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Signpost Open Access Journal of NanoPhotoBioSciences
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Artificial neural networks and their application in biological
and agricultural research
Izabela A. Samborska1, Vladimir Alexandrov2, Leszek Sieczko3, Bożena Kornatowska4, Vasilij
Goltsev2, Magdalena D. Cetner1, and Hazem M. Kalaji1,*
1
Department of Plant Physiology, Warsaw University of Life Sciences SGGW, 02-776 Warsaw, Poland
Department of Biophysics and Radiobiology, Faculty of Biology, St. Kliment Ohridski
University of Sofia, 8 Dr. Tzankov Blvd., 1164 Sofia, Bulgaria;
3
Department of Experimental Statistics and Bioinformatics, Warsaw University of Life Sciences SGGW, 02
776 Warsaw, Poland
4
Institute of Environmental Protection-NRI, Department of Nature and Landscape Conservation, Krucza
5/11, 00-548 Warsaw, Poland
2
*Corresponding author: Dr. Hab. Hazem M. Kalaji, Phone: +48 664943484, Email: [email protected]
Copyright: Samborska et al., This is an open access article distributed under the Creative Commons
Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided
the original work is properly cited.
Received: July 8, 2014
Accepted: August 14, 2014
Academic Editors: Sandra Stirbet and Harvey Hou
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Samborska et al., (2014) Artificial neural networks and their application in biological and agricultural research. SOAJ
NanoPhotoBioSciences, (2): 14-30.
Samborska et al., (2014) Signpost Open Access J. NanoPhotoBioSciences, 14-30. Volume 02, Article ID 010409, 17 pages. ISSN:
2347- 7342 http://signpostejournals.com
Abstract
In the present paper we show that data analysis using artificial neural networks (ANNs) has been increasingly
applied worldwide in a range of scientific fields, including biological and agricultural research. Based on
ANN, the analysis of results can be obtained in a relatively short time, even when considering lots of data. The
method has become an attractive alternative to accepted statistical methods, and provides mean results which
fit well the pattern of variable and hard–to-foretell phenomena in biological and agricultural systems.
Keywords
artificial neural networks, synaptic weights, ANN in biology, ANN in agriculture, data analysis, artificial
nervous system
Introduction
Artificial neural networks (ANN) are used to handle
experimental data, and their benefits have been more
and more recognized in various fields of technology
and science (such as biology, ecology, physics,
chemistry,
agronomy,
economy,
medicine,
mathematics and computers science). Thanks to their
ability to tackle complex calculation issues, they are
progressively applied to solve practical problems.
The main advantage of ANNs is the fact that tasksolving is done by putting forward input signals
stimulating network capability to learn and
recognize patterns. Sometimes ANN is preferred
over complex algorithms or rule-based programming
for solving various tasks
The aim of constructing ANNs is to create artificial
intelligence inspired by the working of human brain,
even though the latter is not yet fully understood. On
the other hand, one has to bear in mind that each
individual neuron of the nervous system plays a very
important part in the transmission of information.
Thousands of small and independent neurons can act
together, and this allows them to analyze and solve a
wide variety of complex tasks simultaneously; no
machine is able to do this in such a reliable way.
ANNs are based on the idea of adjoining the
computer’s and man’s brain abilities. In a similar
way, the main asset of neural networks is the ability
of their neurons to take part in an analysis while
working simultaneously, but independently from
each other. In other words, the artificial neurons
function as those in the brain, and this provides the
possibility to construct technological systems such
as computers equipped with a variety of programs to
solve complex tasks.
Ever since human beings have observed the universe
and contemplated natural phenomena, unknown
processes have been considered. However even
contemporary sciences are not able to endow the
processes happening in nature and living organisms
with unequivocal explanations. Consequently,
contemporary research concentrates more and more
on biological models, which are worth improving
and developing for further applications. An example
of such a system is the human's brain, which shows
an incredible flexibility in adapting to given
conditions, sometimes in extremely difficult
situations, and to handle or eliminate useless
components. The human’s brain is a natural system
capable of achieving tasks by attaining, verifying
and testing various modes in order to accomplish a
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Samborska et al., (2014) Artificial neural networks and their application in biological and agricultural research. SOAJ
NanoPhotoBioSciences, (2): 14-30.
Samborska et al., (2014) Signpost Open Access J. NanoPhotoBioSciences, 14-30. Volume 02, Article ID 010409, 17 pages. ISSN:
2347- 7342 http://signpostejournals.com
designated goal. Its supplementary assets are
connecting and associating capabilities. Further, the
brain is capable to eliminate damaged elements
without disrupting the operation of the whole
system. Therefore, research on the work of
information systems and on methods to analyze and
process information that may shed light on processes
taking place in the human brain, is very important
(Kosiński 2007).
In spite of considerable development of several
indirectly connected scientific fields as well as
various research methods and techniques, ANNs
mimic the work of man’s nervous system only
partially. Thus, the help of multifaceted tools, such
as relevant computer programs, algorithms and other
mathematical tools are needed when ANNs are used
for analyses. Research has been carried out on
various algorithms that can be applied in the
analyses performed by ANNs (Graupe 2013 ). This
approach allows the processes of network learning,
and can make man’s work easier, especially when
dealing with complex tasks where traditional
statistical methods cannot be applied.
ANNs use in plant biology seems to be an
interesting field of research lately. Data concerning
plant tissues are habitually classified as continuous
data (e.g. size, weight), and they are frequently
analyzed with statistical methods such as “analysis
of variance” (ANOVA). However, only normally
distributed or scattered data can be analyzed this
way. For much complex data, as those obtained in
biological studies, different methods of analysis
should be applied, as methods using ANNs.
The study carried out by Gago et al. (2010) showed
that ANNs used models are useful tools in modeling
intricate and non-linear relationships contingent on
data not visible at first sight. The authors showed
that an ANN can achieve good results in the field of
biology, and especially in plant studies. However, if
the analysis is to be successful, the data should be
optimized by taking into consideration various
factors, e.g. environmental or genetic. Thanks to
ANNs, variables can be independently introduced
into the network and factor permutations can be
foreseen. In an analysis of this kind, one should use
as input as much data as possible as a function of
different factors. In this case, ANNs are capable of
rejecting unnecessary ones and of selecting those
most important to achieve sound results (Gago et al.
2010). Learning performed by a network runs
automatically and it is based on the selection of
appropriate values of weights (see more details
later).
When using ANN analysis, there are two major
learning paradigms, each corresponding to a
particular abstract learning task. These are:
supervised learning (with the so called “teacher”)
and unsupervised learning (without “teacher”). The
first paradigm is used when there is a possibility to
verify the answers given by the network. In this
case, for each input vector (for example in agronomy
these can be e.g., soil quality, nutrients, and
cropping year (Wieland and Mirschel 2008), the
value of the output vector is known which is the
exact solution to a given task. The second learning
paradigm is applied when the solution is not known.
As an example of ANN application in plant biology
we mention their use in the analysis of chlorophyll
fluorescence data (Maldonado-Rodriguez et al.
2003; Kirova et al. 2009; Salazar et al. 2009;
Goltsev et al. 2012; Ferreira and Galo 2013), where
among various mathematical models, ANN proved
to be a very useful tool. The method was able to
show expected and true results with as high as 95%
accuracy in the recognition of different plant species
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Samborska et al., (2014) Artificial neural networks and their application in biological and agricultural research. SOAJ
NanoPhotoBioSciences, (2): 14-30.
Samborska et al., (2014) Signpost Open Access J. NanoPhotoBioSciences, 14-30. Volume 02, Article ID 010409, 17 pages. ISSN:
2347- 7342 http://signpostejournals.com
(Kirova et al. 2009) or the determination of the
water content in leaf tissue (Goltsev et al. 2012).
This proves that the development and improvement
of this method for biological research is necessary
and very promising (Tyystjärvi et al. 1999).
Earlier ANN models
Each individual neuron in the nervous system works
independently, but as part of a network it transmits
information obtained from prior neurons to further
ones. In the case of artificial neural networks, this
means that a given neuron sums up input signals
with appropriate weight values obtained from prior
neurons and creates a non-linear threshold function
of the sum obtained, which is sent as input signal to
other connected neurons. The rule functioning in
ANN functions is based on an “all” or “nothing”
rule, which is described by a function that can take
two possible values, 0 or 1 (see McCulloch-Pitts
model (Osowski 2013)): the value 0 means that the
excitation was lower than the activity threshold of
the neuron, and the value 1 means that the excitation
was higher than the activity threshold of neuron.
One of the first most known and well described
examples of artificial neuron networks is the so
called “perceptron” (a model of the nervous cell),
which was proposed by F. Rosenblatt in 1958 (see
Rosenblatt 1958, 1988). This net design had many
advantages, but its effects were not fully satisfying.
The greatest benefit of the net was the fact that it
acted appropriately even though one of its elements
was damaged. On the other hand, the neural net
could not further complicate the task, and it is an
indication of considerable susceptibility to the
various changes, which were happened in process of
learning (Tadeusiewicz 1993). Marvin Minsky and
Seymour Papert (1969) criticized this model in their
book
(“Perceptrons:
an
introduction
to
computational geometry”), which led to dramatic
cuts into financing further research on artificial
neural networks (Newell 1969; Osowski 2013).
Thus, after that, the studies of artificial neuron
networks were continued only in few research
centers.
Until the eighties of the last century, research on
neural networks had been neglected, and only the
rapid development of Very-Large-Scale Integration
(VLSI) technologies succeeded to instigate a
renewed interest in methods of information
processing, including neural networks (Osowski
2013). The work of Hopefield (1982) on ANNs was
a milestone of research in this field, and was
continued in an increasing number of scientific
centers. Hopefield’s works led to a substantial
increase in scientific projects on ANNs, which not
only resulted in new types of networks, but also
added to the progress of practical implementations
of this method. At the same time, rapid progress in
informatics and computer systems resulted in the
creation of innovative solutions and greater
possibilities of exploring, learning and testing
ANNs. Research on artificial neural networks is
nowadays an increasingly popular domain of
knowledge, being used in various scientific fields
(Hashimoto 1997)
Application of artificial neural networks
So far, among all human organs the nervous system
has been the least understood, and maybe this in
spite of the fact that it was intensively studied. The
cerebral cortex, covering both cerebral hemispheres,
has significant cognitive and intellectual functions. It
consists of 1010 neurons and 1012 glial cells. It is
believed that the number of the connections between
cells is ~1015 (Tadeusiewicz 1993).
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NanoPhotoBioSciences, (2): 14-30.
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As mentioned earlier, artificial neural networks were
invented based on the model of the human brain.
Similarly to the brain, which consists of a huge
number of neurons, ANNs possess lots of elements
which aim to process and transmit information. In
the same way as in the nervous system, ANN’s
elements are called neurons. The neurons are
associated in structures, the so called networks, by
linkages called weights; during the learning process
the weight values can be freely changed or else
modified. The mode of linking of the neurons in the
net, as well as their distribution and incidence,
determines the network type and the mode of its
action (Fig.1).
Figure 1: Design of a simple Artificial Neural Network with i input variables and k neurons in
its output layer (modified after Aji et al. 2013)
Figure 1 shows the artificial neuron model called
perceptron (see earlier). The sets x1,…xi represent
input signals (for example leaf water content,
nutrients, soil quality and other agricultural factors),
the wki are synaptic weights, bk is a bias, vk is an
activation potential of the neuron k, φ(.) is an
activation function, yk is the output signal of the
neuron k and uk is the net input, which is the sum of
all inputs multiplied by all synaptic weights (see
uk 
The output is of the form:
below, Equation 1). Each individual constituent of
the network receives signals from the one placed in a
preceding layer. The connection between the inputs
is characterized by the weight coefficient wki and
bias bk (Svozil et al. 1997) The signals are multiplied
by the so called weighting factors, i.e. synaptic
weights (which are functions of time), and then they
are summed up:
i
w
j 1
kj
xj
(1)
yk    uk  bk 
The next step is the change stimulated by the transfer
function (which depends on the goal of the net’s
(2)
operation), and the output signal thus generated is
further transmitted to neurons in the subsequent
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Samborska et al., (2014) Artificial neural networks and their application in biological and agricultural research. SOAJ
NanoPhotoBioSciences, (2): 14-30.
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2347- 7342 http://signpostejournals.com
layer. In order to achieve a solution to any task by a
given network, input signals are needed, the output
signals represent the answers to the solicited
questions (i.e., input signals).
In practice, multilayer artificial neural networks are
often used, in majority consisting in at least three
layers, because single layer neural networks cannot
solve complex problems. The input and output
layers are always needed, and in between them there
are middle layers that are called hidden
(Tadeusiewicz 1993). Further, the neurons can be
linked together in many different ways, for example
using feedback loops (i.e., when a signal obtained
from the cells in the output layer is transmitted back
to the input layer) or else by establishing links within
the same layer (similar to brain operation) (see Fig.
2).
During the net design, the most important stages are
the assignation and the selection of an appropriate
spatial arrangement of the network under
construction, i.e. the number of layers and the
number of neurons in each of them. This is a very
important step, since too few layers or neurons can
cause erroneous results, whereas overstatement can
lead to biased fitting of the tested data (Lasoń et al.
2001).
The next essential step in ANN construction is the
process of network learning. There are few methods
for ANN training, and they depend on the type of the
net. The ANNs with one or many hidden layers form
the group of feed forward networks. The other type
of ANN is the self-organized map (SOM) of
Kohonen (Kohonen 1982). These will be presented
below.
Feed forward networks. Few algorithms exist for the
training of the type feed forward network. The aims
of these algorithms are to find the set of weights that
minimizes the
Figure 2: Wiring diagram of m input variables with neurons in the hidden layer and k output
layer neurons
error between the expected output y’ and the actual
output y. An example of such an algorithm that is
often used is called the back propagation (BP)
algorithm, or Levenberg-Marquardt algorithm. This
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Samborska et al., (2014) Artificial neural networks and their application in biological and agricultural research. SOAJ
NanoPhotoBioSciences, (2): 14-30.
Samborska et al., (2014) Signpost Open Access J. NanoPhotoBioSciences, 14-30. Volume 02, Article ID 010409, 17 pages. ISSN:
2347- 7342 http://signpostejournals.com
method for ANN training is a combination of the
steepest descent method (Rumelhart et al. 1986) and
the Gauss–Newton method (Osborne 1992).
On the other hand SOMs contains a set of artificial
neurons arranged on 2 or 3 dimensional grids. These
ANNs learn on their own through unsupervised
competitive learning. The network must try to
discover patterns in the input data and the input
vectors are used solely. SOM is based on
competitive learning so that for each input data
neurons compete with each other to be winner.
(Chaudhary et al. 2013).
Testing is crucial before selecting a specific net. This
means preparing the so called “sets of certain inputs”
together with the results that should be obtained.
Usually, at the beginning, the synaptic weights are
chosen randomly, but sometimes different
algorithmic methods of selection can be applied
(Lasoń et al. 2001). Such a potential network goes
through many examinations and tests, in order to
find out how many errors it makes. The testing phase
is finished when an anticipated level of correct
results is achieved. This decreases the risk of making
wrong decisions during the selection process of the
right net.
In the process of training, the adaptation of weights
can lead to an overtraining problem. In this case, the
network can reproduce the training data quite well,
but when new data is introduced the network
produces an erroneous output. There are some
strategies to solve this problem. According to one
strategy, the network should start with a simple
structure (i.e., with one or maximum two hidden
layers) and go stepwise to more complicated
structures. In many studies it has been shown that the
certain weight values can affect the network, leading
to overtraining. This problem is avoided by a method
called
“regularization”
(MacKay
2003).
Regularization is obtained by introducing additional
information, usually by means of application of
restrictions in order to solve an ill-posed problem or
to prevent overfitting.
The subsequent phase of network construction
consists in testing the data sets that were formerly
used in the process of network learning. If the results
at this stage are unsatisfactory, it is worth to start
over the process of learning.
There are some conditions that must be provided in
order to be a functional ANN. Then, an appropriate
model should be chosen to fulfill the needs. Thus, in
order to construct an appropriate ANN model the
following issues should be solved:

Assigning a task. The task should be presented
in a way that allows us to understand the results
obtained by using the ANN; this means that the
input stage of the net should help to determine a
solution to an output task;

Establishing values for all initial components of
the ANN;

Determining a suitable energy function, the
minimum of which will reflect an optimal
solution of the output task;

Setting up the weights of connections between
the structures, and the number of excitations;

Knowing the diversity of the dynamic
components of the ANN, so that will not allow
the decrease the value of the energy function.
The self-organized maps (SOM) are ANNs
developed by the Finnish scientist Teuvo Kohonen in
1987; they are used when the inputs are known, but
not the outputs. This type of neural network is
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Samborska et al., (2014) Artificial neural networks and their application in biological and agricultural research. SOAJ
NanoPhotoBioSciences, (2): 14-30.
Samborska et al., (2014) Signpost Open Access J. NanoPhotoBioSciences, 14-30. Volume 02, Article ID 010409, 17 pages. ISSN:
2347- 7342 http://signpostejournals.com
organized as a two or three-dimensional grid, and its
goal is to convert a high dimensional input signal
into a low dimensional discrete output signal (Tiwari
and Misra 2011) (see Fig. 3). Each unit j of the map
is connected to each unit i in the input layer. The sets
of input units are expressed as a column vector xi.
The values of connection weights wij(t) are initially
small. Once the weights are initialized, three
important processes start to form the SOM:
competition, cooperation and synaptic adaptation
(Kohonen 1982).
The competition is a process that leads to the
identification of the neuron-winner, which is
achieved through minimization of the difference
between the input data xi and the weights wij(t):
k j  arg min  xi  wij (t )
(3)
i
This neuron and its neighboring neurons form a
topological neighborhood (i.e., a cluster), having in
its center the neuron-winner. Then, the winning
neuron determines the spatial location of a
topological neighborhood of excited neurons. This is
a cooperation process. After the process of
cooperation the adaptive process is realized. The
neuron-winner and other neurons from the
topological neighborhood are adapted to reduce the
distance between the weight vector and the input
vector through the equation:
wij (t  1)  wij (t )   (t )(ki  wij )h j (t )
Where hj(t) is called neighborhood function, which
takes the value 1 when it relates to the neuron-
(4)
winner, and zero when it relates to remaining
neurons. The η(t) is called the
Figure 3: Schematic diagram of a Self-Organized Map
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NanoPhotoBioSciences, (2): 14-30.
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learning-rate parameter (Chon 2011), and its initial
value is labeled η0. After that, the values are reduced
until they approximate zero. The corrective process
(4) increases the productivity of the SOM. Since the
year 1990, the SOM has been used in many
biological domains (Ferrán and Ferrara 1992), such
as molecular biology, ecology, genetics and so on.
Artificial neural networks can perform various tasks,
the
most
popular
being:
approximation,
classification of formulas, prediction, compression,
interpolation and association. A typical network
model is presented in Fig. 2.
If an ANN is used with the aim to recognize patterns
or classification, the net learning involves recording
various pattern features, distribution of the main
components of the pattern, elements of Fourier’s
transformation, and others. It is important to find the
elements that distinguish the patterns, since the
decision to assign data to a specific class is made
based on them.
When we intend to use an ANN to foresee or
determine potential answers regarding a system
based on the values obtained in the past, information
on the variable x at the time before the prediction,
x(m-1), x(m-2),…, x(m-N), is needed. In such a situation,
the net makes a decision on the value to be estimated
when testing a sequence at the current moment m.
Neural networks in agricultural and biological
sciences
In systems involving plant environments related to
agriculture, in which sudden and quick changes of
environmental conditions usually take place, the
selection of an appropriate net is difficult. Such
environments present non-linearity of variables in
time, and are affected by many unknown factors.
Therefore, it is difficult to assess complex
relationships between the input and output signals
founded on analytical methods. There are many
different methods currently used in agricultural and
biological sciences (Kalra 1998; Benton and Jones
2001), but sometimes these are not efficient enough
in analyses based on scores of obtained results.
Thus, the use of ANNs has become increasingly
popular in these domains of science, as for example
in the study carried out by Kim and Gilley (2008) on
the relationships between soil erosion and
precipitations. The results of these simulations,
performed using models derived from ANNs,
indicated that the amount of soil erosion was
positively correlated with the amount of
precipitations and run-off. Additionally, it was found
that water erosion was the direct result of the
detachment of soil particles by raindrops. Further, in
this study it was concluded that ANNs could
generate models that reflect the non-linearity in the
nutrient medium of the plants derived from the
erosion of the soil, due to the excess water. The later
can also lead to uncontrolled nutrient leaking (Kim
and Gilley 2008). In the above mentioned research,
the authors used the Neural Works Professional
II/PLUS (NeuralWorks, Carnegie, Pennsylvania)
Version 5.22 software for the construction of a
multi-layer net; this package allowed the elaboration
of their own model by providing selected net
parameters and system control.
ANNs can be applied in studies on a great variety of
topics, as it will be shown below. For example,
studies on decreasing herbicide rates are important,
since they are related to thenegative effects of
herbicides on the environment (i.e., pollution). In
such a research, aiming the optimizing of the
herbicide rates (Moshou et al. 2001), the best results
were obtained by using multi-layer perceptron BP
trained, vector quantization and various methods
based on self-organizing maps (SOM).
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NanoPhotoBioSciences, (2): 14-30.
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Aji et al. (2013) applied an ANN in their study
concerning palm oil. There are many diseases which
can attack palms, which result in a substantial
decrease in oil production. Detection of any
pathogen at early development stages is difficult,
thus the authors used in their study a specific
technology designated to produce an early diagnosis
and classification of the disease; they also proposed
an appropriate treatment of this disease. The ANN
was trained in image processing, and was able to
diagnose three threatening palm diseases. A special
method, called ‘the complex linearity method’ was
designed to shorten the time needed for disease
recognition by using mobile devices in
investigations. It was based on visual analyses
performed by means of image processing in a
specially designed spatial system in an ANN. The
optimal number of ANN layers selected by the
authors was 6 (Aji et al. 2013). In this way, by using
the classification model in the learning process,
87.75% of diseases were identified in the palm
leaves under investigation.
Xiaoli Li and Yong He (2008) applied an ANN in
their study on tea leaves. The observations were
conducted in three different tea gardens, and
altogether 293 tea varieties were investigated. The
aim of the study was to discriminate between tea
leaves based on visible and near-infrared reflectance
(Vis/NIR) spectra. The authors were able to
discriminate the low quality tea leaves and to obtain
a good accuracy (77.3%) classification of all three
tea gardens by using ANN models that were
constructed to recognize tea leaf defects based on
specific records. The training of the ANN was
performed by using the Levenberg–Marquardt
algorithm with the backward propagation of errors,
which allows the processing of exemplary patterns,
as well as estimating the probability that the fitted
data of the studied object are introduced during the
process of the net training. According to the authors,
Vis/NIR signals showed a good potential to be used
in the evaluation of low quality tea leaves. Even
though the readings could be disturbed by the factors
such as wind or sunlight angle, the ANN processing
appeared to be a good method in the differentiation
of tea leaves.
Qiao et al. (2010) analyzed water uptake in the soil
environment, by taking into consideration that water
absorption by roots is reliant on the density and
humidity of the soil around growing roots. Water
uptake by plant roots is an important process in the
hydrological cycle. It is not only crucial for plant
growth, but also plays an indispensable role in
determining microorganism communities, as well as
by influencing the physical and biochemical
properties of the soil. Root capability to extract
water from soil depends on both soil and plant
properties. Determination of volume, conformation
and distribution of the roots in soil poses a lot of
difficulties to scientists, since non-invasive methods
for explicit description of the whole plant root
system have not yet been elaborated. Thus, the
authors proposed an alternative method (still being
tested) based on ANN analyses of data on plant
water uptake. The input data used by the ANN were:
soil moisture, electric conductance of the soil
solution, stem height and diameter, potential
evaporation and air humidity and temperature.
Output data concerned water uptake by plant roots at
different soil depths. The absorption rate was
estimated based on direct measurements of mass
balance, the evaluation of soil moisture following
Darcy's law (Brinkman 1949), and the assessment of
water content in soil derived from calculation of
capillary potential at 100 cm depth. The analysis
performed by (Qiao et al. 2010), with the help of an
ANN model, was non-invasive, time efficient and
led to the same results as those obtained by means of
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NanoPhotoBioSciences, (2): 14-30.
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2347- 7342 http://signpostejournals.com
other more invasive methods, thus offering a good
alternative to be used in such studies.
determined water content values of about R2 ≈ 0.98
(Goltsev et al. 2012).
Moreover, fluorescence kinetic curves have been
used for more than two decades by plant researchers
in ANN analyses (Zaimov 1992; Tyystjärvi et al.
1999; Moshou et al. 2001). According to the aim of
investigation, these curves were used either as input
or output data. The photosynthetic apparatus is one
of the most important structural and functional
components of the plant cell. The photosynthetic
reaction centers are highly conservative, and thus
possess low species-specific characteristics. On the
other hand, the characteristics of antenna complexes
are highly variable and specific for various plant
species. Indeed, Kirova et al. (2009) found that
structural and functional properties of the
photosynthetic
machinery
contain
enough
information to be used in the taxonomic
classification of plants.
Several other studies have confirmed that the
application of ANN models is extremely reliable in
the determination of the relative amount of water in
plant leaves (Zakaluk and Sri Ranjan 2006; Chen et
al. 2012; Goltsev et al. 2012). The same method can
be also applied to determine plant stress. Hence,
ANNs have a future in detecting plant stress and
disturbances in the functioning of assimilation
apparatus (Frick et al. 1998; Salazar et al. 2009).
Another important subject of study is the water
deficit (e.g., during drought), which is one of the
most important environmental factors limiting
sustainable yields, and needs a reliable tool for its
quantification. Measurement of the chlorophyll a
prompt fluorescence (PF), simultaneously with that
of delayed fluorescence (DF) and reflectance at 820
nm (MR), allowed the analysis of changes in the
performance of the photosynthetic machinery in
bean leaves submitted gradually to drought
conditions compared to a control (Goltsev et al.
2012). Taking into account the intensity of water
deficit, various changes in essential photosynthetic
processes were assessed. Further, data on PF, DF
and MR were used in an ANN model, which was
capable to recognize the relative water content in a
series of “unknown” samples, with a correlation
between the calculated and gravimetrically
In another type of study, Zaidi et al. (1999) used an
ANN with BP to evaluate the growth of lettuce
plants. The authors designed an ANN consisting of 5
to 8 processing units (input, output and hidden
layers). Clinorotation was used at a range of 0 to 25
rotations/min, with a rotation range between 0 and 5.
The average width and height of the plants after
transplanting were used for decision making on the
selection of plants for further investigations. Fiftyeight training data sets were tested until 22,124
interactive data were obtained. The results obtained
are based on many analyses made the authors and
they conclude that an ANN was an appropriate
method for evaluations of plant growth under
simulated conditions (Zaidi et al. 1999; Brinkman
1949).
ANNs have also been used for the identification of
plant viruses. The results obtained indicated that the
method using ANNs can be areliable tool, very
helpful in such analyses. Therefore, it was suggested
to use ANN models as an alternative for traditional
methods used in verification of a large amount of
data (Glezakos et al. 2010).
A trial on the evaluation of the effects of
environmental factors on banana leaves using an
ANN confirmed the usefulness of this method.
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NanoPhotoBioSciences, (2): 14-30.
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2347- 7342 http://signpostejournals.com
Similar as well as totally different studies have been
conducted with the use of ANNs in agricultural
research (Bala et al. 2005; Diamantopoulou 2005;
Movagharnejad and Nikzad 2007; Zhang et al.
2007). The majority of those involve forecasting
(Jiang et al. 2004; Uno et al. 2005; Savin et al.
2007). Jiang et al. (2004) described an ANN model
with backward propagation. Savin et al. (2007) used
information
obtained
from
the
SPOT
VEGETATION satellite and represents daily and
10-day averaged information about reflectance of
the earth’s surface to forecast the winter wheat
yield. The algorithm, which they were developed is
the connective utilization of ANNs - fuzzy sets,
fuzzy neural networks (FNNs) and granulated neural
networks (GNNs). Uno et al. (2005) elaborated
models for yield prediction in corn using statistical
and ANN methods based on various data on plant
vegetation obtained from airborne digital imaging
systems. They obtained greater prediction accuracy
with an ANN model by comparison with the three
conventional empirical models, which were used in
this study, too. Soares et al. (2013) attempted to
foresee the cultivation efficiency of fields distributed
throughout Russia. The above mentioned studies
indicated ANNs as the best means for this kind of
analyses.
The application of artificial neural networks in
agricultural and biological research has become
more and more accepted, especially in research
concerning the prediction of events (Hashimoto
1997; Moshou et al. 2001; Kim and Gilley 2008;
Qiao et al. 2010; Šťastný et al. 2011). ANNs have
been applied inter alia in sciences such as: medicine
(Malmgren 2000; Akdemir et al. 2009; Lweesy et al.
2011; Feng et al. 2012), technical (António et al.
2008; Ahmadi 2011; Niaki and Hoseinzade 2013;
Selvakumar et al. 2013), economics (Thinyane and
Millin 2011; Landajo et al. 2012; Azizi 2013;
Zanger 2013; Ashhab et al. 2014), chemistry
(Sroczyński and Grzejdziak 2002; Fogelman et al.
2006; Ozkan et al. 2011; Fathy and Megahed 2012;
Harris and Darsey 2013) and also in numerous other
scientific fields (Trichakis et al. 2011; Guarini 2013;
Citakoglu et al. 2014). ANN method is in constant
development, and gains increased worldwide
recognition. At the same time, the constant progress
of science, knowledge and technology makes now
very complex mathematical analyses possible in a
relatively short time. This tendency is an
unquestionable advantage, permitting the expansion
of ANNs based methods and their application in
almost all scientific domains. The relatively short
time needed to obtain results represents an important
advantage in the actual development of computer
sciences and technologies. The operating systems
used nowadays are often capable of processing
information thousands of times faster than those
acting in the eighties or nineties of the 20th century,
which is when the rapid development of information
technology occurred and computers entered
households all over the world. Only 20-30 years
before, all this would have been impossible since
such effective computers equipped with competent
programs did not exist. Today, computers possess
also incredibly larger memory systems, which are
capable of processing vast amounts of data. The
abovementioned
technological
progress
is
undoubtedly most useful in the development of
future research on ANNs. The latter, in turn, will
allow us to obtain answers to numerous questions
asked by global scientists working in miscellaneous
domains, and the humankind will be able get to the
bottom of more and more secrets of the unrivalled
Mother Nature.
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NanoPhotoBioSciences, (2): 14-30.
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2347- 7342 http://signpostejournals.com
5. The future of ANN application
Despite all the above mentioned opportunities, the
application of ANNs in the field of biological and
agricultural sciences is still very limited, but it is
highly expected that ANNs will become one of the
major research tools in these fields in the near
future. The reason behind this is the huge demand to
understand and predict the behavior of any system
based on different physiological processes. The fast
development of electronic devices and research
equipment will allow more and more researchers to
obtain a huge amount of data, even in a short time
ranges (i.e., less than one second). Only ANNs will
be able to deal with such huge amounts of data to
underline the trends and specific reactions and
behaviors of individuals. It can be applied to predict
abiotic and biotic stressors effects on living
organism for example, and will allow us to find
practical solutions for plant production and to avoid
huge financial losses, e.g. in the field of mineral
fertilization.
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