title: Phenotyping date palm varieties via leaflet cross-sectional imaging and artificial neural network application.
abstract: BACKGROUND
  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).
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Institute for Bioengineering (IFB), Aachen University of Applied Sciences, Heinrich-Mussmann-Str, 1, 52428 Juelich, Germany. arinkin.vladimir@yahoo.com.
authors.forename: Vladimir
authors.initials: V
authors.lastname: Arinkin
authors.forename: Ilya
authors.initials: I
authors.lastname: Digel
authors.forename: Dariusz
authors.initials: D
authors.lastname: Porst
authors.forename: Aysegül Temiz
authors.initials: AT
authors.lastname: Artmann
authors.forename: Gerhard M
authors.initials: GM
authors.lastname: Artmann
mesh_terms: D000465:Algorithms; D028023:Arecaceae / Q000145:classification* / Q000648:ultrastructure*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008856:Microscopy, Fluorescence; D016571:Neural Networks, Computer; D010641:Phenotype; D018515:Plant Leaves / Q000648:ultrastructure*