Modeling of stem form and volume through machine learning

Abstract Taper functions and volume equations are essential for estimation of the individual volume, which have consolidated theory. On the other hand, mathematical innovation is dynamic, and may improve the forestry modeling. The objective was analyzing the accuracy of machine learning (ML) techniques in relation to a volumetric model and a taper function for acácia negra. We used cubing data, and fit equations with Schumacher and Hall volumetric model and with Hradetzky taper function, compared to the algorithms: k nearest neighbor (k-NN), Random Forest (RF) and Artificial Neural Networks (ANN) for estimation of total volume and diameter to the relative height. Models were ranked according to error statistics, as well as their dispersion was verified. Schumacher and Hall model and ANN showed the best results for volume estimation as function of dap and height. Machine learning methods were more accurate than the Hradetzky polynomial for tree form estimations. ML models have proven to be appropriate as an alternative to traditional modeling applications in forestry measurement, however, its application must be careful because fit-based overtraining is likely.