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Samborska et al., (2014) Signpost Open Access J. NanoPhotoBioSciences, 14-30. Volume 02, Article ID 010409, 17 pages. ISSN: 2347- 7342 http://signpostejournals.com REVIEW ARTICLE OPEN ACCESS Signpost Open Access Journal of NanoPhotoBioSciences Journal Website: http://signpostejournals.com 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 14 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 15 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 16 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). 17 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 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 18 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 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 19 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 20 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 21 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 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). 22 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 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 23 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 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. 24 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 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. 25 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 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. 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