ESTIMATION OF EUCALYPTUS TREE HEIGHT IN CLONAL AND PROGENY TESTS USING ARTIFICIAL NEURAL NETWORKS
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Revista Árvore (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-67622017000600205 |
Resumo: | ABSTRACT The goal of this study was to test the applicability of artificial neural networks for estimating tree heights in clonal tests and progenies. We used data from 8,329 clonal tests collected for six age groups, divided into six blocks and five repetitions. For the progeny tests, we used 36,793 data points, collected at age 5 and divided into ten blocks and five repetitions. The categorical input variables considered were age, treatment, and block. The diameter (dap) was used with continuous input variables. For training the networks, we used two samples. Sub-sample 1 was composed of the first tree of each block. In sub-sample 2, the tree was selected randomly within each block. This selection was made in both tests. The selected data were separated, with 70% used for training and 30% used for validation. The other unselected trees were used for generalization. For each age and treatment, we used the Kolmogorov-Smirnov (KS) test to verify the normality of the errors. The results show that ANNs can be used to estimate the heights of trees subjected to various experimental plot treatments, with no loss of accuracy or estimation precision. |
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Revista Árvore (Online) |
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ESTIMATION OF EUCALYPTUS TREE HEIGHT IN CLONAL AND PROGENY TESTS USING ARTIFICIAL NEURAL NETWORKSCostPredictionExperimentABSTRACT The goal of this study was to test the applicability of artificial neural networks for estimating tree heights in clonal tests and progenies. We used data from 8,329 clonal tests collected for six age groups, divided into six blocks and five repetitions. For the progeny tests, we used 36,793 data points, collected at age 5 and divided into ten blocks and five repetitions. The categorical input variables considered were age, treatment, and block. The diameter (dap) was used with continuous input variables. For training the networks, we used two samples. Sub-sample 1 was composed of the first tree of each block. In sub-sample 2, the tree was selected randomly within each block. This selection was made in both tests. The selected data were separated, with 70% used for training and 30% used for validation. The other unselected trees were used for generalization. For each age and treatment, we used the Kolmogorov-Smirnov (KS) test to verify the normality of the errors. The results show that ANNs can be used to estimate the heights of trees subjected to various experimental plot treatments, with no loss of accuracy or estimation precision.Sociedade de Investigações Florestais2017-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-67622017000600205Revista Árvore v.41 n.6 2017reponame:Revista Árvore (Online)instname:Universidade Federal de Viçosa (UFV)instacron:SIF10.1590/1806-90882017000600002info:eu-repo/semantics/openAccessSantos,Ana Carolina de AlbuquerqueAlmeida,Filipe MonteiroSouza,Ramon BarretoChaves,RaulPaiva,Haroldo Nogueira deBinot,Daniel Henrique BredaLeite,Helio GarciaFarias,Aline Araújoeng2018-06-11T00:00:00Zoai:scielo:S0100-67622017000600205Revistahttp://www.scielo.br/revistas/rarv/iaboutj.htmPUBhttps://old.scielo.br/oai/scielo-oai.php||r.arvore@ufv.br1806-90880100-6762opendoar:2018-06-11T00:00Revista Árvore (Online) - Universidade Federal de Viçosa (UFV)false |
dc.title.none.fl_str_mv |
ESTIMATION OF EUCALYPTUS TREE HEIGHT IN CLONAL AND PROGENY TESTS USING ARTIFICIAL NEURAL NETWORKS |
title |
ESTIMATION OF EUCALYPTUS TREE HEIGHT IN CLONAL AND PROGENY TESTS USING ARTIFICIAL NEURAL NETWORKS |
spellingShingle |
ESTIMATION OF EUCALYPTUS TREE HEIGHT IN CLONAL AND PROGENY TESTS USING ARTIFICIAL NEURAL NETWORKS Santos,Ana Carolina de Albuquerque Cost Prediction Experiment |
title_short |
ESTIMATION OF EUCALYPTUS TREE HEIGHT IN CLONAL AND PROGENY TESTS USING ARTIFICIAL NEURAL NETWORKS |
title_full |
ESTIMATION OF EUCALYPTUS TREE HEIGHT IN CLONAL AND PROGENY TESTS USING ARTIFICIAL NEURAL NETWORKS |
title_fullStr |
ESTIMATION OF EUCALYPTUS TREE HEIGHT IN CLONAL AND PROGENY TESTS USING ARTIFICIAL NEURAL NETWORKS |
title_full_unstemmed |
ESTIMATION OF EUCALYPTUS TREE HEIGHT IN CLONAL AND PROGENY TESTS USING ARTIFICIAL NEURAL NETWORKS |
title_sort |
ESTIMATION OF EUCALYPTUS TREE HEIGHT IN CLONAL AND PROGENY TESTS USING ARTIFICIAL NEURAL NETWORKS |
author |
Santos,Ana Carolina de Albuquerque |
author_facet |
Santos,Ana Carolina de Albuquerque Almeida,Filipe Monteiro Souza,Ramon Barreto Chaves,Raul Paiva,Haroldo Nogueira de Binot,Daniel Henrique Breda Leite,Helio Garcia Farias,Aline Araújo |
author_role |
author |
author2 |
Almeida,Filipe Monteiro Souza,Ramon Barreto Chaves,Raul Paiva,Haroldo Nogueira de Binot,Daniel Henrique Breda Leite,Helio Garcia Farias,Aline Araújo |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Santos,Ana Carolina de Albuquerque Almeida,Filipe Monteiro Souza,Ramon Barreto Chaves,Raul Paiva,Haroldo Nogueira de Binot,Daniel Henrique Breda Leite,Helio Garcia Farias,Aline Araújo |
dc.subject.por.fl_str_mv |
Cost Prediction Experiment |
topic |
Cost Prediction Experiment |
description |
ABSTRACT The goal of this study was to test the applicability of artificial neural networks for estimating tree heights in clonal tests and progenies. We used data from 8,329 clonal tests collected for six age groups, divided into six blocks and five repetitions. For the progeny tests, we used 36,793 data points, collected at age 5 and divided into ten blocks and five repetitions. The categorical input variables considered were age, treatment, and block. The diameter (dap) was used with continuous input variables. For training the networks, we used two samples. Sub-sample 1 was composed of the first tree of each block. In sub-sample 2, the tree was selected randomly within each block. This selection was made in both tests. The selected data were separated, with 70% used for training and 30% used for validation. The other unselected trees were used for generalization. For each age and treatment, we used the Kolmogorov-Smirnov (KS) test to verify the normality of the errors. The results show that ANNs can be used to estimate the heights of trees subjected to various experimental plot treatments, with no loss of accuracy or estimation precision. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-67622017000600205 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-67622017000600205 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1806-90882017000600002 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade de Investigações Florestais |
publisher.none.fl_str_mv |
Sociedade de Investigações Florestais |
dc.source.none.fl_str_mv |
Revista Árvore v.41 n.6 2017 reponame:Revista Árvore (Online) instname:Universidade Federal de Viçosa (UFV) instacron:SIF |
instname_str |
Universidade Federal de Viçosa (UFV) |
instacron_str |
SIF |
institution |
SIF |
reponame_str |
Revista Árvore (Online) |
collection |
Revista Árvore (Online) |
repository.name.fl_str_mv |
Revista Árvore (Online) - Universidade Federal de Viçosa (UFV) |
repository.mail.fl_str_mv |
||r.arvore@ufv.br |
_version_ |
1750318002615615488 |