, Volume 166, Issue 3, pp 411–421 | Cite as

Artificial neural networks as a tool for plant identification: a case study on Vietnamese tea accessions

  • Camilla Pandolfi
  • Sergio Mugnai
  • Elisa Azzarello
  • Silvia Bergamasco
  • Elisa Masi
  • Stefano Mancuso


Seventeen tea accessions belonging to Chinese (Camellia sinensis), Assamic (C. sinensis var. assamica), and Shan tea (C. sinensis var. pubilimba) groups, which are either commercially planted or new promising tea germplasm, were morphologically described at Phu Tho province (Viet Nam) and assessed for their diversity. Fourteen phyllometric parameters were qualitatively and quantitatively investigated using digital image analysis. The accessions were then discriminated by a dedicated artificial neural network for univocal plant identification and a hierarchical cluster analysis was performed in order to build a dendrogram reporting the relationships among them. Results proved the diversity of investigated tea morphotypes from Phu Tho province based on a morphological screening. More, the artificial neural network was able to perform a correct identification for almost all the accessions using simple dedicated instruments.


Camellia sinensis var. assamica C. sinensis var. pubilimba C. sinensis Image analysis Morphotypes Phyllometric parameters 



Artificial neural network


Back-propagation neural network



The Authors would like to thank Mr. Nguyen Huu La, Head of the Department for Genetic Resources of the Tea Research Institute of Viet Nam (TRI), and Mr. Nguyen Van Tao, Director of for the Tea Research Institute of Viet Nam (TRI), for their technical support and assistance.


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Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • Camilla Pandolfi
    • 1
  • Sergio Mugnai
    • 1
  • Elisa Azzarello
    • 1
  • Silvia Bergamasco
    • 1
  • Elisa Masi
    • 1
  • Stefano Mancuso
    • 1
  1. 1.Department of HorticultureUniversity of FlorenceSesto FiorentinoItaly

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