Predição do Incremento Médio Anual Volumétrico de Eucalyptus com Aprendizado de Máquina - Lopes, Adilson Rosa; Lira, Jean Marcel Sousa; Oliveira, Leonardo Araujo; Garuzzo, Marlon dos Santos Pereira Birindiba; Barbalho, Marcos Veniciu de Sá; Araújo, Patrick Oliveira Corrêa de; Santos, Gleison Augusto dos; Nacif, José Augusto (Proceedings of the Workshop on Computing Applied to the Management of the Environment and Natural Resources (WCAMA); 2023: Proceedings of the 14th Workshop of Applied Computing for the Management of the Environment and Natural Resources, 2023)
This work applied machine learning algorithms to predict the future Average Annual Volume Increment (IMAVol m³/ha/year) of eucalyptus. The dataset used is composed of physiological variables and IMAVol of eucalyptus plants from a forest genetic improvement project. By applying four ML algorithms, the results were an average of 2.84 ± 0.02 and 0.83 ± 0.03 for the root mean square error (RMSE) and coefficient of determination (R²) metrics, respectively, after performing 50 Kfold cross-validation iterations. The results are promising to support the early selection of high-volume productivity genetic materials.