Modeling and identification of fertility maps using artificial neural networks
Autor(a) principal: | |
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Data de Publicação: | 2000 |
Outros Autores: | , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/ICSMC.2000.884399 http://hdl.handle.net/11449/66338 |
Resumo: | The application of agricultural fertilizers using variable rates along the field can be made through fertility maps previously elaborated or through real-time sensors. In most of the cases applies maps previously elaborated. These maps are identified from analyzes done in soil samples collected regularly (a sample for each field cell) or irregularly along the field. At the moment, mathematical interpolation methods such as nearest neighbor, local average, weighted inverse distance, contouring and kriging are used for predicting the variables involved with elaboration of fertility maps. However, some of these methods present deficiencies that can generate different fertility maps for a same data set. Moreover, such methods can generate inprecise maps to be used in precision farming. In this paper, artificial neural networks have been applied for elaboration and identification of precise fertility maps which can reduce the production costs and environmental impacts. |
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Repositório Institucional da UNESP |
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Modeling and identification of fertility maps using artificial neural networksFertilizersInterpolationMathematical modelsReal time systemsSensorsSoilsFertility mapsNeural networksThe application of agricultural fertilizers using variable rates along the field can be made through fertility maps previously elaborated or through real-time sensors. In most of the cases applies maps previously elaborated. These maps are identified from analyzes done in soil samples collected regularly (a sample for each field cell) or irregularly along the field. At the moment, mathematical interpolation methods such as nearest neighbor, local average, weighted inverse distance, contouring and kriging are used for predicting the variables involved with elaboration of fertility maps. However, some of these methods present deficiencies that can generate different fertility maps for a same data set. Moreover, such methods can generate inprecise maps to be used in precision farming. In this paper, artificial neural networks have been applied for elaboration and identification of precise fertility maps which can reduce the production costs and environmental impacts.FCA-UNESP, BotucatuFCA-UNESP, BotucatuUniversidade Estadual Paulista (Unesp)Ulson, Jose Alfredo Covolan [UNESP]da Silva, Ivan Nunes [UNESP]Benez, Sergio Hugo [UNESP]Boas, Roberto L V [UNESP]2014-05-27T11:19:59Z2014-05-27T11:19:59Z2000-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject2673-2678http://dx.doi.org/10.1109/ICSMC.2000.884399Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, v. 4, p. 2673-2678.0884-36271062-922Xhttp://hdl.handle.net/11449/6633810.1109/ICSMC.2000.884399WOS:0001661069004652-s2.0-0034504123Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings of the IEEE International Conference on Systems, Man and Cyberneticsinfo:eu-repo/semantics/openAccess2024-04-15T20:40:22Zoai:repositorio.unesp.br:11449/66338Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:53:56.529045Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Modeling and identification of fertility maps using artificial neural networks |
title |
Modeling and identification of fertility maps using artificial neural networks |
spellingShingle |
Modeling and identification of fertility maps using artificial neural networks Ulson, Jose Alfredo Covolan [UNESP] Fertilizers Interpolation Mathematical models Real time systems Sensors Soils Fertility maps Neural networks |
title_short |
Modeling and identification of fertility maps using artificial neural networks |
title_full |
Modeling and identification of fertility maps using artificial neural networks |
title_fullStr |
Modeling and identification of fertility maps using artificial neural networks |
title_full_unstemmed |
Modeling and identification of fertility maps using artificial neural networks |
title_sort |
Modeling and identification of fertility maps using artificial neural networks |
author |
Ulson, Jose Alfredo Covolan [UNESP] |
author_facet |
Ulson, Jose Alfredo Covolan [UNESP] da Silva, Ivan Nunes [UNESP] Benez, Sergio Hugo [UNESP] Boas, Roberto L V [UNESP] |
author_role |
author |
author2 |
da Silva, Ivan Nunes [UNESP] Benez, Sergio Hugo [UNESP] Boas, Roberto L V [UNESP] |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Ulson, Jose Alfredo Covolan [UNESP] da Silva, Ivan Nunes [UNESP] Benez, Sergio Hugo [UNESP] Boas, Roberto L V [UNESP] |
dc.subject.por.fl_str_mv |
Fertilizers Interpolation Mathematical models Real time systems Sensors Soils Fertility maps Neural networks |
topic |
Fertilizers Interpolation Mathematical models Real time systems Sensors Soils Fertility maps Neural networks |
description |
The application of agricultural fertilizers using variable rates along the field can be made through fertility maps previously elaborated or through real-time sensors. In most of the cases applies maps previously elaborated. These maps are identified from analyzes done in soil samples collected regularly (a sample for each field cell) or irregularly along the field. At the moment, mathematical interpolation methods such as nearest neighbor, local average, weighted inverse distance, contouring and kriging are used for predicting the variables involved with elaboration of fertility maps. However, some of these methods present deficiencies that can generate different fertility maps for a same data set. Moreover, such methods can generate inprecise maps to be used in precision farming. In this paper, artificial neural networks have been applied for elaboration and identification of precise fertility maps which can reduce the production costs and environmental impacts. |
publishDate |
2000 |
dc.date.none.fl_str_mv |
2000-12-01 2014-05-27T11:19:59Z 2014-05-27T11:19:59Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/ICSMC.2000.884399 Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, v. 4, p. 2673-2678. 0884-3627 1062-922X http://hdl.handle.net/11449/66338 10.1109/ICSMC.2000.884399 WOS:000166106900465 2-s2.0-0034504123 |
url |
http://dx.doi.org/10.1109/ICSMC.2000.884399 http://hdl.handle.net/11449/66338 |
identifier_str_mv |
Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, v. 4, p. 2673-2678. 0884-3627 1062-922X 10.1109/ICSMC.2000.884399 WOS:000166106900465 2-s2.0-0034504123 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Proceedings of the IEEE International Conference on Systems, Man and Cybernetics |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
2673-2678 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128718610104320 |