Wood density estimation using dendrometric and edaphoclimatic data in artificial neural networks

Authors

DOI:

https://doi.org/10.5965/223811712242023685

Keywords:

artificial intelligence, modeling, wood quality

Abstract

Forestry measurement is aimed at volumetric production of wood; however, for the pulp processing industry, the main interest is productivity in wood biomass and, to know this variable, it is necessary to determine the basic wood density (BWD) beforehand. Artificial neural networks (ANN) have been used in the forestry sector quite successfully to describe the dynamics of forest characteristics, such as estimating wood volume. In this context, the objective of this study was to assess the accuracy of the basic wood density estimates by means of ANN’s with Continuous Forest Inventory (CFI) and edaphoclimatic input variables. The database consisted of 3,797 data, from permanent plots of the CFI conducted in Eucalyptus sp stands and edaphoclimatic data from the planting sites. The five best ANNs were selected and the analysis of the estimates was carried out through the correlation between the estimated and BWD, the relative root mean square error (RMSE%) and graphical information. It was observed that both the CFI, edaphoclimatic information and the combination of both are potential and present similar results for the basic wood density estimate, and the errors associated with the estimates are between 3.9% to 3.5%.  The ANNs based only on the CFI information presented higher RMSE. The use of ANN’s is feasible for estimating BWD and allows for excellent accuracy statistics.

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Author Biographies

Mauro Antônio Pereira Werneburg, Cenibra S.A – Celulose Nipo-Brasileira, Belo Horizonte, MG, Brazil

.

Mayra Luiza Marques da Silva, Federal University of São João del Rei

.

Helio Garcia Leite, Federal University of Viçosa

.

Antonilmar Araújo Lopes da Silva, Cenibra S.A – Celulose Nipo-Brasileira, Belo Horizonte, MG, Brazil

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José Marinaldo Gleriani, Federal University of Viçosa

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Jeferson Pereira Martins Silva, Federal University of Espírito Santo

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Tais Rizzo Moreira, Federal University of Espírito Santo

.

Sofia Maria Gonçalves Rocha, Federal University of Espírito Santo

.

Nívea Maria Mafra Rodrigues, Federal University of Espírito Santo

.

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Published

2023-12-29

How to Cite

WERNEBURG, Mauro Antônio Pereira; SILVA, Mayra Luiza Marques da; LEITE, Helio Garcia; SILVA, Antonilmar Araújo Lopes da; GLERIANI, José Marinaldo; SILVA, Jeferson Pereira Martins; MOREIRA, Tais Rizzo; ROCHA, Sofia Maria Gonçalves; RODRIGUES, Nívea Maria Mafra. Wood density estimation using dendrometric and edaphoclimatic data in artificial neural networks. Revista de Ciências Agroveterinárias, Lages, v. 22, n. 4, p. 685–694, 2023. DOI: 10.5965/223811712242023685. Disponível em: https://www.revistas.udesc.br/index.php/agroveterinaria/article/view/23732. Acesso em: 14 dec. 2024.

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Section

Research Article - Multisections and Related Areas

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