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

BMC Bioinformatics

Gerhard M Artmann¹  Aysegül Temiz Artmann¹  Dariusz Porst¹  Ilya Digel¹  Vladimir Arinkin¹ 

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

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

Others  :  1087613
DOI  :  10.1186/1471-2105-15-55

 received in 2013-08-05, accepted in 2014-02-14,  发布年份 2014

【 摘 要 】

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.

【 授权许可】

   
2014 Arinkin et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

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