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

Abstract

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.