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

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 4 cm 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.

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► With three diameters neighbors as input an MLP can predict a diameter ahead.
► Diameters estimated are very close to real with too small errors.
► Recursively an MLP can predict all diameters of a tree to 4 cm.
► Calculated volumes from the predicted diameters are as good as actual values.
► Predictions are realized without knowledge about total height of the trees.