Predição do Incremento Médio Anual Volumétrico de Eucalyptus com Aprendizado de Máquina
Abstract
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.
References
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Moraes, C. B. D., de Freitas, M., Casella, T., Pieroni, G. B., Vilela de Resende, M. D., Zimbacks, L., and Mori, E. S. (2014). Estimativas de parâmetros genéticos para seleção precoce de clones de eucalyptus para região com ocorrência de geadas. Scientia Forestalis.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research.
Schumacher, F. X. and Hall, F. S. (1933). Logarithmic expression of timber-tree volume. Journal of Agricultural Research, 47(9):719–734.
Silva, J. C. F., Teixeira, R. M., Silva, F. F., Brommonschenkel, S. H., and Fontes, E. P. (2019). Machine learning approaches and their current application in plant molecular biology: A systematic review. Plant Science.
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Taud, H. and Mas, J. (2018). Multilayer Perceptron (MLP).
Zaiton, S., Sheriza, M. R., Ainishifaa, R., Alfred, K., and Norfaryanti, K. (2020). Eucalyptus in malaysia: Review on environmental impacts. Journal of Landscape Ecology.
Breiman, L. (2001). Random forests. Machine Learning.
Castro, C. A. O., dos Santos, G. A., Takahashi, E. K., Nunes, A. C. P., Souza, G. A., and de Resende, M. D. (2021). Accelerating eucalyptus breeding strategies through top grafting applied to young seedlings. Industrial Crops and Products.
Chen, T. and Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining.
Chen, W., Zou, Y., Dang, Y., and Sakai, T. (2022). Spatial distribution and dynamic change monitoring of eucalyptus plantations in china during 1994–2013. Trees.
Cordeiro, M. A., Arce, J. E., Guimarães, F. A. R., Bonete, I. P., Silva, A. V. d. S., Abreu, J. C. d., and Binoti, D. H. B. (2022). Estimativas volumétricas em povoamentos de eucalipto utilizando máquinas de vetores de suporte e redes neurais artificiais. Madera y bosques.
Corrêa, T. R., de Toledo Picoli, E. A., de Souza, G. A., Conde, S. A., Silva, N. M., Lopes-Mattos, K. L. B., ..., and Oda, S. (2017). Phenotypic markers in early selection for tolerance to dieback in eucalyptus. Industrial Crops and Products.
da Silva Tavares Júnior, I., Torres, C. M. M. E., Leite, H. G., de Castro, N. L. M., Soares, C. P. B., Castro, R. V. O., and Farias, A. A. (2020). Machine learning: Modeling increment in diameter of individual trees on atlantic forest fragments. Ecological Indicators.
IBÁ (2021). Relatório Anual IBÁ Indústria Brasileira de Árvores, 2021.
Li, Y., Wang, R., Shi, W., Yu, Q., Li, X., and Chen, X. (2022). Research on accurate estimation method of eucalyptus biomass based on airborne lidar data and aerial images. Sustainability.
Moraes, C. B. D., de Freitas, M., Casella, T., Pieroni, G. B., Vilela de Resende, M. D., Zimbacks, L., and Mori, E. S. (2014). Estimativas de parâmetros genéticos para seleção precoce de clones de eucalyptus para região com ocorrência de geadas. Scientia Forestalis.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research.
Schumacher, F. X. and Hall, F. S. (1933). Logarithmic expression of timber-tree volume. Journal of Agricultural Research, 47(9):719–734.
Silva, J. C. F., Teixeira, R. M., Silva, F. F., Brommonschenkel, S. H., and Fontes, E. P. (2019). Machine learning approaches and their current application in plant molecular biology: A systematic review. Plant Science.
Smola, A. J. and Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and Computing.
Taud, H. and Mas, J. (2018). Multilayer Perceptron (MLP).
Zaiton, S., Sheriza, M. R., Ainishifaa, R., Alfred, K., and Norfaryanti, K. (2020). Eucalyptus in malaysia: Review on environmental impacts. Journal of Landscape Ecology.
Published
2023-08-06
How to Cite
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.
Predição do Incremento Médio Anual Volumétrico de Eucalyptus com Aprendizado de Máquina. In: WORKSHOP ON COMPUTING APPLIED TO THE MANAGEMENT OF THE ENVIRONMENT AND NATURAL RESOURCES (WCAMA), 14. , 2023, João Pessoa/PB.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 81-90.
ISSN 2595-6124.
DOI: https://doi.org/10.5753/wcama.2023.229896.
