Machine Learning Methods to Estimate Productivity of Harvesters: Mechanized Timber Harvesting in Brazil

Abstract

The correct capture of forest operations information carried out in forest plantations can help in the management of mechanized harvesting timber. Proper management must be able to dimension resources and tools necessary for the fulfillment of operations and helping in strategic, tactical, and operational planning. In order to facilitate the decision making of forest managers, this work aimed to analyze the performance of machine learning algorithms in estimating the productivity of timber harvesters. As predictors of productivity, we used the availability of hours of machine use, individual mean volumes of trees, and terrain slopes. The dataset was composed of 144,973 records, carried out over a period of 28 months. We tested the predictive performance of 24 machine learning algorithms in default mode. In addition, we tested the performance of blending and stacking joint learning methods. We evaluated the model’s fit using the root mean squared error, mean absolute error, mean absolute percentage error, and determination coefficient. After cleaning the initial database, we used only 1.12% to build the model. Learning by blending ensemble stood out with a determination coefficient of 0.71 and a mean absolute percentage error of 15%. From the use of data from machine learning algorithms, it became possible to predict the productivity of timber harvesters. Testing a variety of machine learning algorithms with different dynamics contributed to the machine learning technique that helped us reach our goal: maximizing the model’s performance by conducting experimentation.

Inteligência Artificial Aplicada À Ciência Florestal

“…Na colheita de madeira mecanizada, uma grande quantidade de dados é gerada a partir do registro das atividades das máquinas que compõem os diferentes sistemas de colheita. A partir desses registros, tem-se pesquisado a criação de sistemas de diagnósticos de falhas em colheitadeiras florestais, bem como a geração de modelos capazes de predizer a produtividade, tornado o manejo sob condições de colheita mecanizada, cada vez mais, operacional (HEIDARI et al, 2022;MUNIS et al 2022;ZHANG et al 2022;MARTINS et al, 2023). Superadas essas limitações, os gestores florestais são capazes de extrair informações úteis e tomar decisões mais seguras a respeito de um conjunto de dados, como, por exemplo, estimar a produtividade das máquinas florestais autopropelidas utilizadas na colheita de madeira mecanizada, sob diferentes configurações, a fim de otimizar a operação (KUDYBA, 2018;JHAEGER et al, 2020;LISK et al, 2020).…”

Prediction of Road Transport of Wood in Uruguay: Approach with Machine Learning

“…Using the R programming language [38], data wrangling routines were implemented for planning, managing, and detecting the quality of data to be consumed by machine learning algorithms. To create a balanced dataset, the oversampling technique was applied [39]. Using the random forests algorithm [40], the importance of the attributes for the modeling process was observed and, through the spearman correlation [41], the parameters correlated with 5% of significance were excluded.…”