Integrating satellite and UAV data to predict peanut maturity upon artificial neural networks
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 UFLA |
Texto Completo: | http://repositorio.ufla.br/jspui/handle/1/55129 |
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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Integrating satellite and UAV data to predict peanut maturity upon artificial neural networksPlanetScopeUnmanned aerial vehicleMachine learningMultilayer PerceptronRadial Basis FunctionRedes neurais artificiaisVeículo aéreo não tripuladoAprendizado de máquinaPerceptron MulticamadasFunção de base radialThe 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.Multidisciplinary Digital Publishing Institute - MDPI2022-09-19T19:05:14Z2022-09-19T19:05:14Z2022-06info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfSOUZA, J. B. C. et al. Integrating satellite and UAV data to predict peanut maturity upon artificial neural networks. Agronomy, Basel, v. 12, n. 7, 2022. DOI: https://doi.org/10.3390/agronomy12071512.http://repositorio.ufla.br/jspui/handle/1/55129Agronomyreponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessSouza, Jarlyson Brunno CostaAlmeida, Samira Luns Hatum deOliveira, Mailson Freire deSantos, Adão Felipe dosBrito Filho, Armando Lopes deMeneses, Mariana DiasSilva, Rouverson Pereira daeng2023-05-26T18:50:10Zoai:localhost:1/55129Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2023-05-26T18:50:10Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)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 PlanetScope Unmanned aerial vehicle Machine learning Multilayer Perceptron Radial Basis Function Redes neurais artificiais Veículo aéreo não tripulado Aprendizado de máquina Perceptron Multicamadas Função de base radial |
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 |
author_facet |
Souza, Jarlyson Brunno Costa Almeida, Samira Luns Hatum de Oliveira, Mailson Freire de Santos, Adão Felipe dos Brito Filho, Armando Lopes de Meneses, Mariana Dias Silva, Rouverson Pereira da |
author_role |
author |
author2 |
Almeida, Samira Luns Hatum de Oliveira, Mailson Freire de Santos, Adão Felipe dos Brito Filho, Armando Lopes de Meneses, Mariana Dias Silva, Rouverson Pereira da |
author2_role |
author author author author author author |
dc.contributor.author.fl_str_mv |
Souza, Jarlyson Brunno Costa Almeida, Samira Luns Hatum de Oliveira, Mailson Freire de Santos, Adão Felipe dos Brito Filho, Armando Lopes de Meneses, Mariana Dias Silva, Rouverson Pereira da |
dc.subject.por.fl_str_mv |
PlanetScope Unmanned aerial vehicle Machine learning Multilayer Perceptron Radial Basis Function Redes neurais artificiais Veículo aéreo não tripulado Aprendizado de máquina Perceptron Multicamadas Função de base radial |
topic |
PlanetScope Unmanned aerial vehicle Machine learning Multilayer Perceptron Radial Basis Function Redes neurais artificiais Veículo aéreo não tripulado Aprendizado de máquina Perceptron Multicamadas Função de base radial |
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 |
2022-09-19T19:05:14Z 2022-09-19T19:05:14Z 2022-06 |
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 |
SOUZA, J. B. C. et al. Integrating satellite and UAV data to predict peanut maturity upon artificial neural networks. Agronomy, Basel, v. 12, n. 7, 2022. DOI: https://doi.org/10.3390/agronomy12071512. http://repositorio.ufla.br/jspui/handle/1/55129 |
identifier_str_mv |
SOUZA, J. B. C. et al. Integrating satellite and UAV data to predict peanut maturity upon artificial neural networks. Agronomy, Basel, v. 12, n. 7, 2022. DOI: https://doi.org/10.3390/agronomy12071512. |
url |
http://repositorio.ufla.br/jspui/handle/1/55129 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
http://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 |
Multidisciplinary Digital Publishing Institute - MDPI |
publisher.none.fl_str_mv |
Multidisciplinary Digital Publishing Institute - MDPI |
dc.source.none.fl_str_mv |
Agronomy reponame:Repositório Institucional da UFLA instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
Repositório Institucional da UFLA |
collection |
Repositório Institucional da UFLA |
repository.name.fl_str_mv |
Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
nivaldo@ufla.br || repositorio.biblioteca@ufla.br |
_version_ |
1807835152874733568 |