Application of neural networks to predict volume in eucalyptus

Abstract

The aim of this study was to evaluate the methodology of Artificial Neural Networks (ANN) in order to predict wood volume in eucalyptus and its impacts on the selection of superior families, and to compare artificial neural network with regression models. Data used were obtained in a random block design with 140 half-sib families with five replications at three years of age, and four replications at six years of age, both with five plants per plot. The volume was estimated using ANN and regression models. It was used 2000 and 1500 data to train ANN, and 1500 and 1300 to validate ANN for 3 and 6 years of age, respectively. It is concluded that ANN can help improving the accuracy to measure the volume in eucalyptus trees, and to automate the process of forestry inventory and were more accurate in predicting wood volume than almost all regression models.

Keywords

• Genetic parameters
• gain with selection
• plant breeding