Machine learning techniques to estimate mechanised forest cutting productivity

Abstract

The productivity of wood harvesting operations is one of the main viability indicators of the forestry enterprise, which is directly influenced by land, population, and operational planning characteristics. The variables that affect the productivity of harvesting machines are particularly difficult to measure and have complex relationships, making it challenging to predict the productivity of operations. This study generated a model using machine learning (ML) techniques to estimate harvesting productivity in Eucalyptus plantations in southeastern Brazil. The input variables for modelling harvesting productivity were the average individual tree volumes, wood volume in the stand, cutting age, spacing, operator experience, and the management regime. The database was randomly divided into training (70%) and validation (30%) datasets. Boosted, artificial neural network (ANN), and adaptive network-based fuzzy inference system (ANFIS) techniques were used to fit the model and were evaluated through statistics and graphical analysis of the residues. The configurations selected for training and validation to estimate harvester productivity resulted in correlation coefficient values greater than 0.9, and root-mean-square error (RMSE) percentages less than 12.41, indicating a strong correlation and high accuracy between the estimates and the observed values. The boosted technique yielded the best results, with correlation coefficients of 0.98 and 0.97, and RMSE percentages of 6.15 and 6.65 in training and validation, respectively. The worst performance for estimating harvesting productivity was obtained using the ANFIS technique. ML techniques were efficient in modelling the productivity of mechanised forest cutting with a harvesting model.