African Journal of
Agricultural Research

  • Abbreviation: Afr. J. Agric. Res.
  • Language: English
  • ISSN: 1991-637X
  • DOI: 10.5897/AJAR
  • Start Year: 2006
  • Published Articles: 6574

Full Length Research Paper

Artificial neural networks in predicting energy density of Bambusa vulgaris in Brazil

Ailton Teixeira do Vale
  • Ailton Teixeira do Vale
  • University of Brasilia (UnB), Brazil.
  • Google Scholar
Eder Pereira Miguel
  • Eder Pereira Miguel
  • University of Brasilia (UnB), Brazil.
  • Google Scholar
Alessandro Cezar de Oliveira Moreira
  • Alessandro Cezar de Oliveira Moreira
  • University of Brasilia (UnB), Brazil.
  • Google Scholar
Clarissa Melo Lima
  • Clarissa Melo Lima
  • University of Brasilia (UnB), Brazil.
  • Google Scholar
Bruna Barbara Maciel Amoras Orellana
  • Bruna Barbara Maciel Amoras Orellana
  • University of Brasilia (UnB), Brazil.
  • Google Scholar
Myla Medeiros Fortes
  • Myla Medeiros Fortes
  • University of Brasilia (UnB), Brazil.
  • Google Scholar
Mayara Paula Oliveira Machado
  • Mayara Paula Oliveira Machado
  • University of Brasilia (UnB), Brazil.
  • Google Scholar
Joaquim Carlos Gonçalez
  • Joaquim Carlos Gonçalez
  • University of Brasilia (UnB), Brazil.
  • Google Scholar
Ildeu Soares Martins
  • Ildeu Soares Martins
  • University of Brasilia (UnB), Brazil.
  • Google Scholar


  •  Received: 15 December 2016
  •  Accepted: 16 February 2017
  •  Published: 09 March 2017

Abstract

In this study, the physical and chemical characteristics of Bambusa vulgaris ex J.C. Wendl. var.vulgaris (Bambusa vulgaris) aged 1, 2 and 3 years were evaluated. The objective was to train, validate and evaluate the efficiency of artificial neural networks (ANNs) as predictive tools to estimate bamboo stem energy density grown commercially in northeastern Brazil. For that, samples were collected in a commercial plantation and managed for energy production, determining the energy properties. Among all the characteristics analyzed, basic apparent density was the one with major correlation with bamboo stem energy density. This factor has a great advantage because it is easy to estimate, determined both by dry mass at 0% moisture, and at saturated mass. Also, the precision of ANNs was verified when associated with basic density, as a predictor of bamboo stem energy density, showing low standard error (Syx%, 1.52) and high coefficient of determination (R² = 0.98). ANN-estimated values had no statistical difference (tcal 0.58 ≤ ttab 2.08) with energy density estimated in the laboratory. Therefore, this tool was efficient, being recommended to predict the energetic density of the species under study, with basic density as the only predictive variable.

 

Key words: Bamboo, biomass, energy, artificial intelligence.