Fuzzy modeling of biometric variables development of tomato crop under irrigation and water salinity effects
Autor(a) principal: | |
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Data de Publicação: | 2023 |
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/63515 |
Resumo: | Tomato is a demanding crop in terms of handling, mainly because irrigation has a strong influence on fruit production and quality. Salinity changes the absorption, transport, assimilation, and distribution of nutrients in the plant. In general, such effects are analyzed using statistical tests. However, fuzzy models allow simulations between points that are not verified in agricultural experimentation. Currently, systems with artificial intelligence have excelled in the field of applied sciences, particularly fuzzy systems applied to mathematical modeling. The objective of this research was to use fuzzy modeling to analyze the biometric variables during the development of hybrid tomatoes under two different conditions: the first concerning different water tensions in the soil and the second concerning different salinity doses in irrigation. To this end, two models were developed based on an experiment carried out at São Paulo State University (UNESP), School of Agriculture, Botucatu, São Paulo State, Brazil. Both models sought to estimate the values of biometric variables of the tomato crop. Thus, two models were developed: Model 1 regarded water tensions and days after sowing (DAS), while Model 2 featured salinity and DAS. Fuzzy models provided results that verified the effects of irrigation and salinity layers. Two Fuzzy Rule-Based Systems (FRBS), an input processor with two variables, a set of linguistic rules defined from statistical procedures with percentiles, the Mamdani fuzzy inference method, and the center of gravity method to defuzzification were elaborated for this purpose. The range between −25 and −10 kPa (for Model 1) and between 0.08 and 3 dS m−1 (for Model 2) provided the development within the ideal parameters for the complete development of the plant cycle. The use of fuzzy logic has shown effectiveness in evaluating the development of tomato crops, thus showing potential for use in agricultural sciences. Moreover, the created fuzzy models showed the same characteristics of the experiment, allowing their use as an automatic technique to estimate ideal parameters for the complete development of the plant cycle. The development of applications (software) that provide the results generated by the artificial intelligence models of the present study is the aim of future research. |
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Fuzzy modeling of biometric variables development of tomato crop under irrigation and water salinity effects Fuzzy modeling of biometric variables development of tomato crop under irrigation and water salinity effects mathematical modeling; water potential; phytomass; artificial intelligence.mathematical modeling; water potential; phytomass; artificial intelligence.Tomato is a demanding crop in terms of handling, mainly because irrigation has a strong influence on fruit production and quality. Salinity changes the absorption, transport, assimilation, and distribution of nutrients in the plant. In general, such effects are analyzed using statistical tests. However, fuzzy models allow simulations between points that are not verified in agricultural experimentation. Currently, systems with artificial intelligence have excelled in the field of applied sciences, particularly fuzzy systems applied to mathematical modeling. The objective of this research was to use fuzzy modeling to analyze the biometric variables during the development of hybrid tomatoes under two different conditions: the first concerning different water tensions in the soil and the second concerning different salinity doses in irrigation. To this end, two models were developed based on an experiment carried out at São Paulo State University (UNESP), School of Agriculture, Botucatu, São Paulo State, Brazil. Both models sought to estimate the values of biometric variables of the tomato crop. Thus, two models were developed: Model 1 regarded water tensions and days after sowing (DAS), while Model 2 featured salinity and DAS. Fuzzy models provided results that verified the effects of irrigation and salinity layers. Two Fuzzy Rule-Based Systems (FRBS), an input processor with two variables, a set of linguistic rules defined from statistical procedures with percentiles, the Mamdani fuzzy inference method, and the center of gravity method to defuzzification were elaborated for this purpose. The range between −25 and −10 kPa (for Model 1) and between 0.08 and 3 dS m−1 (for Model 2) provided the development within the ideal parameters for the complete development of the plant cycle. The use of fuzzy logic has shown effectiveness in evaluating the development of tomato crops, thus showing potential for use in agricultural sciences. Moreover, the created fuzzy models showed the same characteristics of the experiment, allowing their use as an automatic technique to estimate ideal parameters for the complete development of the plant cycle. The development of applications (software) that provide the results generated by the artificial intelligence models of the present study is the aim of future research.Tomato is a demanding crop in terms of handling, mainly because irrigation has a strong influence on fruit production and quality. Salinity changes the absorption, transport, assimilation, and distribution of nutrients in the plant. In general, such effects are analyzed using statistical tests. However, fuzzy models allow simulations between points that are not verified in agricultural experimentation. Currently, systems with artificial intelligence have excelled in the field of applied sciences, particularly fuzzy systems applied to mathematical modeling. The objective of this research was to use fuzzy modeling to analyze the biometric variables during the development of hybrid tomatoes under two different conditions: the first concerning different water tensions in the soil and the second concerning different salinity doses in irrigation. To this end, two models were developed based on an experiment carried out at São Paulo State University (UNESP), School of Agriculture, Botucatu, São Paulo State, Brazil. Both models sought to estimate the values of biometric variables of the tomato crop. Thus, two models were developed: Model 1 regarded water tensions and days after sowing (DAS), while Model 2 featured salinity and DAS. Fuzzy models provided results that verified the effects of irrigation and salinity layers. Two Fuzzy Rule-Based Systems (FRBS), an input processor with two variables, a set of linguistic rules defined from statistical procedures with percentiles, the Mamdani fuzzy inference method, and the center of gravity method to defuzzification were elaborated for this purpose. The range between −25 and −10 kPa (for Model 1) and between 0.08 and 3 dS m−1 (for Model 2) provided the development within the ideal parameters for the complete development of the plant cycle. The use of fuzzy logic has shown effectiveness in evaluating the development of tomato crops, thus showing potential for use in agricultural sciences. Moreover, the created fuzzy models showed the same characteristics of the experiment, allowing their use as an automatic technique to estimate ideal parameters for the complete development of the plant cycle. The development of applications (software) that provide the results generated by the artificial intelligence models of the present study is the aim of future research.Universidade Estadual de Maringá2023-12-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/6351510.4025/actasciagron.v46i1.63515Acta Scientiarum. Agronomy; Vol 46 No 1 (2024): Publicação contínua; e63515Acta Scientiarum. Agronomy; v. 46 n. 1 (2024): Publicação contínua; e635151807-86211679-9275reponame:Acta Scientiarum. Agronomy (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMenghttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/63515/751375156914Copyright (c) 2024 Acta Scientiarum. Agronomyhttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessGabriel Filho, Luís Roberto AlmeidaViais Neto, Daniel dos SantosPutti, Fernando FerrariBordin, DeyverSilva Junior, Josué FerreiraCremasco, Camila Pires2024-02-08T19:39:20Zoai:periodicos.uem.br/ojs:article/63515Revistahttp://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:2024-02-08T19:39:20Acta Scientiarum. Agronomy (Online) - Universidade Estadual de Maringá (UEM)false |
dc.title.none.fl_str_mv |
Fuzzy modeling of biometric variables development of tomato crop under irrigation and water salinity effects Fuzzy modeling of biometric variables development of tomato crop under irrigation and water salinity effects |
title |
Fuzzy modeling of biometric variables development of tomato crop under irrigation and water salinity effects |
spellingShingle |
Fuzzy modeling of biometric variables development of tomato crop under irrigation and water salinity effects Gabriel Filho, Luís Roberto Almeida mathematical modeling; water potential; phytomass; artificial intelligence. mathematical modeling; water potential; phytomass; artificial intelligence. |
title_short |
Fuzzy modeling of biometric variables development of tomato crop under irrigation and water salinity effects |
title_full |
Fuzzy modeling of biometric variables development of tomato crop under irrigation and water salinity effects |
title_fullStr |
Fuzzy modeling of biometric variables development of tomato crop under irrigation and water salinity effects |
title_full_unstemmed |
Fuzzy modeling of biometric variables development of tomato crop under irrigation and water salinity effects |
title_sort |
Fuzzy modeling of biometric variables development of tomato crop under irrigation and water salinity effects |
author |
Gabriel Filho, Luís Roberto Almeida |
author_facet |
Gabriel Filho, Luís Roberto Almeida Viais Neto, Daniel dos Santos Putti, Fernando Ferrari Bordin, Deyver Silva Junior, Josué Ferreira Cremasco, Camila Pires |
author_role |
author |
author2 |
Viais Neto, Daniel dos Santos Putti, Fernando Ferrari Bordin, Deyver Silva Junior, Josué Ferreira Cremasco, Camila Pires |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Gabriel Filho, Luís Roberto Almeida Viais Neto, Daniel dos Santos Putti, Fernando Ferrari Bordin, Deyver Silva Junior, Josué Ferreira Cremasco, Camila Pires |
dc.subject.por.fl_str_mv |
mathematical modeling; water potential; phytomass; artificial intelligence. mathematical modeling; water potential; phytomass; artificial intelligence. |
topic |
mathematical modeling; water potential; phytomass; artificial intelligence. mathematical modeling; water potential; phytomass; artificial intelligence. |
description |
Tomato is a demanding crop in terms of handling, mainly because irrigation has a strong influence on fruit production and quality. Salinity changes the absorption, transport, assimilation, and distribution of nutrients in the plant. In general, such effects are analyzed using statistical tests. However, fuzzy models allow simulations between points that are not verified in agricultural experimentation. Currently, systems with artificial intelligence have excelled in the field of applied sciences, particularly fuzzy systems applied to mathematical modeling. The objective of this research was to use fuzzy modeling to analyze the biometric variables during the development of hybrid tomatoes under two different conditions: the first concerning different water tensions in the soil and the second concerning different salinity doses in irrigation. To this end, two models were developed based on an experiment carried out at São Paulo State University (UNESP), School of Agriculture, Botucatu, São Paulo State, Brazil. Both models sought to estimate the values of biometric variables of the tomato crop. Thus, two models were developed: Model 1 regarded water tensions and days after sowing (DAS), while Model 2 featured salinity and DAS. Fuzzy models provided results that verified the effects of irrigation and salinity layers. Two Fuzzy Rule-Based Systems (FRBS), an input processor with two variables, a set of linguistic rules defined from statistical procedures with percentiles, the Mamdani fuzzy inference method, and the center of gravity method to defuzzification were elaborated for this purpose. The range between −25 and −10 kPa (for Model 1) and between 0.08 and 3 dS m−1 (for Model 2) provided the development within the ideal parameters for the complete development of the plant cycle. The use of fuzzy logic has shown effectiveness in evaluating the development of tomato crops, thus showing potential for use in agricultural sciences. Moreover, the created fuzzy models showed the same characteristics of the experiment, allowing their use as an automatic technique to estimate ideal parameters for the complete development of the plant cycle. The development of applications (software) that provide the results generated by the artificial intelligence models of the present study is the aim of future research. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-12-11 |
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/63515 10.4025/actasciagron.v46i1.63515 |
url |
http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/63515 |
identifier_str_mv |
10.4025/actasciagron.v46i1.63515 |
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/63515/751375156914 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2024 Acta Scientiarum. Agronomy https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2024 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 46 No 1 (2024): Publicação contínua; e63515 Acta Scientiarum. Agronomy; v. 46 n. 1 (2024): Publicação contínua; e63515 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_ |
1799305901371293696 |