Título:	Predicting teak tree (Tectona grandis Linn F.) height using generic models and artificial neural networks
Autor(es):	Almeida, Mariana Pacheco de Miguel, Eder Pereira Santos, Mario Lima dos Gaspar, Ricardo de Oliveira Santos, Cassio Rafael Costa dos Raddatz, Dione Dambrós Martin, Walmer Bruno Rocha Matricardi, Eraldo Aparecido Trondoli
ORCID:	https://orcid.org/0000-0001-6259-4594 https://orcid.org/0000-0002-2035-2180 https://orcid.org/0000-0001-9356-0186 https://orcid.org/0000-0001-6538-5763 https://orcid.org/0000-0002-5323-6100
Afiliação do autor:	Federal University of Lavras, Department of Forest Engineering University of Brasilia, Department of Forest Engineering University of Brasilia, Department of Forest Engineering University of Brasilia, Department of Forest Engineering Federal Rural University of Amazon University of Brasilia, Department of Forest Engineering Federal Rural University of Amazon University of Brasilia, Department of Forest Engineering
Assunto:	Redes neurais artificiais Tectona grandis Estimativas de altura Manejo florestal Amazônia Plantio (Cultivo de plantas)
Data de publicação:	Nov-2022
Editora:	Southern Cross Publishing
Referência:	ALMEIDA, Mariana Pacheco de et al. Predicting teak tree (Tectona grandis Linn F.) height using generic models and artificial neural networks. Australian Journal of Crop Science, [S.l], v. 16, n. 11, p. 1243-1252, 2022. DOI: 10.21475/ajcs.22.16.11.p3736. Disponível em: https://www.cropj.com/november2022.html. Acesso em: 16 jul. 2025.
Abstract:	The continuous monitoring of dendrometric variables provides estimates that assist in conducting fast-growing stands. In this study, we aimed to investigate the performance of generic models and artificial neural networks to estimate total height of Tectona grandis in a forest stand in the Eastern Amazon. Continuous forest inventory was performed in this population, where measured of total height and diameter at breast height. These variables, age and the square root of the average diameter (dg) of the plots, were used to compose the methods adopted to estimate the height of the trees. The accuracy of these methods was assessed using the residual standard error of the estimate, the coefficient of correlation, and the graphical analysis of residues. The aggregated difference and ANOVA were calculated to compare the methods. The independent variables mentioned were able to describe the behavior of individuals at height. We concluded that the methods showed good residual dispersion, normal distribution of errors and little tendency to overestimate height. It was found that the generic models and the ANNs do not differ significantly from each other and are efficient to estimate the height of individuals. We also concluded that the ANNs, especially those that included dg, presented superior statistical indicators.
Unidade Acadêmica:	Faculdade de Tecnologia (FT) Departamento de Engenharia Florestal (FT EFL)
Programa de pós-graduação:	Programa de Pós-Graduação em Ciências Florestais
Licença:	All the contents of this journal is licensed under a CC-BY-NC. AJCS does not have any commercial interest in the scientific contents of the journal. Fonte: https://www.cropj.com/about.html. Acesso em: 11 mar. 2025.
DOI:	10.21475/ajcs.22.16.11.p3736
Aparece nas coleções:	Artigos publicados em periódicos e afins

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