Cut-to-Length Harvesting Prediction Tool: Machine Learning Model Based on Harvest and Weather Features

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorScience and Technology—Southeast of Minas Gerais (IFET)-
Autor(es): dc.creatorAlmeida, Rodrigo Oliveira-
Autor(es): dc.creatorda Silva, Richardson Barbosa Gomes-
Autor(es): dc.creatorSimões, Danilo-
Data de aceite: dc.date.accessioned2025-08-21T17:23:38Z-
Data de disponibilização: dc.date.available2025-08-21T17:23:38Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-08-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/f15081398-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/307946-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/307946-
Descrição: dc.descriptionWeather is a significant factor influencing forest health, productivity, and the carbon cycle. However, our understanding of these effects is limited for many regions and ecosystems. Assessing the impact of weather variability on harvester productivity from plantation forests may assist in forest planning through the use of data modeling. We investigated whether weather data combined with timber harvesting attributes could be used to create a high-performance model that could accurately predict harvester productivity in Eucalyptus plantations using machine learning. Furthermore, we aimed to provide an online application to assist forest managers in applying the model. For the modeling, we considered 15 weather and timber harvesting attributes. We considered productivity as the target attribute. We subjected the database to 24 common algorithms in default mode and compared them according to error metrics and accuracy. From the timber harvesting features combined with weather features, the Catboost model can predict the productivity of harvesters in a tuned mode, with a coefficient of determination of 0.70. The use of weather data combined with timber harvesting attributes in the model is an accurate approach for predicting harvester productivity in Eucalyptus plantations, allowing for the creation of an online, free application to assist forest managers.-
Descrição: dc.descriptionDepartment of Forest Science Soils and Environment School of Agriculture São Paulo State University (UNESP)-
Descrição: dc.descriptionFederal Institute of Education Science and Technology—Southeast of Minas Gerais (IFET)-
Descrição: dc.descriptionDepartment of Forest Science Soils and Environment School of Agriculture São Paulo State University (UNESP)-
Idioma: dc.languageen-
Relação: dc.relationForests-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectartificial intelligence-
Palavras-chave: dc.subjectEucalyptus planted forests-
Palavras-chave: dc.subjectforest operations-
Palavras-chave: dc.subjectmeteorological data mechanized-
Palavras-chave: dc.subjecttimber harvesting-
Título: dc.titleCut-to-Length Harvesting Prediction Tool: Machine Learning Model Based on Harvest and Weather Features-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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