Digital platform for experimental and technical support to the cultivation of cactus pear
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , , |
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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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) instacron:UEM |
instname_str |
Universidade Estadual de Maringá (UEM) |
instacron_str |
UEM |
institution |
UEM |
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 |
_version_ |
1799305901179404288 |