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 | Acesso ao texto completo restrito à biblioteca da Embrapa Amazônia Oriental. Para informações adicionais entre em contato com cpatu.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Amazônia Oriental. |
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Data corrente: |
22/08/2024 |
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Data da última atualização: |
22/08/2024 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
FREITAS, P. H. de; CARNEIRO, M. G.; PROTASIO, T. P.; GONÇALVES, D. de A.; MIRANDA, R. O. V.; SOARES, A. A. V.; MARTINS, L. G. A. |
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Afiliação: |
PABLO H. DE FREITAS, UNIVERSIDADE FEDERAL DE UBERLÂNDIA; MURILLO G. CARNEIRO, UNIVERSIDADE FEDERAL DE UBERLÂNDIA; THIAGO P. PROTASIO, UNIVERSIDADE FEDERAL RURAL DA AMAZÔNIA; DELMAN DE ALMEIDA GONCALVES, CPATU; RODRIGO O. V. MIRANDA, UNIVERSIDADE FEDERAL DE UBERLÂNDIA; ALVARO A. V. SOARES, UNIVERSIDADE FEDERAL DE UBERLÂNDIA; LUIZ G. A. MARTINS, UNIVERSIDADE FEDERAL DE UBERLÂNDIA. |
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Título: |
Prediction of managed forest growth based on machine learning and cellular automata. |
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Ano de publicação: |
2024 |
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Fonte/Imprenta: |
In: IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, 2024, Yokohama. [Proceedings]... [New York]: IEEE, 2024. |
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Páginas: |
p. 1-8. |
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DOI: |
https://doi.org/10.1109/CEC60901.2024.10611880 |
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Idioma: |
Inglês |
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Conteúdo: |
The dynamics of forest plantations have been widely studied with computational simulation applications. Cellular automata (CA) is a technique capable of modelling future states based on a set of transition rules. However, this construction is not simple, often requiring technical knowledge of the process through years of scientific research. Machine learning techniques can be applied in this context, facilitating the construction of these simulators. This work presents a simulation model based on probabilistic cellular automata capable of estimating the evolution of wood production throughout the management period. Unlike other works in the literature, the construction of the CA transition rule is based exclusively on historical data from a Tachi-branco plantation, a managed forest species. Linear and logistic regression models are applied to learn and represent the local transition rules of the automaton and simulate its evolution. The proposed CA-based approach was able to predict the future behavior of plantations in the monitored areas with errors around 4%, confirming the potential of using machine learning in discovering transition rules for precise models. |
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Thesaurus Nal: |
Forest plantations; Forestry; Linear models; Plantations; Vegetation; Wood products. |
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Categoria do assunto: |
K Ciência Florestal e Produtos de Origem Vegetal |
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Marc: |
LEADER 02066nam a2200277 a 4500
001 2166703
005 2024-08-22
008 2024 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1109/CEC60901.2024.10611880$2DOI
100 1 $aFREITAS, P. H. de
245 $aPrediction of managed forest growth based on machine learning and cellular automata.$h[electronic resource]
260 $aIn: IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, 2024, Yokohama. [Proceedings]... [New York]: IEEE$c2024
300 $ap. 1-8.
520 $aThe dynamics of forest plantations have been widely studied with computational simulation applications. Cellular automata (CA) is a technique capable of modelling future states based on a set of transition rules. However, this construction is not simple, often requiring technical knowledge of the process through years of scientific research. Machine learning techniques can be applied in this context, facilitating the construction of these simulators. This work presents a simulation model based on probabilistic cellular automata capable of estimating the evolution of wood production throughout the management period. Unlike other works in the literature, the construction of the CA transition rule is based exclusively on historical data from a Tachi-branco plantation, a managed forest species. Linear and logistic regression models are applied to learn and represent the local transition rules of the automaton and simulate its evolution. The proposed CA-based approach was able to predict the future behavior of plantations in the monitored areas with errors around 4%, confirming the potential of using machine learning in discovering transition rules for precise models.
650 $aForest plantations
650 $aForestry
650 $aLinear models
650 $aPlantations
650 $aVegetation
650 $aWood products
700 1 $aCARNEIRO, M. G.
700 1 $aPROTASIO, T. P.
700 1 $aGONÇALVES, D. de A.
700 1 $aMIRANDA, R. O. V.
700 1 $aSOARES, A. A. V.
700 1 $aMARTINS, L. G. A.
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Embrapa Amazônia Oriental (CPATU) |
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| Registros recuperados : 3 | |
| 1. |  | VÁZQUEZ, D.; BERGER, A. G.; CUNIBERTI, M.; BAINOTTI, C.; MIRANDA, M. Z. de; SCHEEREN, P. L.; JOBET, C.; ZÚÑIGA, J.; CABRERA, G.; VERGES, R.; JAVIER PENA, R. Influence of cultivar and environment on quality of Latin American wheats. Journal of Cereal Science, London, v. 6, n. 2, p. 196-203, Sep. 2012.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Trigo. |
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| 2. | | PUNTEL, L. A.; BOLFE, E. L.; MELCHIORI, R. J. M.; ORTEGA, R.; TISCORNIA, G.; ROEL, A.; SCARAMUZZA, F.; BEST, S.; BERGER, A. G.; HANSEL, D. S. S.; PALACIOS, D. D.; BALBOA, G. How digital is agriculture in South America? Adoption and limitations. In: INTERNATIONAL CONFERENCE ON PRECISION AGRICULTURE, 15., 2022, Minneapolis. Proceedings... [Monticello]: International Society of Precision Agriculture, 2022. p. 1-10. ICPA 2022.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| 3. | | PUNTEL, L. A.; BOLFE, E. L.; MELCHIORI, R. J. M.; ORTEGA, R.; TISCORNIA, G.; ROEL, A.; SCARAMUZZA, F.; BEST, S.; BERGER, A. G.; HANSEL, D. S. S.; DURÁN, D. P.; BALBOA, G. R. How digital is agriculture in a subset of countries from South America? Adoption and limitations. Crop & Pasture Science, 2022. 18 p. Special issue.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| Registros recuperados : 3 | |
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