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.
Highlights
It was observed that the values of RMSE and r, respectively, ranged from 3 to 8 and 0.95 to 0.99 for the training set, and from 4 to 9 and 0.95 to 0.99 for the validation set
When analysing the parameter learning cycles, in which a variation of 25 to 500 was tested with an interval of 25, it was observed that the values of RMSE and r, respectively, ranged from 2.5 to 10.39 and 0.93 to 0.99 for the training set, and from 3.33 to 10.56 and 0.93 to 0.99 for the validation set
S4.2 Configurations analysed for artificial neural networks (ANN) technique to estimate harvester productivity Figure S4 shows the statistical indicators (RMSE and r) for the settings used when the ANN weights were adjusted, with the training and validation sets
Summary
The final adjusted model is a linear function of the sum of all trees multiplied by the learning rate based on all data. ( ) Jm (D) Update Fm ( x) = Fm−1 ( x) +ν γ mL x Rjm j =1 ( ) Step 3: Output FM x where: xi = model independent variables; yi = dependent variables; n = number of observations; L = loss function; F(x) = function that provides the predicted values;γ = estimated values; M = number of trees; ν = learning rate.
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