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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
20/12/2017 |
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Data da última atualização: |
07/01/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
LASSO, E.; THAMADA, T. T.; MEIRA, C. A. A.; CORRALES, J. C. |
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Afiliação: |
EMMANUEL LASSO, University of Cauca; THIAGO TOSHIYUKI THAMADA, Unicamp; CARLOS ALBERTO ALVES MEIRA, CNPTIA; JUAN CARLOS CORRALES, University of Cauca. |
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Título: |
Expert system for coffee rust detection based on supervised learning and graph pattern matching. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
International Journal of Metadata, Semantics and Ontologies, v. 12, n. 1, p. 19-27, 2017. |
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DOI: |
10.1504/IJMSO.2017.10008638 |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract: Diseases in agricultural production systems represent one of the main reasons of losses and poor-quality products. For coffee production, experts in this area suggest that weather conditions and crop physical properties are the main variables that determine the development of coffee rust. This paper proposes an extraction of rules to detect coffee rust from induction of decision trees and expert knowledge. In order to obtain a model with greater expressiveness and interpretability, a graph-based representation is proposed. Finally, the extracted rules are evaluated using an expert system supported on graph pattern matching. |
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Palavras-Chave: |
Árvore de decisão; Coffee rust; Decision tree; Ferrugem cafeeira; Graph pattern matching; Sistema especialista. |
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Thesagro: |
Agricultura; Doença de planta; Hemileia vastatrix. |
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Thesaurus Nal: |
Agriculture; Expert systems; Plant diseases and disorders. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
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001 2082986
005 2020-01-07
008 2017 bl uuuu u00u1 u #d
024 7 $a10.1504/IJMSO.2017.10008638$2DOI
100 1 $aLASSO, E.
245 $aExpert system for coffee rust detection based on supervised learning and graph pattern matching.$h[electronic resource]
260 $c2017
520 $aAbstract: Diseases in agricultural production systems represent one of the main reasons of losses and poor-quality products. For coffee production, experts in this area suggest that weather conditions and crop physical properties are the main variables that determine the development of coffee rust. This paper proposes an extraction of rules to detect coffee rust from induction of decision trees and expert knowledge. In order to obtain a model with greater expressiveness and interpretability, a graph-based representation is proposed. Finally, the extracted rules are evaluated using an expert system supported on graph pattern matching.
650 $aAgriculture
650 $aExpert systems
650 $aPlant diseases and disorders
650 $aAgricultura
650 $aDoença de planta
650 $aHemileia vastatrix
653 $aÁrvore de decisão
653 $aCoffee rust
653 $aDecision tree
653 $aFerrugem cafeeira
653 $aGraph pattern matching
653 $aSistema especialista
700 1 $aTHAMADA, T. T.
700 1 $aMEIRA, C. A. A.
700 1 $aCORRALES, J. C.
773 $tInternational Journal of Metadata, Semantics and Ontologies$gv. 12, n. 1, p. 19-27, 2017.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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| 1. |  | BARTZ, M. L. C.; BARRETO, J.; SANTOS, A.; DUDAS, R. T.; FERREIRA, T.; MAIA, L. dos S.; DEMETRIO, W. C.; SMOKANIT, M.; TAVARES, A. A.; SCHUSTER, P. A.; HERNANI, L. C.; BROWN, G. G. Earthworm richness in no-tillage farming systems and riparian forests in Southern and Southeastern Brazil. Zootaxa, v. 5255, n. 1, p. 362-376, 2023.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 4 |
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