BIOPHYSICAL CHARACTERISTICS OF SOYBEAN ESTIMATED BY REMOTE SENSING ASSOCIATED WITH ARTIFICIAL INTELLIGENCE
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.14393/BJ-v38n0a2022-55925 http://hdl.handle.net/11449/241761 |
Resumo: | The biophysical characteristics of vegetative canopies, such as biomass, height, and canopy diameter, are of paramount importance for the study of the development and productive behavior of crops. Faced with a scarcity of studies aimed at estimating these parameters, the objective of this study was to evaluate the performance of artificial neural networks (ANNs) applied to Proximal Remote Sensing (PRS) to estimate biophysical characteristics of soybean culture. The data used to train and validate the ANNs came from an experiment composed of 65 plots with 30 x 30 m mesh, its development was carried out in the 2016/2017 crop in the Brazilian agricultural area. The evaluations were carried out at 30, 45, 60, and 75 days after sowing (DAS), monitoring the spatial and temporal variability of the biophysical characteristics of the soybean crop. Vegetation indexes were collected using canopy sensors. The accuracy and precision were determined by the coefficient of determination (R2) and the error of the forecasts by MAPE (Mean Absolute Percentage Error). PRS and ANNs showed high potential for application in agriculture, since they obtained good performance in the estimation of height (R2 = 0.89) and canopy diameter (R2 = 0.96), being fresh biomass (R2 =0.98) and dry biomass (R2 = 0.97) were the best-estimated variables. |
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BIOPHYSICAL CHARACTERISTICS OF SOYBEAN ESTIMATED BY REMOTE SENSING ASSOCIATED WITH ARTIFICIAL INTELLIGENCEActive Optical SensorArtificial Neural NetworksGlycine max LMachine LearningVegetation IndexThe biophysical characteristics of vegetative canopies, such as biomass, height, and canopy diameter, are of paramount importance for the study of the development and productive behavior of crops. Faced with a scarcity of studies aimed at estimating these parameters, the objective of this study was to evaluate the performance of artificial neural networks (ANNs) applied to Proximal Remote Sensing (PRS) to estimate biophysical characteristics of soybean culture. The data used to train and validate the ANNs came from an experiment composed of 65 plots with 30 x 30 m mesh, its development was carried out in the 2016/2017 crop in the Brazilian agricultural area. The evaluations were carried out at 30, 45, 60, and 75 days after sowing (DAS), monitoring the spatial and temporal variability of the biophysical characteristics of the soybean crop. Vegetation indexes were collected using canopy sensors. The accuracy and precision were determined by the coefficient of determination (R2) and the error of the forecasts by MAPE (Mean Absolute Percentage Error). PRS and ANNs showed high potential for application in agriculture, since they obtained good performance in the estimation of height (R2 = 0.89) and canopy diameter (R2 = 0.96), being fresh biomass (R2 =0.98) and dry biomass (R2 = 0.97) were the best-estimated variables.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)School of Plant Environmental and Soil Sciences Louisiana State UniversityCrop Soil & Environmental Sciences Department Auburn UniversityPostgraduate program in Agronomy (Crop Production) School of Agricultural and Veterinarian Sciences São Paulo State University, São PauloPostgraduate program in Agronomy (Soil Science) School of Agricultural and Veterinarian Sciences São Paulo State University, São PauloEngineering and Exact Sciences Department School of Agricultural and Veterinarian Sciences São Paulo State University, São PauloPostgraduate program in Agronomy (Crop Production) School of Agricultural and Veterinarian Sciences São Paulo State University, São PauloPostgraduate program in Agronomy (Soil Science) School of Agricultural and Veterinarian Sciences São Paulo State University, São PauloEngineering and Exact Sciences Department School of Agricultural and Veterinarian Sciences São Paulo State University, São PauloCNPq: 142367/20150Louisiana State UniversityAuburn UniversityUniversidade Estadual Paulista (UNESP)Carneiro, Franciele Morlinde OLIVEIRA, Mailson Freirede ALMEIDA, Samira Luns Hatum [UNESP]de BRITO FILHO, Armando Lopes [UNESP]Furlani, Carlos Eduardo Angeli [UNESP]Rolim, Glauco de Souza [UNESP]Ferraudo, Antonio Sergio [UNESP]da SILVA, Rouverson Pereira [UNESP]2023-03-01T23:41:13Z2023-03-01T23:41:13Z2022-02-16info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.14393/BJ-v38n0a2022-55925Bioscience Journal, v. 38.1981-31631516-3725http://hdl.handle.net/11449/24176110.14393/BJ-v38n0a2022-559252-s2.0-85128655304Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengBioscience Journalinfo:eu-repo/semantics/openAccess2024-06-06T15:18:03Zoai:repositorio.unesp.br:11449/241761Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T15:37:33.142301Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
BIOPHYSICAL CHARACTERISTICS OF SOYBEAN ESTIMATED BY REMOTE SENSING ASSOCIATED WITH ARTIFICIAL INTELLIGENCE |
title |
BIOPHYSICAL CHARACTERISTICS OF SOYBEAN ESTIMATED BY REMOTE SENSING ASSOCIATED WITH ARTIFICIAL INTELLIGENCE |
spellingShingle |
BIOPHYSICAL CHARACTERISTICS OF SOYBEAN ESTIMATED BY REMOTE SENSING ASSOCIATED WITH ARTIFICIAL INTELLIGENCE Carneiro, Franciele Morlin Active Optical Sensor Artificial Neural Networks Glycine max L Machine Learning Vegetation Index |
title_short |
BIOPHYSICAL CHARACTERISTICS OF SOYBEAN ESTIMATED BY REMOTE SENSING ASSOCIATED WITH ARTIFICIAL INTELLIGENCE |
title_full |
BIOPHYSICAL CHARACTERISTICS OF SOYBEAN ESTIMATED BY REMOTE SENSING ASSOCIATED WITH ARTIFICIAL INTELLIGENCE |
title_fullStr |
BIOPHYSICAL CHARACTERISTICS OF SOYBEAN ESTIMATED BY REMOTE SENSING ASSOCIATED WITH ARTIFICIAL INTELLIGENCE |
title_full_unstemmed |
BIOPHYSICAL CHARACTERISTICS OF SOYBEAN ESTIMATED BY REMOTE SENSING ASSOCIATED WITH ARTIFICIAL INTELLIGENCE |
title_sort |
BIOPHYSICAL CHARACTERISTICS OF SOYBEAN ESTIMATED BY REMOTE SENSING ASSOCIATED WITH ARTIFICIAL INTELLIGENCE |
author |
Carneiro, Franciele Morlin |
author_facet |
Carneiro, Franciele Morlin de OLIVEIRA, Mailson Freire de ALMEIDA, Samira Luns Hatum [UNESP] de BRITO FILHO, Armando Lopes [UNESP] Furlani, Carlos Eduardo Angeli [UNESP] Rolim, Glauco de Souza [UNESP] Ferraudo, Antonio Sergio [UNESP] da SILVA, Rouverson Pereira [UNESP] |
author_role |
author |
author2 |
de OLIVEIRA, Mailson Freire de ALMEIDA, Samira Luns Hatum [UNESP] de BRITO FILHO, Armando Lopes [UNESP] Furlani, Carlos Eduardo Angeli [UNESP] Rolim, Glauco de Souza [UNESP] Ferraudo, Antonio Sergio [UNESP] da SILVA, Rouverson Pereira [UNESP] |
author2_role |
author author author author author author author |
dc.contributor.none.fl_str_mv |
Louisiana State University Auburn University Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Carneiro, Franciele Morlin de OLIVEIRA, Mailson Freire de ALMEIDA, Samira Luns Hatum [UNESP] de BRITO FILHO, Armando Lopes [UNESP] Furlani, Carlos Eduardo Angeli [UNESP] Rolim, Glauco de Souza [UNESP] Ferraudo, Antonio Sergio [UNESP] da SILVA, Rouverson Pereira [UNESP] |
dc.subject.por.fl_str_mv |
Active Optical Sensor Artificial Neural Networks Glycine max L Machine Learning Vegetation Index |
topic |
Active Optical Sensor Artificial Neural Networks Glycine max L Machine Learning Vegetation Index |
description |
The biophysical characteristics of vegetative canopies, such as biomass, height, and canopy diameter, are of paramount importance for the study of the development and productive behavior of crops. Faced with a scarcity of studies aimed at estimating these parameters, the objective of this study was to evaluate the performance of artificial neural networks (ANNs) applied to Proximal Remote Sensing (PRS) to estimate biophysical characteristics of soybean culture. The data used to train and validate the ANNs came from an experiment composed of 65 plots with 30 x 30 m mesh, its development was carried out in the 2016/2017 crop in the Brazilian agricultural area. The evaluations were carried out at 30, 45, 60, and 75 days after sowing (DAS), monitoring the spatial and temporal variability of the biophysical characteristics of the soybean crop. Vegetation indexes were collected using canopy sensors. The accuracy and precision were determined by the coefficient of determination (R2) and the error of the forecasts by MAPE (Mean Absolute Percentage Error). PRS and ANNs showed high potential for application in agriculture, since they obtained good performance in the estimation of height (R2 = 0.89) and canopy diameter (R2 = 0.96), being fresh biomass (R2 =0.98) and dry biomass (R2 = 0.97) were the best-estimated variables. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-02-16 2023-03-01T23:41:13Z 2023-03-01T23:41:13Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.14393/BJ-v38n0a2022-55925 Bioscience Journal, v. 38. 1981-3163 1516-3725 http://hdl.handle.net/11449/241761 10.14393/BJ-v38n0a2022-55925 2-s2.0-85128655304 |
url |
http://dx.doi.org/10.14393/BJ-v38n0a2022-55925 http://hdl.handle.net/11449/241761 |
identifier_str_mv |
Bioscience Journal, v. 38. 1981-3163 1516-3725 10.14393/BJ-v38n0a2022-55925 2-s2.0-85128655304 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Bioscience Journal |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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_ |
1808128541083041792 |