Recursive Diameter Prediction using Neural Networks to calculate comercial volume of Eucalyptus Clones
Abstract
This work made predictions of tree diameters of eucalyptus species with artificial neural networks Multiple Layers Perceptron. Recursively estimates are these diameters up to the value of 4 cm using only three steps diameter at the base and without the need knowledge of the total height of the tree. The training was conducted with only 10% of the total planted site. The performance of the proposed model was satisfactory when compared with the diameters predicted actual diameters.
Text in Portuguese, Original Title: Predicao Recursiva De Diametros Utilizando Redes Neurais Artificiais Para calculo De Volumes Comerciais De Clones De Eucalipto.
Fabrizzio Soares
Associate Professor and CS Chair
Fabrizzio Soares is a professor of Computer Science, Information Systems and Software Engineering at INF/UFG. His research interests include Computer Vision, Human Computer Interaction, Machine Learning and Programming topics. He is the leader of the Pixellab group, which develops solutions for accesibilty, Precision Agriculture, and Interactive Systems.