Integrating Satellite and UAV Data to Predict Peanut Maturity upon Artificial Neural Networks

Detalhes bibliográficos
Autor(a) principal: Souza, Jarlyson Brunno Costa. [UNESP]
Data de Publicação: 2022
Outros Autores: de Almeida, Samira Luns Hatum. [UNESP], Freire de Oliveira, Mailson. [UNESP], Dos Santos, Adão Felipe., Filho, Armando Lopes de Brito. [UNESP], Meneses, Mariana Dias. [UNESP], Silva, Rouverson Pereira da. [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.3390/agronomy12071512
http://hdl.handle.net/11449/242146
Resumo: The monitoring and determination of peanut maturity are fundamental to reducing losses during digging operation. However, the methods currently used are laborious and subjective. To solve this problem, we developed models to access peanut maturity using images from unmanned aerial vehicles (UAV) and satellites. We evaluated an area of approximately 8 hectares in which a regular grid of 30 points was determined with weekly evaluations starting at 90 days after sowing. Two Artificial Neural Networking (ANN) were used with Radial Basis Function (RBF) and Multilayer Perceptron (MLP) to predict the Peanut Maturity Index (PMI) with the spectral bands available from each sensor. Several vegetation indices were used as input to the ANN, with the data being split 80/20 for training and validation, respectively. The vegetation index, Normalized Difference Red Edge Index (NDRE), was the most precise coefficient of determination (R2 = 0.88) and accurate mean absolute error (MAE = 0.06) for estimating PMI, regardless of the type of ANN used. The satellite with Normalized Difference Vegetation Index (NDVI) could also determine PMI with better accuracy (MAE = 0.05) than the NDRE. The performance evaluation indicates that the RBF and MLP networks are similar in predicting peanut maturity. We concluded that satellite and UAV images can predict the maturity index with good accuracy and precision.
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spelling Integrating Satellite and UAV Data to Predict Peanut Maturity upon Artificial Neural Networksmachine learningMultilayer PerceptronPlanetScopeRadial Basis Functionunmanned aerial vehicleThe monitoring and determination of peanut maturity are fundamental to reducing losses during digging operation. However, the methods currently used are laborious and subjective. To solve this problem, we developed models to access peanut maturity using images from unmanned aerial vehicles (UAV) and satellites. We evaluated an area of approximately 8 hectares in which a regular grid of 30 points was determined with weekly evaluations starting at 90 days after sowing. Two Artificial Neural Networking (ANN) were used with Radial Basis Function (RBF) and Multilayer Perceptron (MLP) to predict the Peanut Maturity Index (PMI) with the spectral bands available from each sensor. Several vegetation indices were used as input to the ANN, with the data being split 80/20 for training and validation, respectively. The vegetation index, Normalized Difference Red Edge Index (NDRE), was the most precise coefficient of determination (R2 = 0.88) and accurate mean absolute error (MAE = 0.06) for estimating PMI, regardless of the type of ANN used. The satellite with Normalized Difference Vegetation Index (NDVI) could also determine PMI with better accuracy (MAE = 0.05) than the NDRE. The performance evaluation indicates that the RBF and MLP networks are similar in predicting peanut maturity. We concluded that satellite and UAV images can predict the maturity index with good accuracy and precision.Department of Engineering and Mathematical Sciences School of Agricultural and Veterinarian Sciences São Paulo State University (Unesp), SPDepartment of Crop Soil and Environmental Sciences Auburn UniversityDepartment of Agriculture School of Agricultural Sciences of Lavras Federal University of Lavras (UFLA), MGDepartment of Engineering and Mathematical Sciences School of Agricultural and Veterinarian Sciences São Paulo State University (Unesp), SPUniversidade Estadual Paulista (UNESP)Auburn UniversityUniversidade Federal de Lavras (UFLA)Souza, Jarlyson Brunno Costa. [UNESP]de Almeida, Samira Luns Hatum. [UNESP]Freire de Oliveira, Mailson. [UNESP]Dos Santos, Adão Felipe.Filho, Armando Lopes de Brito. [UNESP]Meneses, Mariana Dias. [UNESP]Silva, Rouverson Pereira da. [UNESP]2023-03-02T10:07:05Z2023-03-02T10:07:05Z2022-07-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.3390/agronomy12071512Agronomy, v. 12, n. 7, 2022.2073-4395http://hdl.handle.net/11449/24214610.3390/agronomy120715122-s2.0-85135878206Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengAgronomyinfo:eu-repo/semantics/openAccess2024-06-06T15:17:56Zoai:repositorio.unesp.br:11449/242146Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T13:39:25.262068Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Integrating Satellite and UAV Data to Predict Peanut Maturity upon Artificial Neural Networks
title Integrating Satellite and UAV Data to Predict Peanut Maturity upon Artificial Neural Networks
spellingShingle Integrating Satellite and UAV Data to Predict Peanut Maturity upon Artificial Neural Networks
Souza, Jarlyson Brunno Costa. [UNESP]
machine learning
Multilayer Perceptron
PlanetScope
Radial Basis Function
unmanned aerial vehicle
title_short Integrating Satellite and UAV Data to Predict Peanut Maturity upon Artificial Neural Networks
title_full Integrating Satellite and UAV Data to Predict Peanut Maturity upon Artificial Neural Networks
title_fullStr Integrating Satellite and UAV Data to Predict Peanut Maturity upon Artificial Neural Networks
title_full_unstemmed Integrating Satellite and UAV Data to Predict Peanut Maturity upon Artificial Neural Networks
title_sort Integrating Satellite and UAV Data to Predict Peanut Maturity upon Artificial Neural Networks
author Souza, Jarlyson Brunno Costa. [UNESP]
author_facet Souza, Jarlyson Brunno Costa. [UNESP]
de Almeida, Samira Luns Hatum. [UNESP]
Freire de Oliveira, Mailson. [UNESP]
Dos Santos, Adão Felipe.
Filho, Armando Lopes de Brito. [UNESP]
Meneses, Mariana Dias. [UNESP]
Silva, Rouverson Pereira da. [UNESP]
author_role author
author2 de Almeida, Samira Luns Hatum. [UNESP]
Freire de Oliveira, Mailson. [UNESP]
Dos Santos, Adão Felipe.
Filho, Armando Lopes de Brito. [UNESP]
Meneses, Mariana Dias. [UNESP]
Silva, Rouverson Pereira da. [UNESP]
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (UNESP)
Auburn University
Universidade Federal de Lavras (UFLA)
dc.contributor.author.fl_str_mv Souza, Jarlyson Brunno Costa. [UNESP]
de Almeida, Samira Luns Hatum. [UNESP]
Freire de Oliveira, Mailson. [UNESP]
Dos Santos, Adão Felipe.
Filho, Armando Lopes de Brito. [UNESP]
Meneses, Mariana Dias. [UNESP]
Silva, Rouverson Pereira da. [UNESP]
dc.subject.por.fl_str_mv machine learning
Multilayer Perceptron
PlanetScope
Radial Basis Function
unmanned aerial vehicle
topic machine learning
Multilayer Perceptron
PlanetScope
Radial Basis Function
unmanned aerial vehicle
description The monitoring and determination of peanut maturity are fundamental to reducing losses during digging operation. However, the methods currently used are laborious and subjective. To solve this problem, we developed models to access peanut maturity using images from unmanned aerial vehicles (UAV) and satellites. We evaluated an area of approximately 8 hectares in which a regular grid of 30 points was determined with weekly evaluations starting at 90 days after sowing. Two Artificial Neural Networking (ANN) were used with Radial Basis Function (RBF) and Multilayer Perceptron (MLP) to predict the Peanut Maturity Index (PMI) with the spectral bands available from each sensor. Several vegetation indices were used as input to the ANN, with the data being split 80/20 for training and validation, respectively. The vegetation index, Normalized Difference Red Edge Index (NDRE), was the most precise coefficient of determination (R2 = 0.88) and accurate mean absolute error (MAE = 0.06) for estimating PMI, regardless of the type of ANN used. The satellite with Normalized Difference Vegetation Index (NDVI) could also determine PMI with better accuracy (MAE = 0.05) than the NDRE. The performance evaluation indicates that the RBF and MLP networks are similar in predicting peanut maturity. We concluded that satellite and UAV images can predict the maturity index with good accuracy and precision.
publishDate 2022
dc.date.none.fl_str_mv 2022-07-01
2023-03-02T10:07:05Z
2023-03-02T10:07:05Z
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/10.3390/agronomy12071512
Agronomy, v. 12, n. 7, 2022.
2073-4395
http://hdl.handle.net/11449/242146
10.3390/agronomy12071512
2-s2.0-85135878206
url http://dx.doi.org/10.3390/agronomy12071512
http://hdl.handle.net/11449/242146
identifier_str_mv Agronomy, v. 12, n. 7, 2022.
2073-4395
10.3390/agronomy12071512
2-s2.0-85135878206
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Agronomy
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
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