Publication |
Intended Purpose(s) |
Number of networks that authors have implemented |
Identification of the Network Inputs and Outputs |

**Binoti et al. (2013**BINOTI, 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.b) |
" This study’s essence aimed at increasing the accuracy of tree height estimates and, at the same time, reducing the need for field height measurement, in order to decrease the forest inventory costs (from the construction and validation of a ANN model). " |
4 MLP networks for scenario A (introduction of a new genetic material with no information regarding the hypsometric relation) and 15 MLP networks for scenario B (knowing the growth tendency in height of the stands implanted, based on the measurements of the CFI). |
"Inputs: Quantitative variables: mean dominant height of the plot, diameter (with bark) at 1,30 m height (DBH) and age. Qualitative variables: only the soil. Output: Eucalyptus Final Height. " |

**Castro et al. (2013**CASTRO, 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.) |
This article aimed at assessing and comparing two categories of growth and production models in commercial Eucalyptus plantations using simultaneous equations and ANN. |
"Training of 500 networks for the estimate of mortality probability; 500 networks for total height prediction at future age; 500 networks for the DBH prognosis in the future age." |
"Inputs: Each of the 15 networks suffered variations in the variables number and type, as follows: current and future age (I1 and I2), site index (S), DBH class (CLA), independent competition index of distance (IID1, IID2, IID3, IID4, IID5), the current age DBH (DBH1) and the total height at the current age (h1). Output: Each ANN had a possibility associated with its output: mortality probability, DBH at future age or total height at future age." |

**Özçelik et al. (2013**ÖZÇELIK, R.; DIAMANTOPOULOU, M.J.; CRESCENTE-CAMPO, F.; ELER, U. Estimating Crimean juniper tree height using nonlinear regression and artificial neural network models. Forest Ecology and Management, v.306, n.15, p.52-60, 2013.) |
This study’s purpose was to conduct a comparative work (among several methods) to obtain height predictions in sample plots of Crimerian juniper, located in the southern region of Turkey. For this, this work used several nonlinear growth functions, adjusted using nonlinear mixed-effect modeling techniques, as well as generalized models of the h-d type (height diameter measures) and ANN. |
2 MLP networks. |
"Inputs: Network I was fed with data of diameter at breast height (DBH) and Network II, with the same DBH, but taking into account the variation found for each plot analyzed. Output: Final height." |

**Soares et al. (2013**SOARES, F.A.A.M.N.; FLÔRES, E.L.; CABACINHA, C.D.; CARRIJO, G.A.; VEIGA, A.C.P. Recursive diameter prediction for calculating merchantable volume of eucalyptus clones using Multilayer Perceptron. Neural Computing and Applications, v.22, n.7-8, p.1407-1418, 2013.) |
The work developed an approach for the use of MLP networks, for the recursive prediction of tree diameters, with the use of only three real measurements taken at the base of the tree, without prior knowledge of the total height. Then, the predicted diameters were used with the Smalian method, in order to calculate the volume of trees in a planted location. |
Uninformed. |
"Inputs: Measurements of diameters at three different heights at the base of each tree (0.30m, 0.70m and 1.30m). Note: The next step considered the first case output as a new input, generating (0.7m, 1.3m and 2m). These steps are repeated every 1m along the stem until reaching the total height of the tree. Thus, we can describe the inputs, in a generalized way: d = diameter (di-2, di-1 and di). Output: Future diameter following the time series di +1." |

**Binoti et al. (2014**BINOTI, 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.a) |
Considering the high cost for tree cubing in forest companies, this study’s objective was to propose a methodology with the ANN use, in order to reduce the number of trees to be scaled during the generation process of volumetric equations. |
4 MLP networks. |
"Inputs: DBH, height and diameters in positions 0,0; 0.5; 1.0; 1.5; 2.0; and 4.0m from the soil, besides the volumes obtained from 2 to 4m and the categorical variable defined as clone, which presents 15 subdivisions (C1 to C15). Output: Volume." |

**Binoti et al. (2014**BINOTI, 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.b) |
The work proposed the ANN development and assessment in the projection of Weibull distribution parameters and in the comparison of this method with some diametric distribution models commonly used in the forest area. |
100 MLP networks developed. However, the study selected the top 16 networks. |
"Inputs: Parameter β of the Weibull function, initial age, future age, parameter γ of the Weibull function, mean diameter, minimum height, dominant height, mean height, maximum height, minimum DBH, mean DBH, maximum DBH and number of trees planted. Output: The first four networks worked with the parameter β of the Weibull function in the future age; Networks 5 to 8 predicted the parameter γ of the Weibull function in the future age; Networks 9 to 12 completed the minimum DBH prognosis at future age and the last 4 networks estimated maximum DBH at future age." |

**Silva et al. (2014**SILVA, P.R.; ACERBI JÚNIOR, F.W.; DE CARVALHO, L.M.T.; SCOLFORO, J.R.S. Use of artificial neural networks and geographic objects for classifying remote sensing imagery. Cerne , v.20, n.2, p.267-276, 2014.) |
This work aimed to model a methodology for the creation of a use and land coverage map in the northern region of the State of Minas Gerais. For this, it outlined three specific objectives: to test the use of image segmentation techniques for an object-based classification, contemplating spectral, spatial and temporal information; to test the use of high spatial resolution images (Rapideye) combined with time series Landsat-TM, aiming at capturing the seasonality effects; and the classification of the data through ANN. |
Development of 10 MLP networks. |
"Inputs: 4 NDVI values obtained through Landsat TM images, average reflectance of objects in each of the 5 bands, total brightness, contribution rate of a given band to the general brightness (bands 4 and 5), maximum difference between the mean intensities of each band, average difference between the pixel values of the objects (bands 4 and 5) and standard deviation (bands 4 and 5). Outputs: Each neuron represents a type of native class, as follows: Agricultural land (1), water (2), cerrado (3), deciduous forest (4), eucalyptus (5), others (6), pastures (7) and track (8)." |

**Ashraf et al. (2015**ASHRAF, 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.) |
This study turned to the construction of a simple growth and yield model that has the capacity to predict the growth of individual trees under different climate change scenarios. These data come from the integration of historical records, ecological processes, JABOWA-3 (responsible for designing tree growth in different climate change scenarios) and ANN. |
The study trained 12 MLP networks. |
"Inputs: basal area, sum of total stands of softwood (SW) basal area in the PSP, total stands of basal area of finewoods or hardwoods (HW) in the PSP, basal area sum of SW-type large trees (based on the diameter) in a PSP, basal area sum of large HW trees (based on the diameter) in a PSP, dominant height, soil moisture, soil nutrients, species class identification, stock factor, days of increasing degree, solar radiation and climatic scenarios. Outputs: Volume increase and basal area." |

**Bhering et al. (2015**BHERING, 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.) |
The main objective aimed at assessing the ANN methodology for the prediction of wood volume in eucalypt breeding programs and in the selection of families, comparing their results with those that the regression model found. |
8 MLP networks segmented into 4 networks, for conditions at 3 years of age, and 4 networks for 6 years |
"Inputs: Situation A: total diameter and height; Situation B: commercial diameter and height; Situation C: types of species, total diameter and height (both for total volume) and commercial diameter and height (used for commercial volume calculation). Output: Volume." |

**Binoti et al (2015**BINOTI, 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.) |
The study’s focus was to model not only the volumetric production of Eucalyptus clone stands in function of categorical and numerical variables, but also to evaluate the accuracy of ANN prognosis. |
"The work trained 600 networks to prognose the basal area: 200 perceptrons, 200 MLPs and 200 RBFs. Analogously, the same applies to the volume forecast, that is, 600 networks divided into the three types of networks presented (200 each). However, the work considered the best 24 networks for the discussion of the results (12 as basal area and 12 as volume)." |
"Inputs: Categorical (for basal area): design, soil type, relief, texture, clone and spacing. Numerical (for basal area): current age, future age and current basal area. Categorical (for volume): design, soil type, relief, texture, clone and spacing. Numerical (for volume): current age, future age, current basal area, initial volume and future basal area. Outputs: An output for the first set of networks: basal area. An output for the second set of networks: future volume. This happens because the future basal area is treated as input for the volume forecast." |

**Cosenza et al. (2015**COSENZA 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.) |
The objective was to compare the results obtained by the forest data processing, with the Support Vector Machine and ANN, aiming to classify the productive capacity of eucalyptus stands. |
Preliminarily, the study considered the training of 400 networks with different configurations. After this process, it selected the 5 best networks, being 4 MLP and 1 RBF |
"Inputs: The soil and preparation type before planting; the spacing used; the stand age; dominant height; basal area; volume with bark; diameter at 1,3m of height (DBH), minimum, average and maximum of the stand, and the number of individuals per hectare. Within these categorical variables, ten types of soils, two types of soil preparation and five types of spacing were described. Output: The value emitted by each of the three neurons of the output layer will represent the probability of a given field belonging to its respective class." |

**Diamantopoulou et al. (2015**DIAMANTOPOULOU, M.J.; ÖZÇELIK, R.; CRECENTE-CAMPO, F.; ELER, T. Estimation of Weibull function parameters for modelling tree diameter distribution using least squares and artificial neural networks methods. Biosystems Engineering , v.133, p.33-45, 2015.) |
The article investigated the potential for improvement in the diameter distribution modeling, at a time when ANN models (with Levenberg-Marquardt learning) were used as an internal procedure to accurately estimate the parameters required in the Weibull distribution modeling of two parameters, using the Method of Moments (MOM) and the Maximum Likelihood Estimation (MLE). |
3 LMANN networks (Neural Networks with Levenberg-Marquardt learning). |
"Inputs: Tree age, mean square diameter and basal area. Output: Diameter distribution." |

**Hickey et al. (2015**HICKEY, C.; KELLY, S.; CARROLL, P.; O’CONNOR, J. Prediction of Forestry Planned End Products Using Dirichlet Regression and Neural Networks. Forest Science, v.61, n.2, p.289-297, 2015.) |
The proposal consisted of the construction of alternative forecast models for the proportion of planned final products that can be extracted from a forest compartment. For this, the study developed a Dirichlet regression model and an ANN to compare their respective results with a multivariate model of multiple regression benchmark. |
Only one MLP network constructed with the aid of three heuristics, in order to obtain the best topology. |
"Inputs: Mean diameter at breast height, first thinning harvest, second thinning harvest, SS/NS species and LP/LPS/OC species (both uninformed about what they mean) and elevation. Outputs: Sawlog ratio (size suitable for sawing wood, processed in sawmills); proportion of wood pallets; proportion of wood stakes; Proportion of wood pulp." |

**Miguel et al. (2015**MIGUEL, 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.) |
This study’s objective was to assess the regression analysis effectiveness and ANN models in the prediction of the amount of wood and biomass above the soil, of the arboreal vegetation in an area composed of the cerrado biome, known as “cerradão”. |
The study counted on the development of 400 networks. However, from this added value, the authors configured 100 networks for each of the four selected outputs. |
"Inputs: Basal area (G); Enhanced Vegetation Index Modified (EVI2); Normalized Difference Vegetation Index (NDVI); Soil Adjusted Vegetation Index (SAVI) and the simple ratio vegetation index (SR). Output: Total volume, stem volume, total biomass or stem biomass." |

**Santi et al. (2015**SANTI, E.; PALOSCIA, S.; PETTINATO, S.; CHIRICI, G.; MURA, M.; MASELLI, F. Application of neural networks for the retrieval of forest woody volume from SAR multifrequency data at l and C bands. European Journal of Remote Sensing, v.48, p.673-687, 2015.) |
The authors sought to investigate the potential of L-band SAR (ALOS/PALSAR) and C-band (ENVISAT/ASAR) images in the monitoring of forest biomass and, simultaneously, to develop a recovery algorithm based on ANN to estimate the wood volume the combined acquisitions of satellite images. |
"Scenario for area A: 4 MLP networks considering the input data generated by: PALSAR 4 inches, PALSAR (HH, HV), PALSAR 2 Inches + ASAR and PALSAR 4 inches + ASAR. Scenario for area B: 2 MLP networks considering all image data (ALOS/PALSAR and ENVISAT/ASAR)." |
"Inputs: Acquisitions of available frequencies and polarizations intermediated by RAS, in addition to the incidence angles and an auxiliary database. Output: Volume." |

**Aquino et al. (2016**AQUINO, P.S.R.; RODRIGUES, M.S.; CASTRO, R.V.O.; NAPPO, M.E. Use of artificial neural network in the analysis of environmental variables associated to litterfall. Comunicata Scientiae, v.7, n.3, p.394-405, 2016.) |
The proposal considered the analysis of the environmental variables that act in the litter formation in a gallery forest through the application of the ANN methodology. |
Initially, the work developed 500 MLP networks. However, it selected the best 3 ones to continue the experiment. |
"Inputs: Elevation, spatial position (x and y coordinate in UTM), monthly accumulated rainfall data, number of rainy days, maximum and minimum temperatures, average temperature, average relative air humidity, average atmospheric pressure, average rainfall, average wind speed, number of individuals and species per plot, Shannon Weaver diversity index and number of individuals from the most representative families. Outputs: Leaf fractions; Branch fractions (branches and barks); reproductive structures and total litter in grams." |

**Leite et al. (2016**LEITE, 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.) |
The study involved the basic density modeling of wood for Eucalyptus clones as a function of numerical variables obtained by CFI with the ANN use under different training standpoints: Error backpropagation, resilient propagation, Manhattan update rule, scaled conjugate gradient, levenberg marquardt, quick propagation and the SA and GA metaheuristics. |
Training of 4.16 x 10^12 and test of 4.16 x 10^10 MLP networks according to the various combinations of the number of neurons, training algorithms and activation functions. |
"Inputs: Age (years); basal area (m²/ha); annual average increase (m³/ha/year) at the measurement age; total height (m); diameter at 1.3m from the soil surface (DBH); number of stems per hectare (n/ha) and the ratio between DBH and total height. Output: Density." |

**Martins et al. (2016**MARTINS, E.R.; BINOTI, M.L.M.S.; LEITE, H.G.; BINOTI, D.H.B.; DUTRA, G.C. Configuration of artificial neural networks for estimation of total height of eucalyptus trees. Revista Brasileira de Ciências Agrárias, v.11, n.2, p.117-123, 2016.) |
The study’s scope consisted in defining the appropriate ANN configurations to obtain the total height of eucalyptus trees, taking into account different training methods, among them: the Manhattan Update Rule, Scaled Conjugate Gradient, Levenberg Marquardt and two meta- heuristics, Genetic Algorithm and Simulated Annealing. |
1.2 X 10^6 MLP networks tested through a script containing all configurations. |
"The inputs included the use of quantitative (diameter at breast height, dominant height and age) and categorical (project, clone, soil type, spacing and terrain, respectively, 4, 6, 15 and 6 classes) variables. Output: Total height of Eucalyptus trees." |

**Nunes & Gorgens (2016**NUNES, 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.) |
This study’s alignment included the assessment of the abilities of ANN models and the RF technique in the prognosis of the tree diameter, at any height and accumulated volume, along the length of the stem (depending on the measurement of the tree conicity), in three different regions: the cerrado, a tropical forest and a semi-deciduous forest. At the end, the study compared these results with a specific model of taper equation. |
1 network for the volume forecast and another for the diameter prognosis. |
"Inputs: Diameter at breast height, total height and three categorical “dummy” variables that represent the forest type of each of the scenarios studied (cerrado, tropical forest and semideciduous). Outputs: Volume and diameter." |

**Reis et al. (2016**REIS, 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.) |
The study’s purpose was to model the projection of the future diameter of individual trees in a forest managed in the Amazon, using ANN as a source of subsidy for decision-making. |
The study developed 1200 MLP networks, subdivided according to the competition index assessed. That is, 300 networks for each index (1, 2 and 3) and 300 networks that did not consider it. |
"Inputs: Semi-independent distance competition index, diameter measured at 1.30m from the soil, forest class, growth group, trunk identification class, liana infestation intensity and crown illumination. Output: Annual periodical increment of diameter." |

**Ribeiro et al. (2016**RIBEIRO, 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.) |
This study’s guidelines aimed at the application and assessment of the regression methods, based on the expansion of the Schumacher model and on ANN for the estimate of stem volume and branches in the Tapajós National Forest. |
The authors did not report the total number of networks. |
"Inputs: The work unit, type of species, DBH, commercial height and a dummy variable (0 for the stem volume and 1 for the volume of branches). Output: Volume of stems and branches." |

"**Binoti et al. (2017**BINOTI, D.H.B.; DUARTE, P.J.; BINOTI, M.L.M.S.; DA SILVA, G.F.; LEITE, H.G.; MENDONÇA, A.R.; DE ANDRADE, V.C.L.; VEGA, A.E.D. Estimation of height of Eucalyptus trees with neuroevolution of Augmenting Topologies (NEAT). Revista Árvore , v.41, n.3, 2017.)" |
This study’s objective was to assess the Neuroevolution of Augmenting Topologies (NEAT) method for adjusting the weights and the ANN topology, in the height estimate of clonal eucalypt stands, as well as to compare their predictions with the estimates found by a hypsometric regression model. |
60 Neural Networks coded by the NEAT method. |
"Inputs: Diameter adjusted to 1,3m high (DBH) and dominant height (Hd). Output: Total height." |

**Cosenza et al. (2017**COSENZA, D.N.; SOARES, A.A.V.; DE ALCÂNTARA, A.E.M.; DA SILVA, A.A.L.; REDE, R.; SOARES, V.P.; LEITE, H.G. Site classification for eucalypt stands using artificial neural network based on environmental and management features. Cerne , v.23, n.3, p.310-320, 2017.) |
This study’s objective was to evaluate the ANN for the classification of Eucalyptus plantation sites (based on silvicultural and environmental information), in order to answer two questions: to find the best network configuration for site classification and to know if the RNA approach, without stand feature as input, was more accurate than the guide-curve method. |
Initially, the work created 200 MLP networks, but only one network for each classification type (the best one) was chosen for analysis, totaling 2 networks. |
"Inputs: Genetic material, spacing, rotation, soil type and climatic information. According to the authors, they considered 17 soil types, 49 genotypes, 8 spacings and 11 different climate conditions. Outputs: The network with 3 classes presented 3 neurons at the output. For this case, the mapping classification of the area had amplitudes of 5.5m (32 - 37.5m, 26.5 - 31.9m and 21 - 26.4m). On the other hand, the network with 4 classes had 4 neurons in the output. Similar to the first case, the mapping classification of the area had amplitudes, however, with values equivalent to 4.0m: (33 - 37m, 29 - 32.9m, 25 - 28.9m and 21 - 24.9m). In other words, the output refers to the site classification based on the site index observed in the different classes with amplitude variation." |

**Lacerda et al. (2017**LACERDA, T.H.S.; CABACINHA, C.D.; ARAÚJO JÚNIOR, C.A.; MAIA, R.D.; LACERDA, K.W.S. Artificial neural networks for estimating tree volume in the Brazilian savanna. Cerne , v.23, n.4, p.483-491, 2017.) |
This study’s essence was to portray that the use of ANN can be seen as a highly feasible tool to estimate the volume of trees, considering different species of the Brazilian savanna. As a complement, it created comparisons between the estimates of the networks with some volumetric equations. |
6 MLP Networks. |
"Inputs: Situation A: total diameter and height; Situation B: commercial diameter and height; Situation C: types of species, commercial and total diameter, as well as total and commercial height. Outputs: Situation A: total volume; situation B: commercial volume and situation C: total volume (commercial + total)." |

**Vendruscolo et al. (2017**VENDRUSCOLO, D.G.S.; CHAVES, A.G.S.; MEDEIROS, R.A.; DA SILVA, R.S.; SOUZA, H.S.; DRESCHER, R.; LEITE, H.G. Height estimative of Tectona grandis L. f. trees using regression and artificial neural networks. Nativa: Pesquisas Agrárias e Ambientais, v.5, n.1, p.52-58, 2017.) |
This paper aimed at assessing and comparing the ANN modeling with the regression technique, regarding the total height estimate of Tectona grandis trees at different distances in the city of Cáceres, MT, Brazil. |
5 MLP networks. |
"Inputs: diameter at breast height (DBH), plus maximum diameter and spacing. Output: Total height." |

**Çatal &Saplioğlu (2018**ÇATAL, 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.) |
The study analyzed which method could correctly determine the amount of bark in Pinus brutia ten., in a region of Turkey. The idea was to focus on the construction of ANN models and a NeuroFuzzy adaptive inference system, as an alternative to the nonlinear regression model. |
One MLP network and one with Fuzzy logic |
"Inputs: Diameter at breast height with bark, diameter at breast height without bark and volume of the tree with bark. Output: Bark volume. " |

**Reis et al. (2018**REIS, L.P.; SOUZA, A.L.; REIS, P.C.M.; MAZZEI, L.; BINOTI, D.H.B.; LEITE, H.G. Prognosis of the diameter distribution in the Amazon by using artificial neural networks and cellular automata. Floresta, v.48, n.1, p.:93-102, 2018.a) |
The purpose was to estimate the survival and mortality of individual trees in a selectively harvested forest, from the ANN use (as a source of subsidy) for silvicultural decisions on forest management in the Amazon. |
1200 MLP networks stratified into 4 compositions each one containing 300 architectures. |
"Inputs: Diameter at breast height, forest class, trunk identification class, competition index, growth groups, liana infestation intensity, crown illumination, lesions (or not) in trees and tree rot. Outputs: Mortality and Survival." |

**Reis et al. (2018**REIS, L.P.; SOUZA, A.L.; REIS, P.C.M.; MAZZEI, L.; BINOTI, D.H.B.; LEITE, H.G. Prognosis of the diameter distribution in the Amazon by using artificial neural networks and cellular automata. Floresta, v.48, n.1, p.:93-102, 2018.b) |
The work had the proposal of using the cellular automata, as a rule of evolution in ANN, to design the distribution of diameters in harvested forest, and could serve as a decision-making for sustainable Forest Management in the Brazilian Amazon. |
Initially, the study counted on the creation of 300 networks; however, it selected only the 5 best ones.s. |
"Inputs: Current density, future density and the measurement period (in years). Output: Diametric distribution." |

**Sanquetta et al. (2018**SANQUETTA, C.R.; PIVA, L.R.O.; WOJCIECHOWSKI, J.; CORTE, A.P.D.; SCHIKOWSKI, A.B. Volume estimation of Cryptomeria japonica logs in southern Brazil using artificial intelligence models. Southern Forests, v.80, n.1, p.29-36, 2018.) |
The article sought to examine the performance of some Artificial Intelligence models (k-neighbors variant, one and three nearest neighbors and ANN) in estimating the tradable volume of Cryptomeria japonica logs in an experimental plantation in southern Brazil. |
Training of 450 MLP-networks |
"Inputs: Not informed. Output: Total volume." |

**Vieira et al. (2018**VIEIRA, 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.) |
Considering the Artificial Intelligence potential in forest measurement, this article proposed the application of ANN and a fuzzy inference system (ANFIS) to predict the growth in DBH and the Eucalyptus height. |
Although 100 trained networks, the experiment had 8 MLP and 6 ANFIS. |
"Inputs: Current diameter at 1.30 m (DBH), future age, current age, competition index (regardless of distance), genetic material, site index and current total height. One output per network, and can be directed to the future growth in DBH or future height." |