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

Jianne Rafaela Mazzini de Souza; Renato Vinícius de Oliveira Castro; Ivaldo da Silva Tavares Júnior; Reginaldo Arthur Gloria Marcelino; Rakiely Martins da Silva; Sherellyn Daphnee Alves Moretti

doi:10.5380/rf.v53i2.78754

Autores

Jianne Rafaela Mazzini de Souza Universidade Federal de Viçosa (UFV), Viçosa, Minas Gerais. https://orcid.org/0000-0002-0803-4438
Renato Vinícius de Oliveira Castro Universidade Federal de São João Del Rei (UFSJ-CSL), Sete Lagoas, Minas Gerais.
Ivaldo da Silva Tavares Júnior Universidade Federal de Viçosa (UFV), Viçosa, Minas Gerais. https://orcid.org/0000-0001-5226-0638
Reginaldo Arthur Gloria Marcelino Universidade Federal de São João Del Rei (UFSJ-CSL), Sete Lagoas, Minas Gerais
Rakiely Martins da Silva Universidade Estadual do Norte Fluminense Darcy Ribeiro (UENF),
Sherellyn Daphnee Alves Moretti Universidade Federal de Viçosa (UFV), Viçosa, Minas Gerais.

DOI:

https://doi.org/10.5380/rf.v53i2.78754

Palavras-chave:

Taper modeling, Multilayer Perceptron, Network topologies.

Resumo

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, h_i, A and Clone inputs; scenario 2 with Ht, dbh, h_i and Clone; scenario 3 with Ht, dbh, h_i and A; and scenario 4 with Ht, dbh and h_i. 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.