Application of Artificial Intelligence in Trees Stem Volume Estimation

Volume estimation of the tree is considered as one of the important sections of the forest growth prediction and production. So far, many relationships such as: Newton, Smalian, Pressler and Huber have been used to estimate the volume of the trees that all these relationships require measurements of diameters at certain heights that are difficult to obtain on standing trees especially when diameter measurements have to be taken several meters above ground. In this study, an attempt was made to implement the new technology of Artificial Intelligence (AI), and one of its subsets as Artificial Neural Networks (ANN), since there was no primary assumption about the distribution of data, and for industrial bole volume estimation of 101 trees of trees marked of Research and Educational Forest of Tarbiat Modares University. For this purpose, DBH, diameter at stump height, end diameter stem, stem height and total tree height were measured with high accuracy. Two neural network models, multi-layer perception (MLP) and radial basis function (RBF), were developed to estimate bole volume. The results indicated that the radial basis function neural network was more accurate in bole volume estimation than the multi-layer perception neural network. Comparing evaluation criteria for ANN showed that MLP and RBF neural networks had RMSE value 1.18 and 1.05, respectively.