Recursive diameter prediction for calculating merchantable volume of Eucalyptus clones without previous knowledge of total tree height using artificial neural networks

Authors:
FabríZzio Alphonsus A. M. N. Soares;Edna LúCia FlôRes;Christian Dias Cabacinha;Gilberto Arantes Carrijo;AntôNio CláUdio Paschoarelli Veiga
Affiliations:
Instituto de Informatica, Universidade Federal de Goiás, Brazil;Faculdade de Engenharia Elétrica, Universidade Federal de Uberlíndia, Brazil;Instituto de Ciências Agrárias, Universidade Federal de Minas Gerais, Brazil;Faculdade de Engenharia Elétrica, Universidade Federal de Uberlíndia, Brazil;Faculdade de Engenharia Elétrica, Universidade Federal de Uberlíndia, Brazil
Venue:
Applied Soft Computing
Year:
2012

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Abstract

In this work, diameters of Eucalyptus trees are predicted by means of Multilayer Perceptron and Radial Basis Function artificial neural networks. By taking only three diameter measures at the base of the tree, diameters are predicted recursively until they reach the value of minimum merchantable diameter, with no previous knowledge of total tree height. It was considered the diameter top of 4cm outside bark as minimum merchantable diameter. The training was conducted with only 10% of the trees from the total planted site. The Smalian method utilizes the predicted diameters to calculate merchantable tree volumes. The performance of the proposed model was satisfactory when predicted diameters and volumes are compared to actual ones.