ARTIFICIAL NEURAL NETWORKS APPLIED IN FOREST BIOMETRICS AND MODELING: STATE OF THE ART (JANUARY/2007 TO JULY/2018)

FLÁVIO CHIARELLO MARIA TERESINHA ARNS STEINER EDILSON BATISTA DE OLIVEIRA JÚLIO EDUARDO ARCE JÚLIO CÉSAR FERREIRA About the authors

ABSTRACT

Artificial Intelligence has been an important support tool in different spheres of activity, enabling knowledge aggregation, process optimization and the application of methodologies capable of solving complex real problems. Despite focusing on a wide range of successful metrics, the Artificial Neural Network (ANN) approach, a technique similar to the central nervous system, has gained notoriety and relevance with regard to the classification of standards, intrinsic parameter estimates, remote sense, data mining and other possibilities. This article aims to conduct a systematic review, involving some bibliometric aspects, to detect the application of ANNs in the field of Forest Engineering, particularly in the prognosis of the essential parameters for forest inventory, analyzing the construction of the scopes, implementation of networks (type - classification), the software used and complementary techniques. Of the 1,140 articles collected from three research databases (Science Direct, Scopus and Web of Science), 43 articles underwent these analyses. The results show that the number of works within this scope has increased continuously, with 32% of the analyzed articles predicting the final total marketable volume, 78% making use of Multilayer Perceptron Networks (MLP, Multilayer Perceptron) and 63% from Brazilian researchers.

Keywords:
Artificial Intelligence; Systematic Review; Bibliometric Review; Multilayer Perceptron; Forest Engineering Problems

INTRODUCTION

The human brain has attributes that would be desirable in any artificial system. Its skills in dealing not only with probabilistic and/or inconsistent information in different situations, but also its flexibility to adapt to poorly defined situations, has attracted the attention of many scholars, who intensified their research in the field of artificial intelligence (AI) in the 1980s with the use of intensive computing.

The dissemination of the methodologies found in this field has achieved interesting results in different areas of knowledge. In forestry, the Artificial Neural Network (ANNs) technique is considered a promising and very efficient alternative for defining the best management of forest resources (Menzies et al., 2007MENZIES, J.; JENSEN, R.; BRONDIZIO, E.; MORAN, E.; MAUSEL, P. Accuracy of neural network and regression leaf area estimators for the Amazon Basin. GIScience and Remote Sensing, v.44, n.1, p.82-92, 2007.; Bhering et al., 2015BHERING, L.L.; CRUZ, C.D.; PEIXOTO, L.A.; ROSADO, A.M.; LAVIOLA, B.G.; NASCIMENTO, M. Application of neural networks to predict volume in eucalyptus. Crop Breeding and Applied Biotechnology, v.15, n.3, p.125-131, 2015.; Miguel et al., 2015MIGUEL, E.P.; REZENDE, A.V.; LEAL, F.A.; MATRICARDI, E. A. T.; VALE, A.T.; PEREIRA, R.S. Artificial neural networks for modeling wood volume and aboveground biomass of tall Cerrado using satellite data. Pesquisa Agropecuária Brasileira , v.50, n.9, p.829-839, 2015.; Ribeiro et al., 2016RIBEIRO, R.B. DA S.; GAMA, J.R.V.; DE SOUZA, A.L.; LEITE, H.G.; SOARES, C.P.B.; DA SILVA, G.F. Methods to estimate the volume of stems and branches in the Tapajós national forest. Revista Árvore , v.40, n.1, p.81-88, 2016. and Çatal & Saplioğlu, 2018ÇATAL, Y.; SAPLIOĞLU, K.Comparison of adaptive neuro-fuzzy inference system, artificial neural networks and non-linear regression for bark volume estimation in brutian pine (Pinus brutia ten.). Applied Ecology and Environmental Research, v.16, n.2, p.2015-2027, 2018.]).

Studies guarantee that in different scenarios ANNs have contributed to high performance compared with classical regression models. Their purely massive structure, distributed (in layers) and ability to learn and generalize situations, tolerance of flaws and noises, and their flexibility in modeling categorical (qualitative) and numerical variables provide the methodology with a favorable context regarding the capacity to solve problems of any size (Binoti et al., (2013BINOTI, D.H.B.; BINOTI, M.L.M.S.; LEITE, H.G.; DA SILVA, A.A.L.; ALBUQUERQUE, A.C.An approach to diameter distribution modeling using Cellular Automata and Artificial Neural. Cerne, v.19, n.4, p.677-685, 2013.).

The wide range of spectra allows the evaluation of different network configurations based on alterations to the input data, number of neurons in the hidden layer, the output feedback (as input signals for the next iteration) or weight adjustments. Consequently, ANNs allow the finding of feasible solutions in the search space, even with small, but well balanced, population samples.

The need to develop studies and analyses for the parameterization and adaptation to different scenarios, such as final volume prediction, basal area, dominant height or growth and production of forest plantations (Binoti, 2014BINOTI, M.L.M.S.; BINOTI, D.H.B.; LEITE, H.G.; DA SILVA, A.A.L.; PONTES, C. Use of artificial neural network for diameter distribution modelling for even-aged population. Revista Árvore , v.38, n.4, p.747-754, 2014.), has attributed a high potential to ANNs, particularly with regard to the variables and phenomena modeling, which are considered complex. Studies in this line of research, such as those conducted by Diamantopoulou et al. (2009DIAMANTOPOULOU, M.J.; MILIOS, E.; DOGANOS, D.; BISTINAS, I. Artificial neural network modeling for reforestation design through the dominant trees bole-volume estimation. Natural Resource Modeling, v.22, n.4, p.511-543, 2009.); Özçelik et al. (2010ÖZÇELIK, R.; DIAMANTOPOULOU, M.J.; BROOKS, J.R.; WIANT JR., H.V. Estimating tree bole volume using artificial neural network models for four species in Turkey. Journal of Environmental Management, v.91, n.3, p.742-753, 2010.); Soares et al. (2011SOARES, F.A.A.M.N.; FLÔRES, E.L.; CABACINHA, C.D.; CARRIJO, G.A.; VEIGA, A.C.P. Recursive diameter prediction and volume calculation of eucalyptus trees using Multilayer Perceptron Networks. Computers and Electronics in Agriculture, v.78, n.1, p.19-27, 2011.) and Binoti et al. (2017BINOTI, M.L.M.S.; BINOTI, D.H.B.; LEITE, H.G. Application of artificial neural networks to estimate the height of even-aged stands of eucalyptus. Revista Árvore, v.37, n.4, p.639-645, 2013.), have been considered relevant to the applications that occur in forestry science.

The significant increase in scientific content, not only in the field of forestry, but also in several others, has shown that the development of syntheses that facilitate access to this information (forecasting of intrinsic variables) is urgently needed. In other words, its idealization has enabled the formation of conclusions based on a combination of results from numerous sources. Thus, we can define a systematic review as a process that compiles and clusters a series of works (or studies) related to one or more topics based on the focused, well-defined investigative approach, which defines criteria on the identification, selection, assessment and synthesis of relevant information for conducting research.

Researchers have recently shown a strong tendency to conduct systematic and/or bibliometric literature reviews. Studies of this kind, although secondary, not only allow the expansion of knowledge but also address different relevant prisms and perspectives of themes of the same nature.

Considering these premises, unlike analogous works (with the same semantic), this article prioritizes the construction of a holistic view behind articles related to the application of ANNs in the field of forest sciences based on a detailed systematic review plan involving some bibliometric aspects. In other words, it seeks to clarify very important points on which to base the knowledge and directives that can support or not support the notion that the methodology can be viewed as promising. For this purpose, a survey of scientific articles was conducted by analyzing the construction of their respective scopes, verifying the form of implementation of ANNs (type (classification), architecture, configurations, computational language), data stratification, sample size, complementary techniques and findings.

The study is structured into five sections, including introduction. Section 2 presents the concept of ANNs, their applications and some relevant aspects regarding systematic and bibliometric literature reviews. The methodological procedure used in the study is outlined in Section 3. The results are presented and discussed in Section 4. The conclusions of the study are given in Section 5.

THEORETICAL FOUNDATION

The theoretical foundation is based on two approaches. First, the study briefly discusses the concept of Bibliometric and Systematic Literature Reviews. This is followed by the contextualization and a detailed description of what constitutes an ANN. The functioning of an artificial neuron is explained, and the main implementation algorithm for network learning/training, backpropagation, is presented.

Systematic Literature Review

Galvão and Pereira (2015GALVÃO, T.F.; PEREIRA, M.G. Systematic reviews of the literature: steps for preparation. Epidemiologia e Serviços de Saúde, v.1, n.23, p.183-184, 2015.) claimed that systematic reviews should be made available so that other researchers can repeat the applied methodological procedure and that their search criteria need to be comprehensive and free of bias.

Normally, the development of this type of review establishes a relatively solid pattern in the search for works and ends up being modified according to the researcher’s needs or the field under study. Although a sequence of steps should be followed, there is, in fact, no single rule for the definition of the search criteria or the way in which the search will be directed.

As pointed out by Medeiros et al. (2015MEDEIROS I.L.; VIEIRA, A.; BRAVIANO, G.; GONÇALVES, B.S. Revisão Sistemática e Bibliometria facilitadas por um Canvas para visualização de informação. Revista Brasileira de Design da Informação, São Paulo, v. 12, n. 1, p. 93 - 100, 2015.), the Bibliometric Literature Review can be understood as a purely investigative action that focuses on issues that are similar to the one being addressed. For this reason, it is considered one of the initial steps of the scientific method (applied to avoid any possibility of duplication in studies).

As a contribution, Gil (2002Gil, A.C. Como elaborar projetos de pesquisa. São Paulo: Atlas, 2002.) emphasized that this classic approach to analyzing (or assessing) a review is intended to allow researchers much greater coverage/measurement of the indexes oriented to the production and dissemination of relevant subjects than pragmatic, i.e., direct research.

Artificial Neural Networks

The first study of neurocomputation dates back to 1943, when the physiologist McCulloch and the mathematician Pitts developed a pioneer work, which consisted of assimilating the behavior of biological neurons as a binary circuit for the creation of a corresponding mathematical model. Despite its ability to separate two Boolean inputs, this model was not able to perform the learning process, since it did not have free parameters (Aguiar, 2010AGUIAR, F. G. Utilização de Redes Neurais Artificiais para detecção de padrões de vazamento em dutos. Dissertação (Mestrado em Engenharia Mecânica) - Universidade de São Carlos, São Carlos, SP, 2010.).

Years later, more precisely in 1949, the neuropsychologist Donald Hebb made his great contribution to the McCulloch and Pitts’ model. In brief, Hebb’s projects had as a guideline the creation of a specific learning law for the synapse of neurons, demonstrating that the competence of ANNs is the result of altered synaptic efficiency (ALMEIDA, 2001ALMEIDA, A. D. Comparação entre métodos para roteamento de redes de dados usando Redes Neurais Artificiais. Dissertação (Mestrado em Ciência da Computação) - Universidade Federal de Santa Catarina. Florianópolis, SC, 2001.).

Despite the considerable reductions in related research, ANNs reached their peak in 1980. With the advent of computers with greater calculation processing capacity, Rumelhart, Hinton and Williams formalized the development of the back-propagation algorithm. (Rumelhart et al., 1986). This new approach provided the training of Multilayer Perceptron (MLP) networks with great generalization power, opening up new areas of implementation, which until then were only idealized (Freiman, 2004FREIMAN, J. P. Utilização de Redes Neurais Artificiais na previsão de indicadores financeiros para avaliação econômica de negócios em situação de risco. Dissertação (Mestrado em Engenharia de Produção) - Universidade Federal de Itajubá. Itajubá, 2004.).

We can observe an ANN as a complex system of massively distributed data processing that aims to simulate the functions of the neurons in the human brain based on mathematical values or equations (Diamantopoulou et al., 2009DIAMANTOPOULOU, M.J.; MILIOS, E.; DOGANOS, D.; BISTINAS, I. Artificial neural network modeling for reforestation design through the dominant trees bole-volume estimation. Natural Resource Modeling, v.22, n.4, p.511-543, 2009.). This traditional computational tool is capable of obtaining excellent results in different areas and applications, as described in the work of Oliveira et al. (2015OLIVEIRA, V.P.L. Redes Neurais Artificiais e K-médias em um modelo híbrido alternativo para a classificação de clientes em aprovação de crédito. Universidade Federal de Goiás.Goiânia, 2015.) and Steiner et al. (2006STEINER, M.T.A.; SOMA, N.Y.; SHIMIZU, T.; NIEVOLA, J.C.; STEINER NETO, P.J. Using Neural Networks Rule Extraction for Credit-risk Evaluation. International Journal of Computer Science and Network Security, v.6, n.5ª, p.6-17, 2006. ), regarding bank lending through clients’ classification (defaulters or paying customers). Other works in this respect are those of Valente et al. (2014VALENTE, G.F.S.; GUIMARÃES, D.C.; GASPARDI, A.L.A.; OLIVEIRA, L.A. Aplicação de redes neurais artificiais como teste de detecção de fraude de leite por adição de soro de queijo. Instituto de Laticínios Cândido Tostes, v.69, n.6, p.425-432, 2014.), concerning solving problems involving adulteration in the milk manufacturing process, and Gonçalves et al. (2016) on the classification of forest strata, based on remote sense data. These are just some of the practical examples that could be mentioned.

According to Haykin (2001Haykin, S. Redes neurais: princípios e prática. Porto Alegre: Bookman, 2001.), the ANN structure assumes a classical behavior of systems based on interconnected neurons, responsible for the basic processing of information/knowledge acquired by the network. This process occurs mainly due to the learning procedures and the connecting forces between the neurons, called synaptic weights, as shown in Figure 1.

FIGURE 1
Description of the k-th neuron.

In Figure 1,x 1 , x 2 ,..., x p corresponds to the process inputs, usually associated with the previous neuron output. The synaptic weights, w k1 , w k2 ,..., w kp represent, in synthesis, the network memory, from the experience acquired through the presentations of the patterns. The sum symbolizes the linear combination process. In other words, it is responsible for producing an activation potential based on the sum of the input signals, weighted with their respective synaptic weights. The activation function φ is observed as an output saturator from the neurons (u k ), restricting their amplitude to a finite value y k , usually normalized in the closed interval of [0,1]. Finally, the parameter θ, or limit value (threshold), has the characteristic to help the ANN to adjust to the knowledge provided.

In general, ANN models are defined by the network topology, the node features, training type, and learning rules which, in addition to specifying an initial set of weights, indicate how these weights (or parameters) must be adapted, so that the performance of the networks is the best possible. According to Diamantopoulou et al. (2009DIAMANTOPOULOU, M.J.; MILIOS, E.; DOGANOS, D.; BISTINAS, I. Artificial neural network modeling for reforestation design through the dominant trees bole-volume estimation. Natural Resource Modeling, v.22, n.4, p.511-543, 2009.), the design procedures and the training algorithm definitions are widely discussed and, therefore, they add value to many researches’ development.

Although there are numerous learning algorithms, the most well-known and commonly used algorithm is the back-propagation algorithm, a method with a supervised paradigm that operates in two consecutive stages, feeding the network (forward propagation) and back-propagating the error (backward propagation) (Rumelhart et al. 1986RUMELHART, D.E.; HINTON, G.E.; WILLIAMS, R.J. Learning internal representations by backpropagating errors. Nature, v.323, p 533-536, 1986.; Fausett 1994FAUSETT, L. Fundamentals of Neural Networks, Prentice-Hall, Englewood Cliffs, NJ, 1994. and Silva 2009SILVA A. M. Utilização de Redes Neurais Artificiais para Classificação de SPAM. Dissertação (Mestrado em Modelagem Matemática e Computacional) - Centro Federal de Educação Tecnológica de Minas Gerais, Belo Horizonte, BH, 2009.).

As a contribution, Silva (2009SILVA A. M. Utilização de Redes Neurais Artificiais para Classificação de SPAM. Dissertação (Mestrado em Modelagem Matemática e Computacional) - Centro Federal de Educação Tecnológica de Minas Gerais, Belo Horizonte, BH, 2009.) guarantees that this algorithm pragmatic character is directed to make the application of a set of inputs able to produce the desired or, at least, consistent, outputs. In theory, this is due to the gradual convergence of the network weights, so that the application of the input vectors can produce the necessary outputs.

MATERIAL AND METHODS

In order to formalize all the important conditions for a successful review, the present study adopted, a priori, the interpretation of the subjects that are considered relevant and that could serve as a basis for the development of the following research question: what advances were made in the field of forestry regarding the use of the ANN methodology for the prediction, prognosis or estimation of variables for its management?

In order to answer this question, a pilot study was conducted with different terms, followed by a logical AND or OR operator to find out which search format would be the most convenient in the following search bases: Science Direct, Scopus and Web of Science. The preliminary analysis showed that most of the authors reject the manipulation of derivations or very specific terminologies in the field of forestry, particularly with regard to the titles, abstracts or keywords, since the research space and, consequently, the index of the articles found, were relatively low.

The search filters were adjusted to refine the search, especially regarding the date of submission, document type and language (to check the progress in the field in national and international scenarios).

Thus, the prerogatives obtained by the several experiments were described by identifying the best configuration in the search terms, which delimits the selection of articles to the maximum, as shown in Figure 2.

FIGURE 2
Methodology applied to Systematic and Bibliometric Reviews.

Figure 2 shows that the preliminary search surveyed 1,140 articles in the three databases, of which 465 were from Scopus, 557 from the Web of Science and 118 from Science Direct up to the end of July 2018. However, the subsequent step was directed towards preparing an activity that could offer a solution to separating the articles based on their relevance. To do this, we conducted a survey of these works by applying a selection criterion processed in steps 1 and 2 (defined below) and in the exclusion of duplicate articles.

Theoretically, although different steps are considered, their natures encompass the same objective, i.e., they are responsible for weighing - by title (Step 1) or abstract (Step 2, analyzed when only the title does not allow us to decide whether the subject is of interest) - the works of interest (or not) to the research in question.

Despite these assumptions, it is important to emphasize that, on various occasions, the reading of the abstracts (step 2) was unnecessary, since the paper title did not present any kind of analogy or relation to the study’s subject, such as forensic context, population growth, landslides in urban areas, electrocardiograms, X-ray examinations, limb fractures, innovations in the clean energy sector, climate changes, density patterns, possibility of radiation, bleeding problems, and hurricane forecasting.

Theoretically, with the execution of the first step, 636 articles guided the survey and, with the exclusion of duplicate articles (using Excel 2010) the final set contained 438 articles for a more detailed analysis (step 2) to identify those that could actually contribute to the study. With these definitions, 43 works were filtered up to July 2018, serving as a basis for conducting the surveys of the main themes in each article: definition of their scope, the software used, classification of ANNs, activation functions, validation methods, authors by country, complementary techniques (applied cases), filiations, main journals and findings. Some of these alignments can be viewed in Figure 3.

FIGURE 3
Criteria for filtering the 43 related articles.

RESULTS AND DISCUSSION

Initially, the analysis focused on the production of published articles, with emphasis on the application of ANNs in the forest context. As can be seen in Figure 4, there was a slight rise in the first years (up to 2012LOTTERING, R.; MUTANGA, O. Estimating the road edge effect on adjacent Eucalyptus grandis forests in KwaZulu-Natal South Africa using texture measures and an artificial neural network. Journal Spatial Science, v.57, p.153-173, 2012.), with some stationary periods (2009, 2011BALCI, B.; KESKINKAN, O.; AVCI, M. Use of BDST and an ANN model for prediction of dye adsorption efficiency of Eucalyptus camaldulensis barks in fixed-bed system. Expert Systems with Applications, v.38, n.1, p.949-956, 2011. and 2012, with only two publications), and more significant production in the last 6 years, with emphasis on the eight publications in 2015 and five new works up to July of 2018, when this research was conducted. This growth is associated with interest in the field, the availability of databases, new software and updated packages, with different functional techniques, often distributed free of charge.

FIGURE 4
The 43 forestry publications analyzed and their publication years.

Considering the interpretation of the objectives proposed by each author, some authors chose to deepen their studies with more than one aim, often due to the possibility of finding an alternative tool to the traditional techniques or models that are frequently used in the forest scenario. Of these, we can mention Castro et al. (2013CASTRO, R.V.O.; SOARES, C.P.B.; MARTINS, F.B.; LEITE, E.H.G. Growth and yield of commercial plantations of eucalyptus estimated by two categories of models. Pesquisa Agropecuária Brasileira, v.48, n.3, p.287-295, 2013.), whose objectives addressed the issue of Mortality, DBH (Diameter at Breast Height) and Total Height. Others include Ashraf et al. (2015ASHRAF, M.I.; MENG, F.R.; BOURQUE, C.P.A.; MACLEAN, D.A.; BOND-LAMBERTY, B. A novel modelling approach for predicting forest growth and yield under climate change. PLoS ONE, v.10, n.7, 2015.) and Binoti et al. (2015BINOTI, M.L.M.S.; LEITE, H.G.; BINOTI, D.H.B.; GLERIANI, J.M. Stand-level prognosis of eucalyptus clones using artificial neural networks. Cerne , v.21, n.1, p.97-105, 2015.), who worked with the Volume Increase and Basal Area, and Miguel et al. (2015MIGUEL, E.P.; REZENDE, A.V.; LEAL, F.A.; MATRICARDI, E. A. T.; VALE, A.T.; PEREIRA, R.S. Artificial neural networks for modeling wood volume and aboveground biomass of tall Cerrado using satellite data. Pesquisa Agropecuária Brasileira , v.50, n.9, p.829-839, 2015.), who worked with Volume and Final Height. Nunes and Gorgens (2016NUNES, M. H.; GORGENS, E. B. Artificial intelligence procedures for tree taper estimation within a complex vegetation mosaic in Brazil. PLoS ONE , v.11, n.5, p.1-16, 2016.) conducted a study on Volume and Diameter, and Vieira et al. (2018VIEIRA, G.C.; DE MENDONÇA, A.R.; DA SILVA, G.F.; ZANETTI, S.; DA SILVA, M.M.; DOS SANTOS, A.R. Prognoses of diameter and height of trees of eucalyptus using artificial intelligence. Science of the Total Environment, v.619-620, p.1473-1481, 2018.) dealt with Growth and Future Height.

In the same circumstances, we can highlight that the most addressed scope of the whole sample was the prediction (prognosis) of the total final volume of marketable wood (32% of the works). Figure 5 presents the integration of all the information into a relationship format (Author-Objectives).

FIGURE 5
Relationships (Author-Objectives) of the 43 articles selected.

Despite the orientation towards ANNs, 31 articles included the application of a complementary technique, be it of Linear Regression (37%), Cellular Automata (7%) or Kalman filter (13%) (procedure incorporated to networks), as shown in Figure 6. Other approaches, such as Volumetric Equations, Random Forest, the Support Vector Machine (SVM), Least Squares, Individual-tree Models and the Image Segmentation technique, were also applied, but only once. Therefore, they are aggregated as “other techniques”, accounting for 43% of the total.

FIGURE 6
Complementary techniques addressed by the 31 (out of 43) articles that used more than one technique.

Another premise of this study involved identifying the most frequently applied types of ANN to solve the problems. With five different classifications at the end of the research, the authors’ preference (Figure 7) was restricted to the classical Multilayer Perceptron (MLP) model with 78% of the total, followed by the Radial Basis Functions (RBF) with 13%, the Cascade Correlation Artificial Neural Network (CCANN) - a model considered promising for achieving satisfactory results in the total volume prognosis, according to Diamantopoulou & Milios (2010DIAMANTOPOULOU, M.J.; MILIOS, E. Modelling total volume of dominant pine trees in reforestations via multivariate analysis and artificial neural network models. Biosystems Engineering, v.105, p.306-315, 2010.) - with 7%, and fuzzy logic networks and the Single Layer Perceptron, with 1% each.

FIGURE 7
CANN models most used in the 43 studies.

In addition to the panorama that was identified, another item of information was extracted regarding the use of ANNs, namely the type of activation function employed in their respective input and output layers. Although there are different types (or mathematical components), most authors (15 in total) chose to use the Hyperbolic Tangent, followed by the Sigmoid or Sigmoidal function (11), Logistics (10), Linear and Exponential (5), Sigmoidal Tangent (4), Sine and Identity (3), Softmax and Log (2), others (1). There were 13 unspecified (uninformed) works, as shown in Figure 8.

FIGURE 8
Main activation functions used in the 43 articles.

Regarding the application of the validation (or verification) methods of the ability of ANNs to predict the expected results successfully, Figure 9 shows that the correlation coefficient was the main criterion adopted by the authors (21 times), followed by Root Mean Square Error (RMSE) with 20, %RMSE (9), Bias (9), Mean Absolute Error (7), Histograms and Graphical Residue Analysis (6), Mean Error and Mean Square Error (5), Standard Error (4), Relative Error, Maximum Absolute Error and Coefficient of Determination (3), Statistical Test (L&O), Efficiency Models, Error Matrix, Akaike Information Criterion, Kappa Coefficient and the %Absolute Deviation totaled (2) and the “Other” class had only a single application.

FIGURE 9
Methods used for assessing the ANNs in the 43 articles.

In addressing the researchers’ nationality (Figure 10), Brazil was the main motivator in the development of articles in this line of research, responsible for 63% of the total. Brazil was followed by Turkey with 12%, Greece with 10% and, with 3%, South Africa, the USA, England, Ireland and Japan. The Federal University of Viçosa, Brazil, was the institution with the largest number of publications, a total of eight.

FIGURE 10
Geographical distribution of the 43 papers, involving several authors.

The analysis of the computational procedures for ANN implementation and execution showed that the most used interface was Statistica software, in 12 works, followed by Matlab (8), NeuroForest (7), R Software (2), WEKA - Waikato Environment for Knowledge Analysis - (1), ENVI - Environment for Visualizing Images - (1), JavaNNS - Java Neural Network Simulator - (1) and uninformed (11). However, it is worth noting that the different versions or derivations of the same package/program were not considered different operating systems, such as Statistica (7, 10 and 12) or Matlab (r2010a, r2013a and r2016b). These data are shown in figure 11.

FIGURE 11
Software used to implement the language used in the 43 analyzed works s.

In relation to the main journals, Table 1 shows that “Revista Árvore” and “Cerne” are considered the main journals, containing 6 and 5 published articles, respectively. On the other hand, the class designated as “Other” comprises a long list of journals with only one article, including Applied Ecology and Environmental Research, Applied Soft Computing Journal, Computers and Electronics in Agriculture, Comunicata Scientiae, Crop Breeding and Applied Biotechnology, Ecological Engineering, Ecological Modelling, European Journal of Remote Sensing, Expert Systems with Applications, Floresta, Forest Products Journal, Forest Science, GIScience & Remote Sensing, Journal of Environmental Management, Journal of Spatial Sience, Nativa, Natural Resource Modeling, Neural Computing and Applications, Revista Brasileira de Ciências Agrárias, Science of the Total Environment, Sensors and Southern Forests.

TABLE 1
Descriptive statistics of the data groups.

To complement the information presented in Table 1, Table 2 shows, in perspective, the institutions that participated in the survey. In short, the Federal University of Viçosa, located in the state of Minas Gerais, is the one that contributed the most, comprising 8 publications, followed by Süleyman Demirel University and the Federal University of Jequitinhonha and Mucuri Valleys, with 5 and 4 publications, respectively. The “Other” class consists of Arid Agriculture University, Cukurova University, Forestry and Forest Products Research Institute, Indiana University, Institute of Applied Physics, Federal University of Jequitinhonha and Mucuri, Federal University of Lavras, Federal University of Mato Grosso, Federal University of Espírito Santo, Federal University of Western Pará, Federal University of Paraná, University College Dublin, University of Bristol, University of Cambridge, University of KwaZulu-Natal and University of Lisbon.

TABLE 2
Main affiliations found in the sample of 43 articles.

Analyzing the conclusions of the studies, it can be said that all the information aided the development and execution of experiments to assess the capacity of ANNs in different contexts (scenarios and objectives). In general, all 43 studies showed that ANNs achieved satisfactory results (due to relatively low errors) and, therefore, can be considered a very efficient and accurate alternative in the prognosis of intrinsic variables in the forest inventory.

Detailed information on the Brazilian institutions, main researchers at these institutions, types of study and number of publications is shown in Table 3.

TABLE 3
Brazilian affiliations and other information found in the sample of 43 articles.

Assuming that there are many other data and/or elementary information regarding the articles analyzed in this study, Table 4 provides a clear and organized summary of this content.

TABLE 4
Detailed description of relevant data/information regarding the survey of the 43 articles selected by the sample.

CONCLUSION

Due to the great rise of Artificial Intelligence in the various fields of research and development, this study portrayed the construction and development of a systematic review, involving bibliometric aspects, orientated to identify the application of ANNs in different areas of the forest context, especially in forest biometry and modeling, more specifically, in the prediction of parameters considered important, from January 2007 to July 2018.

For this purpose, a methodological procedure was developed to scan articles in three research databases (Science Direct, Scopus and Web of Science), defining the main search terms (from a pilot study), filter selection and the exclusion of studies considered to be of no relevance and/or duplicated. Thus, at the end of the last step, 43 articles were selected, enabling the stratification of aggregating information for the study.

The variations in scope showed that advances were made in several respects, but the predominant one is estimating total marketable volume, making use of ANNs (especially the MLP network topology) and comparing them frequently with the traditional regression models. The results show that the number of works within this scope (volume) has increased continuously, with 32% of the analyzed articles predicting final total marketable volume (Figure 5), 78% making use of Multilayer Perceptron Networks (MLP; Figure 7), 28% using Statistica software (Figure 11) and 63% from Brazilian researchers (Tables 2 and 3).

It should be highlighted that a justification for the MLP model being more widespread and used in the literature is that the architecture of this network is considered a “universal approximator”. In other words, it is a general purpose model that can be applied on a large scale (ranging from linear to non-linear problems with high complexity). Other justifications are that it is easy to use (not requiring much knowledge of the functions that will be modeled) and has been proved efficient in many studies covering a wide range of areas of expertise. On the other hand, the majority of the authors opted to make comparisons of MLP with the traditional forecasting techniques most used by forest science researchers, as is the case of regression. The idea was generally to verify which of the approaches presents a better accuracy for the output variables (mainly volume).

The Brazilian researchers who stood out during the period in question were those from the Federal University of Viçosa (8 publications). The authors (cited here individually and in chronological order) were: Silva (2009SILVA A. M. Utilização de Redes Neurais Artificiais para Classificação de SPAM. Dissertação (Mestrado em Modelagem Matemática e Computacional) - Centro Federal de Educação Tecnológica de Minas Gerais, Belo Horizonte, BH, 2009.); Binoti (2013BINOTI, D.H.B.; BINOTI, M.L.M.S.; LEITE, H.G.; DA SILVA, A.A.L.; ALBUQUERQUE, A.C.An approach to diameter distribution modeling using Cellular Automata and Artificial Neural. Cerne, v.19, n.4, p.677-685, 2013.a); Binoti (2013b); Bhering (2015BHERING, L.L.; CRUZ, C.D.; PEIXOTO, L.A.; ROSADO, A.M.; LAVIOLA, B.G.; NASCIMENTO, M. Application of neural networks to predict volume in eucalyptus. Crop Breeding and Applied Biotechnology, v.15, n.3, p.125-131, 2015.); Cosenza (2015COSENZA D.N.; LEITE, H.G.; MARCATTI, G.E.; BINOTI, D.H.B.; DE ALCÂNTARA, A.E.M.; RODE, R. Site classification with support vector machine and artificial neural network. Scientia Forestalis, v.43, n.108, p.955-963, 2015.); Leite (2016LEITE, H.G.; BINOTI, D.H.B.; OLIVEIRA NETO, R.R.; LOPES, P.F.; DE CASTRO, R.R.; PAULINO, E.J.; BINOTI, M.L.M.S.; COLODETTE, J.L. Artificial neural networks for basic wood density estimation. Scientia Forestalis , v.44, n.109,:p.49-154, 2016.); Reis (2016REIS, L.P.; SOUZA, A.L.; MAZZEI, L.; REIS, P.C.M.; LEITE, H.G.; SOARES, C.P.B.; TORRES, C.M.M.E.; DA SILVA, L.F. Prognosis on the diameter of individual trees on the eastern region of the amazon using artificial neural networks. Forest Ecology and Management , v.382, p.161-167, 2016.) and Binoti (2017). The themes addressed in their studies were eucalyptus clone plantation sites (7 publications) and managed forests in the Amazon (1 publication), as shown in Table 3.

As a consequence of the information stratification, the synthesis of conclusions indicated that ANNs are capable of safely predicting the different parameters of the fields of biometrics and forest modeling (considering the low indexes found by the validation metrics) and, therefore, it can be considered a very promising alternative technique. Suggestions for future studies include applying ANN to other forest problems and using other types of ANN to maximize the accuracies involved in each problem.

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HIGHLIGHTS

  • Artificial Intelligence and Artificial Neural Networks in Forest Engineering.
  • Systematic and Bibliometric Review: Science Direct, Scopus and Web of Science.
  • 32% of the analyzed papers predict the final total marketable volume.
  • 63% of the analyzed articles were from Brazilian researchers.
  • 78% making use of Multilayer Perceptron Networks (MLP).
  • 28% using Statistica software.

Publication Dates

  • Publication in this collection
    09 Sept 2019
  • Date of issue
    Apr-Jun 2019

History

  • Received
    23 Jan 2019
  • Accepted
    29 May 2019
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