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

• Autor(a) principal: Munis, Rafaele Almeida [UNESP]
• Data de Publicação: 2022
• Outros Autores: Almeida, Rodrigo Oliveira [UNESP], Camargo, Diego Aparecido [UNESP], Silva, Richardson Barbosa Gomes da [UNESP], Wojciechowski, Jaime, Simões, Danilo [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.3390/f13071068; http://hdl.handle.net/11449/240431
• topic: blending ensemble learning; decision making; forest plantation; individual mean volumes of trees; stacking ensemble learning; terrain slope

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.