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| 2. |  | LUACES, O.; RODRIGUES, L. H. A.; MEIRA, C. A. A.; QUEVEDO, J. R.; BAHAMONDE, A. Viability of an alarm predictor for Coffee Rust disease using interval regression. In: INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND OTHER APPLICATIONS OF APPLIED INTELLIGENT SYSTEMS, 23., 2010, Cordoba. Trends in applied intelligent systems: proceedings. Berlin: Springer, 2010. pt. II, p. 337-346. (Lecture notes in artificial intelligence, 6097). IEA/AIE 2010.| Biblioteca(s): Embrapa Agricultura Digital. |
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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. |
Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
04/10/2011 |
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Data da última atualização: |
13/01/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
LUACES, O.; RODRIGUES, L. H. A.; MEIRA, C. A. A.; BAHAMONDE, A. |
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Afiliação: |
OSCAR LUACES, Universidad de Oviedo; LUIZ HENRIQUE A. RODRIGUES, Feagri/Unicamp; CARLOS ALBERTO ALVES MEIRA, CNPTIA; ANTONIO BAHAMONDE, Universidad de Oviedo. |
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Título: |
Using nondeterministic learners to alert on coffee rust disease. |
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Ano de publicação: |
2011 |
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Fonte/Imprenta: |
Expert systems with applications, New York, v. 38, n. 11, p. 14276-14283, 2011. |
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Idioma: |
Inglês |
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Conteúdo: |
Motivated by an agriculture case study, we discuss how to learn functions able to predict whether the value of a continuous target variable will be greater than a given threshold. In the application studied, the aim was to alert on high incidences of coffee rust, the main coffee crop disease in the world. The objective is to use chemical prevention of the disease only when necessary in order to obtain healthier quality products and reductions in costs and environmental impact. In this context, the costs of misclassifications are not symmetrical: false negative predictions may lead to the loss of coffee crops. The baseline approach for this problem is to learn a regressor from the variables that records the factors affecting the appearance and growth of the disease. However, the number of errors is too high to obtain a reliable alarm system. The approaches explored here try to learn hypotheses whose predictions are allowed to return intervals rather than single points. Thus, in addition to alarms and non-alarms, these predictors identify situations with uncertain classification, which we call warnings. We present three different implementations: one based on regression, and two more based on classifiers. These methods are compared using a framework where the costs of false negatives are higher than that of false positives, and both are higher than the cost of warning predictions. |
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Palavras-Chave: |
Aprendizado de máquina; Avaliação de risco; Coffee; Ferrugem do café. |
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Thesagro: |
Hemileia Vastatrix. |
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Thesaurus NAL: |
Risk assessment. |
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Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
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Marc: |
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005 2020-01-13
008 2011 bl uuuu u00u1 u #d
100 1 $aLUACES, O.
245 $aUsing nondeterministic learners to alert on coffee rust disease.$h[electronic resource]
260 $c2011
520 $aMotivated by an agriculture case study, we discuss how to learn functions able to predict whether the value of a continuous target variable will be greater than a given threshold. In the application studied, the aim was to alert on high incidences of coffee rust, the main coffee crop disease in the world. The objective is to use chemical prevention of the disease only when necessary in order to obtain healthier quality products and reductions in costs and environmental impact. In this context, the costs of misclassifications are not symmetrical: false negative predictions may lead to the loss of coffee crops. The baseline approach for this problem is to learn a regressor from the variables that records the factors affecting the appearance and growth of the disease. However, the number of errors is too high to obtain a reliable alarm system. The approaches explored here try to learn hypotheses whose predictions are allowed to return intervals rather than single points. Thus, in addition to alarms and non-alarms, these predictors identify situations with uncertain classification, which we call warnings. We present three different implementations: one based on regression, and two more based on classifiers. These methods are compared using a framework where the costs of false negatives are higher than that of false positives, and both are higher than the cost of warning predictions.
650 $aRisk assessment
650 $aHemileia Vastatrix
653 $aAprendizado de máquina
653 $aAvaliação de risco
653 $aCoffee
653 $aFerrugem do café
700 1 $aRODRIGUES, L. H. A.
700 1 $aMEIRA, C. A. A.
700 1 $aBAHAMONDE, A.
773 $tExpert systems with applications, New York$gv. 38, n. 11, p. 14276-14283, 2011.
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