv Digital platform for experimental and technical support to the cultivation of cactus pear
Digital platform for experimental and technical support to the cultivation of cactus pear
title Digital platform for experimental and technical support to the cultivation of cactus pear
spellingShingle Digital platform for experimental and technical support to the cultivation of cactus pear
Guimarães, Bruno Vinícius Castro
digital agriculture 4.0; artificial intelligence; smartphone; online and offline models; Opuntia sp.
digital agriculture 4.0; artificial intelligence; smartphone; online and offline models; Opuntia sp.
title_short Digital platform for experimental and technical support to the cultivation of cactus pear
title_full Digital platform for experimental and technical support to the cultivation of cactus pear
title_fullStr Digital platform for experimental and technical support to the cultivation of cactus pear
title_full_unstemmed Digital platform for experimental and technical support to the cultivation of cactus pear
title_sort Digital platform for experimental and technical support to the cultivation of cactus pear
author Guimarães, Bruno Vinícius Castro
author_facet Guimarães, Bruno Vinícius Castro
Donato, Sérgio Luiz Rodrigues
Aspiazú, Ignacio
Azevedo, Alcinei Mistico
Lima, Fábio dos Santos
Macêdo, Samuel Victor Medeiros de
Brito, Cleiton Fernando Barbosa
Couto, Hiago Fagundes
author_role author
author2 Donato, Sérgio Luiz Rodrigues
Aspiazú, Ignacio
Azevedo, Alcinei Mistico
Lima, Fábio dos Santos
Macêdo, Samuel Victor Medeiros de
Brito, Cleiton Fernando Barbosa
Couto, Hiago Fagundes
author2_role author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Guimarães, Bruno Vinícius Castro
Donato, Sérgio Luiz Rodrigues
Aspiazú, Ignacio
Azevedo, Alcinei Mistico
Lima, Fábio dos Santos
Macêdo, Samuel Victor Medeiros de
Brito, Cleiton Fernando Barbosa
Couto, Hiago Fagundes
dc.subject.por.fl_str_mv digital agriculture 4.0; artificial intelligence; smartphone; online and offline models; Opuntia sp.
digital agriculture 4.0; artificial intelligence; smartphone; online and offline models; Opuntia sp.
topic digital agriculture 4.0; artificial intelligence; smartphone; online and offline models; Opuntia sp.
digital agriculture 4.0; artificial intelligence; smartphone; online and offline models; Opuntia sp.
description Among the forage species, especially in semiarid ecosystems, cactus pear is exceptional because of its high tolerance to adverse conditions and high productivity. Due to this alone, several studies have been conducted to identify the main technologies for this crop. Despite being consolidated and integrated, the cactus pear production system has limited accessibility, technical assistance, and availability of information for those dedicated to its production. This study aimed to present a digital platform, website, and applications to provide technical information on the cactus pear and demonstrate the efficiency of these applications through experimental data. On this digital platform, applications were made available for predicting the productivity of cactus pear using artificial neural networks (ANN) on a computer with routines in the R software and by simple linear regression (SLR) on smartphones on the Android system of the MIT App Inventor 2 platform. In addition, using the smartphone app, it is possible to obtain the cladode area through multiple linear regression (MLR). It is also possible to obtain the estimates of the experimental plot sizes by the maximum modified curvature, linear and quadratic methods with plateau response, relative information, comparison of variances, and convenient plot size. The platform provides technical information associated with the cactus pear crop from different sources (dissertations, theses, articles) and formats (video classes and teaching resources), offline for applications, and online with download for publications, dissertations, theses and articles, video classes, and several didactic resources. The biomathematical models integrated with the applications were highly precise in predicting the phenomena, in which the variation explained by the models in the prediction of responses for future observations had R² values of 0.95, 0.72, and 0.92, respectively, for productivity with computer-ANN and smartphone-SLR, and for the cladode area with a smartphone - MLR.
publishDate 2022
dc.date.none.fl_str_mv 2022-11-22
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/57407
10.4025/actasciagron.v45i1.57407
url http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/57407
identifier_str_mv 10.4025/actasciagron.v45i1.57407
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/57407/751375155042
dc.rights.driver.fl_str_mv Copyright (c) 2023 Acta Scientiarum. Agronomy
https://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2023 Acta Scientiarum. Agronomy
https://creativecommons.org/licenses/by/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade Estadual de Maringá
publisher.none.fl_str_mv Universidade Estadual de Maringá
dc.source.none.fl_str_mv Acta Scientiarum. Agronomy; Vol 45 (2023): Publicação contínua; e57404
Acta Scientiarum. Agronomy; v. 45 (2023): Publicação contínua; e57404
1807-8621
1679-9275
reponame:Acta Scientiarum. Agronomy (Online)
instname:Universidade Estadual de Maringá (UEM)
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instname_str Universidade Estadual de Maringá (UEM)
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reponame_str Acta Scientiarum. Agronomy (Online)
collection Acta Scientiarum. Agronomy (Online)
repository.name.fl_str_mv Acta Scientiarum. Agronomy (Online) - Universidade Estadual de Maringá (UEM)
repository.mail.fl_str_mv actaagron@uem.br||actaagron@uem.br|| edamasio@uem.br
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