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 | Acesso ao texto completo restrito à biblioteca da Embrapa Pecuária Sul. Para informações adicionais entre em contato com cppsul.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Pecuária Sul. |
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Data corrente: |
03/02/2021 |
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Data da última atualização: |
03/02/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
FERREIRA, J. S. de A.; FERREIRA, A. P. L.; PEREZ, N. B. |
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Afiliação: |
Jean Samarone de Almeida Ferreira, UNIPAMPA; Ana Paula Lüdtke Ferreira, UNIPAMPA; NAYLOR BASTIANI PEREZ, CPPSUL. |
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Título: |
A hidden Markov chain approach to crop yield forecasting. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
International Journal on Computer Science and Information Systems, v. 15, n. 2, p. 148-160, 2020. |
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ISSN: |
1646-3692 |
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Idioma: |
Inglês |
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Conteúdo: |
Prediction of harvest yield is an important and challenging problem. Attempts to solve this problem rely usually rely on regression techniques highly dependent on local factors. This paper presents a hidden Markov model approach for forecasting weight production. The model can deal with any culture or provided data. Results show that the model can capture both spatial and temporal harvest variability. Model analysis can help determine causes of variability, differently from regression or more straightforward Markov chain approaches. The resulting structure can benefit from statistical techniques for model tuning and model fitting. |
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Palavras-Chave: |
Cadeia de Markov. |
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Thesagro: |
Agricultura de Precisão; Colheita. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 01236naa a2200193 a 4500
001 2129773
005 2021-02-03
008 2020 bl uuuu u00u1 u #d
022 $a1646-3692
100 1 $aFERREIRA, J. S. de A.
245 $aA hidden Markov chain approach to crop yield forecasting.$h[electronic resource]
260 $c2020
520 $aPrediction of harvest yield is an important and challenging problem. Attempts to solve this problem rely usually rely on regression techniques highly dependent on local factors. This paper presents a hidden Markov model approach for forecasting weight production. The model can deal with any culture or provided data. Results show that the model can capture both spatial and temporal harvest variability. Model analysis can help determine causes of variability, differently from regression or more straightforward Markov chain approaches. The resulting structure can benefit from statistical techniques for model tuning and model fitting.
650 $aAgricultura de Precisão
650 $aColheita
653 $aCadeia de Markov
700 1 $aFERREIRA, A. P. L.
700 1 $aPEREZ, N. B.
773 $tInternational Journal on Computer Science and Information Systems$gv. 15, n. 2, p. 148-160, 2020.
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