Do AI Models Improve Taper Estimation? A Comparative Approach for Teak

Abstract

Correctly estimating stem diameter at any height is an essential task in determining the profitability of a commercial forest plantation, since the integration of the cross-sectional area along the stem of the trees allows estimating the timber volume. In this study the ability of four artificial intelligence (AI) models to estimate the stem diameter of Tectona grandis was assessed. Genetic Programming (PG), Gaussian Regression Process (PGR), Category Boosting (CatBoost) and Artificial Neural Networks (ANN) models’ ability was evaluated and compared with those of Fang 2000 and Kozak 2004 conventional models. Coefficient of determination (R2), Root Mean Square of Error (RMSE), Mean Error of Bias (MBE) and Mean Absolute Error (MAE) statistical indices were used to evaluate the models’ performance. Goodness of fit criterion of all the models suggests that Kozak’s model shows the best results, closely followed by the ANN model. However, PG, PGR and CatBoost outperformed the Fang model. Artificial intelligence methods can be an effective alternative to describe the shape of the stem in Tectona grandis trees with an excellent accuracy, particularly the ANN and CatBoost models.