African Journal of
Agricultural Research

  • Abbreviation: Afr. J. Agric. Res.
  • Language: English
  • ISSN: 1991-637X
  • DOI: 10.5897/AJAR
  • Start Year: 2006
  • Published Articles: 6576

Full Length Research Paper

Artificial neural networks in whole-stand level modeling of Eucalyptus plants

Mirella Basileu de Oliveira Lima
  • Mirella Basileu de Oliveira Lima
  • Department of Forest Engineering, University of Brasília, Brasília, DF, 70910-900, Brazil. Mirella Basileu de Oliveira Lima, Ilvan Medeiros Lustosa Junior, Elian Meneses Oliveira, Jéssica Cristina Barbosa Ferreira, Kálita Luis Soares and Eder Pereira Miguel*
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Ilvan Medeiros Lustosa Junior
  • Ilvan Medeiros Lustosa Junior
  • Department of Forest Engineering, University of Brasília, Brasília, DF, 70910-900, Brazil. Mirella Basileu de Oliveira Lima, Ilvan Medeiros Lustosa Junior, Elian Meneses Oliveira, Jéssica Cristina Barbosa Ferreira, Kálita Luis Soares and Eder Pereira Miguel*
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Elian Meneses Oliveira
  • Elian Meneses Oliveira
  • Department of Forest Engineering, University of Brasília, Brasília, DF, 70910-900, Brazil. Mirella Basileu de Oliveira Lima, Ilvan Medeiros Lustosa Junior, Elian Meneses Oliveira, Jéssica Cristina Barbosa Ferreira, Kálita Luis Soares and Eder Pereira Miguel*
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Jéssica Cristina Barbosa Ferreira
  • Jéssica Cristina Barbosa Ferreira
  • Department of Forest Engineering, University of Brasília, Brasília, DF, 70910-900, Brazil. Mirella Basileu de Oliveira Lima, Ilvan Medeiros Lustosa Junior, Elian Meneses Oliveira, Jéssica Cristina Barbosa Ferreira, Kálita Luis Soares and Eder Pereira Miguel*
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Kálita Luis Soares
  • Kálita Luis Soares
  • Department of Forest Engineering, University of Brasília, Brasília, DF, 70910-900, Brazil. Mirella Basileu de Oliveira Lima, Ilvan Medeiros Lustosa Junior, Elian Meneses Oliveira, Jéssica Cristina Barbosa Ferreira, Kálita Luis Soares and Eder Pereira Miguel*
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Eder Pereira Miguel
  • Eder Pereira Miguel
  • Department of Forest Engineering, University of Brasília, Brasília, DF, 70910-900, Brazil. Mirella Basileu de Oliveira Lima, Ilvan Medeiros Lustosa Junior, Elian Meneses Oliveira, Jéssica Cristina Barbosa Ferreira, Kálita Luis Soares and Eder Pereira Miguel*
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  •  Received: 12 December 2016
  •  Accepted: 19 January 2017
  •  Published: 16 February 2017

Abstract

Forestry production is traditionally predicted using mathematical modelling, where whole-stand models are prominent for providing estimates of growth and production per unit area. However, there is a need to perform research that adopts innovative tools, such as artificial Intelligence techniques. The objective of this study was to train and evaluate the efficiency of Artificial Neural Networks (ANN) in the modeling process of growth and production of Whole-Stand Level, in “equineanean” forests of the Eucalyptus genus clones. For the training of the networks, the supervised method was adopted. There were 100 networks trained, of which, for each output variable, 5 networks were selected. The criteria used to verify the quality of the training and validation of the networks were: Student's test “T”, graphical analysis of the dispersion of residues, standard error of the relative estimate (Syx%), Pearson's correlation coefficient (r) between the observed and estimated values, ​​and aggregate difference (AD). The ANN selected in the training process, which estimated the volume variables, site index and future production, when validated did not differ statistically by the Test "T" and presented adequate statistical accuracy values ​​to the modeling process, with satisfactory correlation values ​​(r) between the observed and estimated values, low values ​​of standard error of the relative estimate (Syx%) and aggregate difference (AD). Artificial neural networks of the multilayer perceptron type are precise and efficient in the entire modeling process for whole-stand level of Eucalyptus plantations, therefore its use is recommended.

Key words: Artificial intelligence, prognosis, site classification, Brazil.