African Journal of
Agricultural Research

  • Abbreviation: Afr. J. Agric. Res.
  • Language: English
  • ISSN: 1991-637X
  • DOI: 10.5897/AJAR
  • Start Year: 2006
  • Published Articles: 6358

Full Length Research Paper

Leaf detector box: Artificial vision system for leaf area identification

Cristian Manuel Agudelo Restrepo
  • Cristian Manuel Agudelo Restrepo
  • Departamento de Ingeniería, Semillero de Investigación KINESTASIS, Universidad de Cundinamarca, Colombia
  • Google Scholar
Edgar Eduardo Roa-Guerrero
  • Edgar Eduardo Roa-Guerrero
  • Departamento de Ingeniería, Semillero de Investigación KINESTASIS, Universidad de Cundinamarca, Colombia
  • Google Scholar
Humberto Numpaque López
  • Humberto Numpaque López
  • Departamento de Ingeniería, Grupo de Investigación GITEINCO, Universidad de Cundinamarca, Colombia
  • Google Scholar


  •  Received: 12 September 2016
  •  Accepted: 14 October 2016
  •  Published: 18 May 2017

Abstract

The detection of leaf area in plants is a very important parameter to evaluate growth rates. Nowadays, this analysis is performed manually through a process of counting, cutting and weighing leaf that involves long periods of time, providing subjective results and with low repeatability. The purpose of this research was to develop a computational tool (leaf detector box) to detect leaf area through image processing techniques. The methodology implemented based on application of image processing techniques to segment leaves. Later, morphological operations were apply to eliminate objects that were not part of leaf. Finally, the leaf area was identified through a conversion of pixel to squared centimeter. The validation results showed a determination coefficient greater than 0.97 for four species of plants analyzed with regard to manual analysis by a technical expert. In addition, it validated in a set of 40 leaves precision of algorithm implemented carrying on three measurements for each one at different positions. The results showed a variance of 0.00024 for orange tree leaves, 0.007 for lemon tree leaves, 0.008 for almond tree leaves and 0.001 for mango tree leaves, indicating precision of algorithm providing similar results when it was applied in different opportunities to one leaf. Therefore, the tool becomes a reliable technological support in process of detection leaf area allowing reducing long periods of time and subjectivity in process. Likewise, the repeatability in results were increased.  

 

Key words: Leaf area, growth rates, computational tool, image processing, morphological operations, coefficient of determination.