ESTIMATION OF EUCALYPTUS TREE HEIGHT IN CLONAL AND PROGENY TESTS USING ARTIFICIAL NEURAL NETWORKS
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 36,79...
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Sociedade de Investigações Florestais
2018-06-01
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doaj-b40e375110614a9c801656abdced39392020-11-25T00:43:21ZengSociedade de Investigações FlorestaisRevista Árvore1806-90882018-06-0141610.1590/1806-90882017000600002S0100-67622017000600205ESTIMATION OF EUCALYPTUS TREE HEIGHT IN CLONAL AND PROGENY TESTS USING ARTIFICIAL NEURAL NETWORKSAna Carolina de Albuquerque SantosFilipe Monteiro AlmeidaRamon Barreto SouzaRaul ChavesHaroldo Nogueira de PaivaDaniel Henrique Breda BinotHelio Garcia LeiteAline Araújo FariasABSTRACT 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.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-67622017000600205&lng=en&tlng=enCustoPrediçãoExperimento |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ana Carolina de Albuquerque Santos Filipe Monteiro Almeida Ramon Barreto Souza Raul Chaves Haroldo Nogueira de Paiva Daniel Henrique Breda Binot Helio Garcia Leite Aline Araújo Farias |
spellingShingle |
Ana Carolina de Albuquerque Santos Filipe Monteiro Almeida Ramon Barreto Souza Raul Chaves Haroldo Nogueira de Paiva Daniel Henrique Breda Binot Helio Garcia Leite Aline Araújo Farias ESTIMATION OF EUCALYPTUS TREE HEIGHT IN CLONAL AND PROGENY TESTS USING ARTIFICIAL NEURAL NETWORKS Revista Árvore Custo Predição Experimento |
author_facet |
Ana Carolina de Albuquerque Santos Filipe Monteiro Almeida Ramon Barreto Souza Raul Chaves Haroldo Nogueira de Paiva Daniel Henrique Breda Binot Helio Garcia Leite Aline Araújo Farias |
author_sort |
Ana Carolina de Albuquerque Santos |
title |
ESTIMATION OF EUCALYPTUS TREE HEIGHT IN CLONAL AND PROGENY TESTS USING ARTIFICIAL NEURAL NETWORKS |
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 |
publisher |
Sociedade de Investigações Florestais |
series |
Revista Árvore |
issn |
1806-9088 |
publishDate |
2018-06-01 |
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. |
topic |
Custo Predição Experimento |
url |
http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-67622017000600205&lng=en&tlng=en |
work_keys_str_mv |
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