- Research article
- Open Access
Phenotyping date palm varieties via leaflet cross-sectional imaging and artificial neural network application
© Arinkin et al.; licensee BioMed Central Ltd. 2014
- Received: 5 August 2013
- Accepted: 14 February 2014
- 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
- Date palm leaf
- Vascular bundles
We may ask ourselves why care about date palms (Phoenix dactylifera)? The simple answer is: This tree and its fruits were and are important nutrition for humans living in tropical and subtropical countries . The total number of date palm trees in 2001 was about 100 million, distributed over 30 countries producing between 2.5 and 5 million tonnes of fruit per year ; the FAO  estimated the fruit production to be 7.5 million tonnes for 2010. Interest in the differentiation of date palm cultivars is very great, since high fruit quality and quantity are desired and offshoot leaves of different cultivars look alike to a great extent. Early recognition of cultivar and gender is particular important, due to huge expenses for the growth of at least 8–10 years old trees before they start to bear fruit and their cultivar can be confirmed . As for culturing dates in modern times, offshoots are cut off from mother plants, put in pure sand and watered every day. After 12–15 years, female trees produce fruits differing a lot in quality and quantity. Nowadays tissue culture methods could be used to clone date palms, but there are relatively high chances for spontaneous mutations leading to genotype (and phenotype) changes .
The general problem of phenotype description has begun from Wilhelm Johannsen in 1911  by defining the phenotype term, and currently experience a huge agricultural interest in a machine learning based and automate acquisition of phenotypic traits [7–9]. In the date palm agriculture there is a need for early confirmation of a cultivar due to high genetic diversity , where machine vision characterisation of a plant’s cultivars can be used to support subjective human observations. To achieve statistically reliable data with the help of modern technology while performing a realistic amount of measurements, the methods used need to be robust and effective. Many phenotype-oriented techniques for date palm cultivars differentiation such as analysis of extracts of fruits and leaves with SDS-PoroPAGE , RP-HPLC , as well as description of vegetative and reproductive traits [13–15], growth, flowering and yield characters  have been reported to be successful. Additionally for other plant cultivars, RP-HPLC/Mass spectrometry  and capillary zone electrophoresis  techniques have been used. Unfortunately predictive models which would open up easy possibilities for practical applications have not been used in the above-mentioned works. A good example of such application would be work of Wu et al. .
Along with phenotype analyses, genotyping-oriented techniques such as genetic fingerprinting by using random amplified polymorphic DNA (RAPD) markers and inter simple sequence repeat (ISSR) markers [20, 21] or analysis of leaflet isozymes expression as a genetic marker [22–24] have been used to study the genetic diversity of date palm cultivars. Although the results achieved with these techniques are very good, our intent has been to test the feasibility of approach with focus on phenotypic features and a future possible field application.
Date palm leaves
Samples have been collected from trees of the National Date Palm Research Centre, Saudi Arabia. Leaflets have been collected from the middle part of a pinnae area (an upper part) of the date palm leaf blade. The trees have been growing in similar conditions in the same area.
Leaflets of date palm leaves have been carefully washed with regular warm (35-40°C) water to remove dirt, then washed with room-temperature (25°C) deionized water and wiped with soft cellulose tissues. Leaflets are stored further under nitrogen gas atmosphere (Quality 5.0, ≥ 99.999% pure) to protect them from degradation by aerobic microorganisms and oxidation.
In order to obtain a cross section of a date palm leaflet, it was first precooled (4°C) and fixed with paraffin wax (Roti®-Plast (melting point 56-58°C) from Carl-Roth GmbH + Co. KG, Germany) in a histological sample holder. A 40 μm thick cross section was produced using a microtome (R Jung AG Heidelberg, Germany) and then placed with isotonic 0.9% NaCl (from Carl-Roth GmbH + Co. KG, Germany) water solution on a microscope slide and then covered with a cover glass.
For the acquisition of fluorescence images, a Keyence BZ-8100E fluorescence microscope (Keyence Corp., Osaka, Japan) equipped with a true colours CCD sensor (2/3”, 1.5 megapixels) was used. The following three filters sets (excitation, absorption) were used: DAPI-BP (320-400 nm, 410–510 nm), GFP-BP (430–510 nm, 485–585 nm), Texas-Red (520–600 nm, 570–690 nm) together with a zoom objective CFI Plan Apo VC 20X (Nikon Corp., Tokyo, Japan).
- 1)For characterisation of a MnVB distribution the following parameters have been introduced:
Number of MnVBs between two MjVBs
The baseline is defined as a line between the centres of two rectangles exactly fitting manually the width of two MjVBs, and the height of the cross-section, see Figure 1. MnVB centres, on the other hand, are defined by fitting manually MnVBs with ovals and calculating the centres of this ovals and number.
For obtaining the Ratio an absolute value of the perpendicular line length connecting a MnVB centre and the baseline have been added and then divided by the baseline length.
For obtaining the Ratio2 an absolute value of the line length connecting MnVB centre and centre of the baseline have been added and then divided by the baseline length.
To describe a MjVB shape following parameters have been introduced:
The Form factor is intended to describe a deviation of a MjVB shape from a perfect circular shape, whereas Rectangularity describes a deviation of a MjVB shape from a rectangle. Additionally, Aspect ratio describes the proportional relationship between its width and its height.
Ellipse fit residual error = Residual error after fitting a shape of MjVB with an ellipse
Artificial neural network
In order to use obtained parameters (4 of the MnVB distribution and 5 of the MjVB shape) from fluorescence images for differentiation of date palm tree cultivars, an artificial neural network (ANN) has been applied. In particular, a multilayer perceptron with bias architecture under supervised learning (backpropagation learning rule) has been used due to reportedly better results for data pattern recognition . This ANN has been built and tested with the help of IBM SPSS software package ver. 19 (IBM Corp., New York, USA).
The hidden layer activation function has been taken as a hyperbolic tangent tan(x) = (e x - e- x)/(e x + e- x), whereas for the output layer – a softmax function which takes a vector of real-valued arguments and transforms it to a vector whose elements fall in the range (0, 1) and sum to 1 was used. Input variables have been rescaled with a method called standardisation, in which from each value the mean of all values is subtracted and divided by its standard deviation, (x - mean)/std. dev. The cross-entropy error function has been chosen due to a better network performance compared to the mean square error function .
A summary of samples used for processing by the best ANN out of 10 in the cross-folding
Per each cultivar
Hewlat al Jouf
The best ANN training and testing result
Percent of correct predicted
Hewlat al Jouf
Overall per cent
Overall per cent of correct predicted from tenfold cross-validation of ANN
Overall per cent of correct predicted
Variable importance analysis of the best ANN
Many phenotypic studies of date palm tree cultivars utilize features specific to a certain time or age of a tree [14, 15, 28]. Analysis of fruit characteristics or protein extracts of them is unfortunately not an all-season application. Moreover, characterisation of fruits by their taste and flesh structure is often also quite subjective. In the same manner description of the whole date palm leaf or trunk is then restricted to the adult trees only. In contrast, early detection is of major interest for current date palm tree agriculture before a huge investment is made in the growth of plant of unknown properties .
In light of this situation a method for date palm trees differentiation should be based on features which can be readily obtained from date palm offshoots. One of these objects for feature extraction is date palm tree leaflets.
Among other types of ANN used in this work, the multilayer perceptron showed the best result and easy learning, which could be related to some correlations between extracted features. PCA revealed two or three meaningful clusters, where positive as well as negative correlations exist in clusters. Despite the fact that it is possible according to the statistical results to reduce some parameters, the application of a diminished set of features into the ANN has showed a decrease in prediction rate. These results lead to the conclusion that although parameters from MnVB or MjVB share some common information, they carry vital specific features information necessary for a better ANN performance.
As it has been mentioned before, parallel genetic studies to clarify the actual differences between cultivars would be very helpful [20, 21, 29]. An additional step in the direction of an industrial application could be done by possible usage of fluorescence cross section images of lower resolution, or ideally just regular light images of cross sections.
Moreover a fluorescence imaging with an artificial neural network analysis could be applied to other members of the Phoenix genus as well as for other vascular plants with linear vascular venation patterns, like maize (corn) and rice. For plants with a net-like vascular system, a different set of features need to be identified except keeping an idea of ANN usage for classification and differentiation. However the technology enabling image acquisition and handling on living trees in a plantation still remains to be developed.
Overall an achieved result in prediction and differentiation of date palm tree cultivars based on the fluorescence microscopy of palm leaflets cross sections with the help of the artificial neural network was very good. The average prediction in tenfold cross-validation 89.1% and 100% in one of the best ANN’s can be considered as very promising results, in spite of only a total of 85 sample data being used in the ANN. Additionally, the fact that only 5 cultivars have been used in this study also needs to be taken into account by extrapolating this result to the general problem of date palm tree cultivars differentiation.
The authors wish to thank various people for their contribution to this project; Mr. Prince Faisal Al-Saud, CEO of the ERAM Holding, Jeddah, Saudi Arabia, and his group for their valuable support on this project and continuous collaboration. We thank Mr. Adnan A. Al-Afaleq, General Director of the Date Palm Research Centre, Saudi Arabia for their help in collecting date palm leaves, for leaf preparation and shipments of samples to our laboratory. We would like to also thank Mr. Dipl.-Ing. Peter Kayser for his support in organisational issues.
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