Configuração de redes neurais artificiais para relação hipsométrica de árvores de Eucalyptus spp.

Rocha, Jonas Elias Castro da; Nogueira, Marlon Roque; Júnior, Ivaldo da Silva Tavares; Souza, Jianne Rafaela Mazzini de; Lopes, Lucas Sérgio de Sousa; Silva, Márcio Lopes da

doi:10.18671/scifor.v49n132.08

Cited by 4 publications

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“…In general, advances with the use of ANNs led to the appearance of different available configurations that can be used in the modeling of forest variables (COSTA FILHO et al, 2019). However, choosing the appropriate configuration and input variables in an ANN is a process that can take a long time (ROCHA et al, 2021). The evaluation of different ANN configurations for modeling the stem tapering of trees is extremely important for the forestry sector, as it indicates the best ANN configurations for this application, among the many possible combinations.…”

Section: Discussionmentioning

confidence: 99%

STEM TAPERING OF Eucalyptus spp. USING DIFFERENT CONFIGURATIONS OF ARTIFICIAL NEURAL NETWORKS

Souza

Castro

Júnior

et al. 2023

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The objective of this work was to test and evaluate different configurations of artificial neural networks (ANNs) for modeling tree stem taper in Eucalyptus spp. in strands in the microregion of Pirapora, Minas Gerais. The data used came from 8,410 Eucalyptus spp. at different speeds. The quantitative variables measured were: age, total height, diameter at the height of 1.30 m (dbh), diameter and height in different positions on the stem. The only qualitative variable measured was the clone. Four scenarios were evaluated: scenario 1 with Ht, dbh, hi, A and Clone inputs; scenario 2 with Ht, dbh, hi and Clone; scenario 3 with Ht, dbh, hi and A; and scenario 4 with Ht, dbh and hi. We tested different ANNs topologies of the Multilayer Perceptron type. The ANNs 102 (neurons in the hidden layer = 18; function = Exponential; algorithm = Rprop), 91 (neurons in the hidden layer = 19; function = Exponential; algorithm = Rprop), 13 (neurons in the hidden layer = 7; Function = Exponential; Algorithm = SCG) and 27 (neurons in the hidden layer = 6; function = Exponential; algorithm = Rprop) presented the best measures of statistical accuracy in training to predict the bottleneck in scenarios 1, 2, 3 and 4, respectively. The ANN 103 (neurons in the hidden layer = 19; function = Exponential; algorithm = Rprop) from scenario 1 presented good statistical results in the validation. Thus, the ANNs were efficient in predicting the diameter along the Eucalyptus spp stem.

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Section: Discussionmentioning

confidence: 99%

STEM TAPERING OF Eucalyptus spp. USING DIFFERENT CONFIGURATIONS OF ARTIFICIAL NEURAL NETWORKS

Souza

Castro

Júnior

et al. 2023

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Configuration of the Deep Neural Network Hyperparameters for the Hypsometric Modeling of the Guazuma crinita Mart. in the Peruvian Amazon

Casas

Gonzáles

Villanueva

et al. 2022

Forests

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The Guazuma crinita Mart. is a dominant species of great economic importance for the inhabitants of the Peruvian Amazon, standing out for its rapid growth and being harvested at an early age. Understanding its vertical growth is a challenge that researchers have continued to study using different hypsometric modeling techniques. Currently, machine learning techniques, especially artificial neural networks, have revolutionized modeling for forest management, obtaining more accurate predictions; it is because we understand that it is of the utmost importance to adapt, evaluate and apply these methods in this species for large areas. The objective of this study was to build and evaluate the efficiency of the use of a deep neural network for the prediction of the total height of Guazuma crinita Mart. from a large-scale continuous forest inventory. To do this, we explore different configurations of the hidden layer hyperparameters and define the variables according to the function HT = f(x) where HT is the total height as the output variable and x is the input variable(s). Under this criterion, we established three HT relationships: based on the diameter at breast height (DBH), (i) HT = f(DBH); based on DBH and Age, (ii) HT = f(DBH, Age) and based on DBH, Age and Agroclimatic variables, (iii) HT = f(DBH, Age, Agroclimatology), respectively. In total, 24 different configuration models were established for each function, concluding that the deep artificial neural network technique presents a satisfactory performance for the predictions of the total height of Guazuma crinita Mart. for modeling large areas, being the function based on DBH, Age and agroclimatic variables, with a performance validation of RMSE = 0.70, MAE = 0.50, bias% = −0.09 and VAR = 0.49, showed better accuracy than the others.

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Conversion Factor Estimation of Stacked Eucalypt Timber Using Supervised Image Classification with Artificial Neural Networks

de Barros,

Boechat Soares,

da Silva

et al. 2024

JST

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Stacked timber is quantified in-store units and then adjusted with a conversion factor for volume estimation in cubic meters, which is important for the wood trade in South America. However, measuring large quantities accurately can be challenging. Digital image processing and artificial intelligence advancements offer promising solutions, making research in this area increasingly attractive. This study aims to estimate conversion factors of stacked Eucalyptus grandis timber using supervised image classification with Artificial Neuronal Network (ANN). Measured data and photographs from an experiment involving thirty stacks of timber were used to achieve this. The conversion factor was determined using photographic methods that involved the applications of equidistant points and ANN and subsequently validated with values observed through the manual method. The ANN method produced more accurate conversion factor estimates than the equidistant points method. Approximately 97% of the ANN estimates were within the ±1% error class, even when using low-resolution digital photographs.

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Configuração de redes neurais artificiais para relação hipsométrica de árvores de Eucalyptus spp.

Cited by 4 publications

References 0 publications

STEM TAPERING OF Eucalyptus spp. USING DIFFERENT CONFIGURATIONS OF ARTIFICIAL NEURAL NETWORKS

STEM TAPERING OF Eucalyptus spp. USING DIFFERENT CONFIGURATIONS OF ARTIFICIAL NEURAL NETWORKS

Configuration of the Deep Neural Network Hyperparameters for the Hypsometric Modeling of the Guazuma crinita Mart. in the Peruvian Amazon

Conversion Factor Estimation of Stacked Eucalypt Timber Using Supervised Image Classification with Artificial Neural Networks

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