Open Access Research article

Phenotyping date palm varieties via leaflet cross-sectional imaging and artificial neural network application

Vladimir Arinkin*, Ilya Digel, Dariusz Porst, Aysegül Temiz Artmann and Gerhard M Artmann

Author Affiliations

Institute for Bioengineering (IFB), Aachen University of Applied Sciences, Heinrich-Mussmann-Str. 1, 52428 Juelich, Germany

For all author emails, please log on.

BMC Bioinformatics 2014, 15:55  doi:10.1186/1471-2105-15-55

Published: 24 February 2014



True date palms (Phoenix dactylifera L.) are impressive trees and have served as an indispensable source of food for mankind in tropical and subtropical countries for centuries. The aim of this study is to differentiate date palm tree varieties by analysing leaflet cross sections with technical/optical methods and artificial neural networks (ANN).


Fluorescence microscopy images of leaflet cross sections have been taken from a set of five date palm tree cultivars (Hewlat al Jouf, Khlas, Nabot Soltan, Shishi, Um Raheem). After features extraction from images, the obtained data have been fed in a multilayer perceptron ANN with backpropagation learning algorithm.


Overall, an accurate result in prediction and differentiation of date palm tree cultivars was achieved with average prediction in tenfold cross-validation is 89.1% and reached 100% in one of the best ANN.

Artificial neural network; Backpropagation algorithm; Fluorescence microscopy; Cultivars; Date palm leaf; Vascular bundles; Phenotyping