TY  - JOUR
A1  - Arinkin, Vladimir
A1  - Digel, Ilya
A1  - Porst, Dariusz
A1  - Temiz Artmann, Aysegül
A1  - Artmann, Gerhard
T1  - Phenotyping date palm varieties via leaflet cross-sectional imaging and artificial neural network application
T2  - BMC bioinformatics
N2  - 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.
Y1  - 2014
UR  - https://opus.bibliothek.fh-aachen.de/opus4/frontdoor/index/index/docId/6388
SN  - 1471-2105
VL  - 15
IS  - 55
SP  - 1
EP  - 8
ER  -