NanoPhotoBioSciences
Volume **** (2014), Article ID ******, 5 pages
doi:
Review Article
Artificial neural networks and their application in biological
and agricultural researches
Izabela A. Samborska1, Vladimir Alexandrov2, Leszek Sieczko3, Bożena
Kornatowska4, Vasilij Goltsev2, and Hazem M. Kalaji1,*
Department of Plant Physiology, Warsaw University of Life Sciences SGGW, 02-776
Warsaw, Poland
1
2
Department of Biophysics and Radiobiology, Faculty of Biology, St. Kliment Ohridski
University of Sofia, 8 Dr. Tzankov Blvd., 1164 Sofia, Bulgaria;
Department of Experimental Statistics and Bioinformatics, Warsaw University of Life
Sciences SGGW, 02-776 Warsaw, Poland
3
Institute of Environmental Protection-NRI, Department of Nature and Landscape
Conservation
4
Received ****** 2014; Accepted *****
Academic Editor: *****
Copyright © 2014 ****.
Keywords
artificial neural networks, synaptic weights, ANN in biology, ANN in agriculture, data
analysis, artificial nervous system
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 for accepted statistical methods, and provides mean results fitting well the
pattern of variable and hard to foretell phenomena in biological and agricultural systems.
* Corresponding authors: Dr. Hab. Hazem M. Kalaji, Phone: +48 664943484, Email:
hazem@kalaji.pl
1. Introduction
Artificial neural networks (ANN) are the systems of handling data, the benefits of which
have been more and more recognized in various fields of technology and science. Thanks to
their ability to tackle complex calculation issues they are progressively applied to solving
practical problems. The neural networks can be applicable in a magnitude of science and
knowledge domains. ANN’s main advantage is the fact that task solving is done by putting
forward input signals stimulating network capability to learn and recognize patterns. This
way complicated algorithms or rule-based programming are not always necessary to find
right answers. ANN’s performance and its mode of computing information reflect the activity
of the central nervous system that seems to be never-failing with its superlative processes
of transmitting information.
The aim of constructing ANNs was to create artificial intelligence inspired by work of
human brain, even though the latter has not yet been fully understood. On the other hand,
one has to bear in mind that each individual neuron of the nervous system is a very
important part of transmitting information. Thousands of small and independent neurons
can act together and this allows analyzing and simultaneous solving a wide variety of
complex tasks. No machine could be able to do it in such a reliable way. Computers are
most capable to perform advanced calculations and they do this much faster and more
efficiently than any human being, nevertheless, analyzing and learning on mistakes can be
made by man’s brain only. ANNs are based on the idea of adjoining computer’s and man’s
brain abilities. The main asset of neural networks is the ability of their neurons to take part
in an analysis when working simultaneously but independently from each other. In other
words, the neurons function as those in the brain and this provides for a possibility to
construct a system based on technology
such as computers equipped in a variety of
programs to solve complex tasks.
Ever since, human beings have observed the universe and contemplated natural
phenomena. Unknown processes have been tried to be understood, however even
contemporary science has not been able to endow with unequivocal explanation of the
processes happening in nature and living organisms. From the time of first research,
knowledge and technology have considerably progressed, however this is not enough to
come back with all the questions to answer by the humankind. Human beings constitute the
inseparable and inherent element of nature. Consequently, contemporary research
concentrates more and more on biological models, and the latter are worth improving and
developing towards further application. An example of such research is the work on
information systems and methods of analyzing and reprocessing information (Kosiński
2007). In the process of creation of such systems natural world is still a superlative
paradigm as for example man’s brain and nervous system are able to reprocess information
in a parallel way using thousands or millions of diminutive components for performing
subsequent operations, and this allows tackling very complex issues. The systems created
by nature are capable to achieve tasks through attaining, verifying and testing various
modes of accomplishing a designated goal. Besides, natural systems are capable to
eliminate damaged elements without disrupting operation of the whole system. Therefore,
the brain of man indicates incredible flexibility as well as ability to adapt to given conditions
–
even
extremely
difficult,
and
to
handle
or
eliminate
useless
components.
Its
supplementary assets are connecting and associating capabilities.
Artificial neural networks attempt to take off the work of human brain, and there still are
carried out investigations on improving and updating their complex structures. 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 is needed when ANNs are used for analyses. Often
ANNs are mathematical models, which need employment of an appropriate software. There
has been research carried out on various algorithms applied in the analyses performed by
ANNs. This approach allows the processes of network learning and makes man’s work
easier, especially when dealing with complex tasks where traditional statistical methods
cannot be applied.
Data concerning plant tissues are habitually classified as continuous data (e.g. size,
weight), and they are frequently analyzed with statistical methods such as ANOVA.
However, only normally distributed or scattered data can be analyzed this way. In the case
of as much complex data as those biological, there should be applied different methods of
analysis. What’s more, in biological research, forecasting should be performed using a
suitable method, and this is possible when ANNs are applied.
The study carried out by Gago (Gago et al. 2010) showed that ANN is an useful tool in
modeling intricate and non-linear relationships contingent on data not visible from the first
sight, and which are most possible to be rejected by the researcher. The authors showed
that ANN gave good results in the field of biology, and especially in the case of plants.
However, if the analysis is to be successful, the data has to be optimized towards taking
into consideration many various factors, such e.g. those environmental or genetic. Thanks
to ANN, variables can be independently introduced into the network and factor permutations
can be foreseen. So as to run the analysis of this kind there has to be input of as much as
possible data on different factors. ANN is capable to reject unnecessary ones and select
those most important for achieving sound results (Gago et al. 2010). Learning performed by
the network runs automatically and it is based on the selection of appropriate values of
weights. In the case of ANN, there are distinguished 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 exists a possibility to verify the answers given by the network. In this
case, for each input vector there has to be known the value of an output vector, in other
words – an exact solution to a given task. The second learning paradigm is applied when
task solution is not known. Somebody constructs artificial neural network when he study
complex processes depend on many variables. The ANNs are used in almost all fields of
science such as biology, ecology, physics, chemistry, agronomy, economy, medicine,
mathematics and computers science. The aims of ANNs are to predicted complex process
when there are some inputs (for example in agronomy these are soil quality, nutrients, and
cropping year) end single output (crop yield) (Wieland and Mirschel 2008). Among various
mathematical models, just ANN showed to be the most useful tool for the analysis of
chlorophyll fluorescence signals. The method has proved to be very precise being able to
obtain expected and true results at 95% level. Therefore, it seems that it will be further
developed and improved in biological research (Tyystjärvi et al. 1999).
2. First ANN models
Each individual neuron in the nervous system is independent and essentially works
alone. At the same time, 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 a prior neuron and creates a nonlinear threshold function of the sum obtained, which is followed by sending a signal to other
connected neurons. The rule functioning in ANN is based on zero-one system, i.e.: “all” or
“nothing”. In preliminary neural models the output signal was determined as a binary
number 0 or 1. The value 0 meant neural activation lower than neuron activity threshold,
and the value 1 was attributed to neuron agitation higher than the threshold of neuron
activity.
One of the first, most known and well described examples of artificial neuron networks is
the Perceptron constructed and described by Rosenblatt in 1958 (Rosenblatt 1988). The
author explicated dynamic neuron systems based on the perceptron model of the nervous
cell. In keeping with Rosenblatts’s theory, the function of neuron activation has two binary
values, i.e. 0 or 1, and the neuron was described by the McCulloch-Pitts model (Osowski
2013.). The net design had many advantages, however 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. In any case the perceptron was the first model of effectively
functioning neural network. On the other hand, the net could not realize more complex
tasks, and it indicated considerable susceptibility to various changes which were inextricable
elements in the process of learning (Tadeusiewicz 1993)
Marvin Minsky and Seymour
Papert (1969) criticized the above model in their book (“Perceptrons: an introduction to
computational geometry”), which resulted in dramatic cuts of financing further research of
this kind (Osowski 2013.; Newell 1969). Thus, the latter were continued on much smaller
scale, only in a few research centers.
Until the eighties of the last century, scientific discussions on neural networks were not
carried out, and only rapid development of Very-Large-Scale Integration (VLSI) technologies
instigated new-fangled interest in the methods of information processing, including neural
networks (Osowski 2013.). The work by Hopefield (1982) on ANNs was the milestone in
development of research in this field, ever since conducted in an increasing number of
designated scientific centers. Hopefield’s works contributed significantly to substantial
enhancement of granting scientific projects on ANNs, which not only resulted in establishing
novel network types, but also added to the progress on practical implementation of this
method. At the same time, quick progress of information and computer systems resulted in
creation of innovative solutions and greater possibilities of exploring, learning and testing
ANNs.
Research on artificial neural networks has been yet conducted, and nowadays this has
become a more and more popular domain of knowledge being willingly used in various
scientific fields.
3. Application of artificial neural networks
Neural network model was constructed based on surveillance of the nervous system and
readiness to imitate its operations. So far, among all human organs the nervous system has
been the least understood, and maybe this is the reason why it has been so eagerly studied.
The cerebral cortex covering both cerebral hemispheres plays significant cognitive and
intellectual functions. It consists of and 1010 nerve cells and 1012 glial cells. It is believed
that the number of the connections between cells is app. 10 15 (Tadeusiewicz 1993).
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, ANN possesses lots of
elements with the aim to process and transmit information to the next element. In the same
way as in the nervous system, ANN’s elements are called neurons. The neurons are
associated in the structures, the so called networks, by linkages called weights. Beneficial is
the fact that during the learning process weight values can be freely changed or else
modified. The mode of linking neurons in the net as well as their distribution and incidence
determine 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)
In Fig 1 a simple model of artificial neuron called Perceptron is shown. The sets x1,…xi
represent input signals (for example leaf water content, nutrients, soil quality and other
agricultural factors), the wki are synaptic weights, vk is a linear combiner output, φ(.) is an
activation function, yk is an output, bk is a bias, uk is net input and this is the sum of all
inputs multiple by all synaptic weights. Each individual constituent of the network takes
signals from the other 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, and next they are
summed up.
uk 
i
w
j 1
kj
xj
(1)
Then the output is:
yk    uk  bk 
(2)
The next step is the change stimulated by the transfer function (depending on the goal
of net’s operation), and the output signal generated is further transmitted to neurons in
subsequent layer. In order to achieve a solution to any task there are needed input and
output signals of a given network, and the latter constitute the answers to the questions
asked (input signals).
In the practice often are used multilayer artificial neural networks, because single layer
neural networks cannot solve complex problems. Nowadays, the large majority of
constructed and applied neural networks possess at least three layers. There always has to
exist input layer and output one, and in between there are middle layers, the so called:
Figure 2: Wiring diagram of m input variables with
neurons in the hidden layer and k output layer neurons
hidden (Tadeusiewicz 1993). The neurons can be linked together in many different ways, for
example using feedback loops (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
(analogously to brain operation) (Fig. 2).
At the stage of net projecting, the most important are assignation and selection of an
appropriate spatial arrangement of the network under construction, i.e. the number of its
layers and the number of neurons in each of them. This is a very important step, since too
few layers or neurons can cause obtaining mistaken results, whereas overstatement
forwards can lead to biased fitting tested data (Lasoń et al. 2001).
The next essential step in ANN constructing is the process of network learning. There
exist a few methods for the training process of ANNs and they depend on the type of the
neural networks. The artificial neural networks with one and many hidden layers are formed
the group of feed forward networks. The other type of ANN is the self-organized map of
Kohonen (Kohonen 1982).
Few algorithms exist for the training of the feed forward networks. The aims of
algorithms are to find the set of weights that minimizes the error between expected output
y’ and the actual output y. The algorithm, which is often used, is called backpropagation
algorithm or also called the Levenberg Marquardt algorithm. This method for training of
ANNs is a combination of the steepest descent method (Rumelhart et al. 1986) and the
Gauss–Newton method (Osborne 1992). It is important to choose such synaptic weights,
which will generate expected results as input signals. Testing is a crucial undertaking before
selecting a specific net. This means preparing the so called sets of certain inputs together
with the results which should be obtained. At the beginning, the synaptic weights are
chosen randomly, however sometimes there can be applied algorithmic methods of selection
(Lasoń et al. 2001). Such potential network is subject to many examinations and tests so as
to check how many errors she makes. Testing is finished when an anticipated level of
correct results is achieved. This decreases the risk of making wrong decision during the
selection process of the right net.
In the process of the training, the adaptation of weights can lead to so called
overtraining problem. In this case, the network can reproduce the training data quite well
but when new data is introduced to the network the error is large. There exist some
strategies to solve this problem. The one strategy is the network should start with a
‘‘simple’’ structure with one or two hidden layers and go over stepwise to more complicated
structures. In many works have been shown that values of the weight affect the network
and overtraining arises. This problem is avoided by regularization (MacKay 2003).
The subsequent phase of network construction is testing data sets, which were formerly
used in the process of network learning. Concluding on effectiveness of the net tested
depends on passing this stage. If the results at this stage are unsatisfactory, it is worth to
start the process of learning over again.
Certain conditions have to be ensured to have ANN functioning as well as there has to be
known its structure and an appropriate model has to be selected to meet our needs. Thus,
in order to construct an appropriate model there have to be solved the following issues:

finding the type of task representation in the manner allowing to understand the
result obtained with the use of ANN; this means that input stage of the net will help
to determine a solution to an output task;

knowing and establishing values for all initial components of ANN;

determining suitable energy function, the minimum of which will reflect an optimal
solution to an output task;

setting up the weights of connections between the structures and knowing the
number of exterior agitations;

knowing diversity of ANN components’ dynamics so as to not allow decreasing
energy function value.
The self-organized maps (SOM) are used when inputs are known, but output is
unknown. This ANN was developed from Finnish scientist Teuvo Kohonen in 1987. This
network is organizes as a two or three-dimensional grid and its goal is to convert high
dimensional input signal into a low dimensional discrete output signal (Tiwari and Misra
2011). The every units of the map j are 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. We must remember that weight is a function of time. Once the weights
are initialized, three important processes are started for forming of SOM – competition,
cooperation and synaptic adaptation (Kohonen 1982). The competition is a process, which
leads to determination of neuron-winner. We shall determine the neuron-winner through
minimization of the difference between input data xi and the weight wij(t).
The neuron
which respond maximally to input vector and which has a minimum distance to the weight
vector is a neuron-winner ki.
k j  arg min  xi  wij (t )
i
(3)
This neuron and its neighboring neurons form topological neighborhood. The center of
this cluster is the neuron-winner. The neuron-winner and other neurons from topological
neighborhood are adapted to reduce a distance between weight vector and input vector:
wij (t  1)  wij (t )   (t )(ki  wij )h j (t )
(4)
In the equation (4) hj(t) is so called neighborhood function and it is equal to one when it
relate to neuron-winner, and it is zero when it relate to remaining neurons. The η(t) is
learning-rate parameter (Chon 2011). Initially the η(t) has a value η0. After that, the values
are reduced and they approximate to zero. The corrective process (4) increases the
Figure 3: Schematic diagram of Self-Organized
Map
productivity of the SOM. Since 1990 the SOM has been used for many biological researches
(Ferrán and Ferrara 1992) such as molecular biology, ecology, genetics and so on, because
these networks are very powerful and flexible.
Artificial neural networks are employed for solving theoretical and practical problems,
which need solutions based on time consuming and complex calculations. The speed of
information processing and obtaining the results with the use of neural networks provides
for good possibilities of solving even very work and time consuming tasks.
Artificial neural networks can perform various functions, of which most popular are:
approximation, classification
of formulas, prediction, compression, interpolation
and
association. A model of a typical network is presented in Fig. 2. Most of the time, ANN acts
in keeping with non-linear function y= f (x), where y is realized vector function of different
variables and x – a universal approximate.
If ANN is used with the aim to recognize patterns or classification, then net learning is
based on recording various pattern features, distribution of main components of the pattern,
elements of Fourier’s transformation, and etc. It is important to find the elements
distinguishing the patterns, since based on them the decision will be made on assigning
data to a specific class.
In the case when we intend ANN to foresee or determine potential answers of the
system, when based on the values obtained in the past, information on variable x is
indispensable at the time before the prediction x(m-1), x(m-2),…, x(m-N). In the situation as
such, the net constructed makes a decision on the value to be estimated when testing a
sequence at the current moment m.
4. Neural networks in agricultural and biological sciences
In the agricultural systems, such e.g. plant environments, which are characteristic of
sudden and quick changes of conditions, the selection of an appropriate net is not easy.
Such environments indicate non-linearity of variables in time and are affected by many
unknown factors. Therefore, it is difficult to assess complex relationships between input and
output in the system founded on analytical methods. Recently, the intelligent control system
based on artificial intelligence (AI) has become one of the most advanced technologies in
system, learning (Hashimoto.Y 1997).
There are many methods currently used in agricultural and biological sciences. However,
sometimes it appears that they are not sufficient enough for analyses and estimates based
on scores of the results obtained. Thus, using ANNs has become more and more popular in
the abovementioned domains of science. Thanks to ANN application it is possible to assess
whether the factors investigated are correlated, as it was examined in the study on
relationships of soil erosion and precipitation carried out by Kim and Gilley (2008). The
results of simulations performed by these authors with the use of models derived from ANNs
indicated that the amount of soil erosion was positively correlated with the amount of
precipitation and run-off. Additionally, it was found that water erosion was a result of
detaching soil particles by raindrops. In general, transport of soil particles by flowing water
can cause a considerable loss of water quality. At the same time, it was concluded that ANN
could generate the models which reflected non-linearity in plants’ nutrient environment
resultant of soil erosion set off attributable to water excess. The latter can also lead to
uncontrolled nutrient leaching (Kim and Gilley 2008). Neural Works Professional II/PLUS
(NeuralWorks,
Carnegie,
Pennsylvania)
Version
5.22
software
was
used
in
the
abovementioned research for the construction of a multi-layer net. The package allows
elaboration of own model through providing selected net parameters and system control.
The multi-layer perceptron (MLP) has been acknowledged as the architecture of artificial
neural networks which can be trained by the algorithm of backward propagation of errors
(BP) (Kim and Gilley 2008). ANNs can be applied in studies on decreasing herbicide rates,
and thereby on negative effects on environment. Research on optimizing herbicide rates
gave more defined results with the use of multi-layer perceptron BP trained, vector
quantization and various methods based on self-organizing maps (SOM) (Moshou et al.
2001b). Aji et al. (2013) applied ANN in their study concerning palm oil. There are many
diseases which can attack palms, which results in a substantial decrease in oil production.
Detection of any pathogen at early development stages is hardly possible, thus the authors
conducted their study by means of specific technology designated for disease early
diagnosis and classification as well as adjustment of right treatment. It was proposed to
train ANN in image processing, and as a result 3 threatening palm diseases could be
diagnosed. The complex linearity method was designed so as to cut down the duration of
time needed for disease recognition. The method allowed using mobile devices in
investigations. It was based on visual analyses performed by means of image processing in
specially designed spatial system in ANN. This way 87.75% of diseases were identified in
palm leaves following the classification model in the learning process. The optimal number
of ANN’s layers selected by the authors was 6 (Aji et al. 2013).
Xiaoli Li and Yong He (2008) applied ANN in their study on tea leaves. The observations
were conducted in 3 different tea gardens, and altogether 293 tea varieties were
investigated. The aim of the study was discrimination of tea leaves based on visible and
near-infrared reflectance (Vis /NIR) spectra. Good accuracy of classification was obtained
and discrimination of low quality tea leaves with the use of ANN was 77.3% for all three tea
gardens observed. The authors constructed appropriate models recognizing tea leaves’
defects based on specific records. ANN’s training was performed by means of BP, which
allowed the net processing exemplary patterns as well as estimating probability that the
object studied fitted data introduced during the process of net training. According to the
authors, Vis/NIR signals showed a good potential for discrimination of tea leaves with low
quality. Even though the readings could be disturbed by the factors such wind or sunlight
angle, ANN processing appeared to be a good method for differentiation of tea leaves.
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 in shaping soil physical and biochemical properties.
Root capability to extract water from soil depends on both soil and plant properties. Qiao et
al. (2010) analyzed water uptake in soil environment bearing in mind that water absorption
by roots is reliant on density and humidity of soil around growing roots. Determination of
volume, conformation and distribution of roots in soil poses a lot of difficulties for scientists
since non-invasive methods for explicit description of the whole plant root system have not
been yet elaborated. Thus, the authors proposed an alternative method (still tested) based
on ANN analyses of data on plant water uptake. Data used for analyses in the input layer of
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, evaluation of soil moisture following
Darcy's law and assessment of water content in soil derived from calculation of capillary
potential at 100 cm depth. The analysis performed with the use ANN was non-invasive, time
efficient and indicated the same results as those obtained by means of other methods.
Therefore, successful model realization offered alternate and practical means for estimating
water absorption by plant roots in the soil solution (Qiao et al. 2010).
For two decades, some plant researchers have used fluorescence kinetic curves for ANNs
(Zaimov 1992; Tyystjärvi et al. 1999; Moshou et al. 2001b). These curves are used for
input or output data in ANNs according to aim of investigation. The antenna complexes
characteristics are highly variable and specific for varies groups of plant species. Kirova et
al. (1992) have found that structural and functional characteristics of the photosynthetic
machinery contains enough information about the taxonomic classification of the studied
plants.
Photosynthetic apparatus is the main structural and functional element of the plant cell.
Its reaction centers are highly conservative with low species-specific characteristics.
Water deficit is one of the most important environmental factors limiting sustainable
yields and needs a reliable tool for its quantification. Research in this field encompasses
registration of the photo-induced signal of prompt fluorescence (PF) and that of delayed
fluorescence (DF) as well as modulated fluorescence and reflectance at 820 nm to analyze
the changes in photosynthetic machinery performance in bean leaves first as fresh and then
drought gradually. Taking into account the intensity of water deficit, there can be assessed
various changes in essential plant processes, such as e.g. photosynthesis. Using data on PF,
DF and MR there was constructed ANN which was capable to recognize a relative water in a
series of “unknown” samples with a correlation between calculated and gravimetrically
determined water content values of about R2 ≈ 0.98 (Goltsev et al. 2012).
Several researchers confirm that application of ANN is unfailing in the determination of a
relative amount of water in collected plant leaves. The method can also be applied to
determine plant stress. ANN has a future in detecting plant stress and disturbances in the
functioning of assimilation apparatus (Frick et al. 1998; Salazar et al. 2009).
Zaidi et al. (1999) used ANN with BP so as to evaluate lettuce in terms of its growth.
The authors designed ANN consisting of 5 to 8 processing units (input, output and hidden
layers).
There was used clinorotation at a range from 0 to 25 rotations/min at rotation
range between 0 and 5. Average width and height of plants after transplanting were used
for decision making on the selection of plants for further investigations. Fifty eight training
data sets were tested until 22 124 of interactive data were obtained. The results obtained
based on as many analyses made the authors conclude that ANN was an appropriate
method for evaluations of plant growth under simulated conditions (Zaidi et al. 1999;
Prasad and Dutta Gupta 2008).
In other, ANNs were used for identification of plant viruses. The results obtained
indicated that the method applied provided for a right and reliable tool, helpful in easing the
analyses. Therefore, it was suggested to use ANN as an alternative for traditional methods
used in verification of lots of data (Glezakos et al. 2010).
A trial on evaluation of the effects of environmental factors on banana leaves using ANN
confirmed usefulness of this method. There have been conducted similar as well as totally
different studies with the use of ANN in agricultural research (Bala et al. 2005;
Diamantopoulou 2005; Movagharnejad and Nikzad 2007; Zhang et al. 2007). The majority
of the latter concerned forecasting (Jiang et al. 2004; Uno et al. 2005; Savin et al. 2007).
Jiang et al. (2004) described ANN model with backward propagation. The algorithms used
during net training concerned forecasting winter wheat yield using spatial information. Uno
et al. (2005) elaborated models for yield prediction in corn using statistical and ANN
methods based on various data on plant vegetation. Greater accuracy was obtained with
ANN model, which was better than one of the three typical experimental models. Savin et
al. (2007) studied connective utilization of ANNs, fuzzy sets, fuzzy neural networks (FNNs)
and granulated neural networks (GNNs). Soares et al. (2013) attempted to foresee
cultivation efficiency of fields distributed throughout Russia. The abovementioned studies
indicated ANN as the best means for analyses of this kind.
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.Y 1997; Moshou et al. 2001a; 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;
Lweesy et al. 2011; Akdemir et al. 2009; 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;
Harris and Darsey 2013; Fathy and Megahed 2012) and also in numerous other scientific
fields (Trichakis et al. 2011; Guarini 2013; Citakoglu et al. 2014). This method has been
constantly developing and gaining more and more worldwide recognition. In unison,
constant progress of science, knowledge and technology makes even very complex
mathematical analyses possible in a relatively short time. The latter is an unquestionable
advantage of ANNs and causes that they can be applied in almost all scientific domains. A
relatively short time for obtaining the results is an advantage of actual development of
computer sciences and technologies. The systems operating nowadays are often capable to
process information thousands of times faster than those acting in the eighties or nineties of
the 20th century, that is when rapid development of informatics occurred and computers
entered households all over the world. Only 20-30 years before, all this would be impossible
since that much effective computers equipped in that competent programs did not exist
then. At the present time, computers possess incredibly better memory systems, which are
characteristic of fast processing, and thus they are capable to process vast amounts of data
in a short time. The abovementioned futures make joining together nature and rapidly
developing technology most useful in the progress of research on ANNs. The latter in turn
allows to reach the answers to numerous questions asked by global scientists of
miscellaneous domains. Thanks to this progress humankind can get to the bottom of more
and more secrets of the world around and unrivalled Mother Nature.
5. The future of ANN application
Despite that, yet the application of ANN in the field of biological and agricultural
sciences is limited, it is highly expected that it will be one of the major research tools in
those fields in the near future. The reason behind that is the big demand to understand and
predict the behaviour of any system based on different physiological processes. The fast
development of electronic devices and research equipment will allow more and more to
have a huge number of data in a very short time, even in one second. Only ANN will be able
to deal with such huge number of data to underline the trends and specific reactions and
behaviours of individuals. It can be applied to predict abiotic and biotic stressors effects on
living organism, this will allow to find practical solutions for plant production and avoid huge
loss of money e.g. in the field of mineral fertilization.
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