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Registro Completo |
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Biblioteca(s): |
Biblioteca Rui Tendinha. |
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Data corrente: |
22/02/2017 |
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Data da última atualização: |
22/02/2017 |
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Autoria: |
EMBRAPA. Secretaria de Gestão e Estratégia. |
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Título: |
V Plano Diretor da Embrapa : 2008-2011-2023. |
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Ano de publicação: |
2008 |
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Fonte/Imprenta: |
Brasília, DF : EMBRAPA, 2008. |
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Páginas: |
43 p. |
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Idioma: |
Português |
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Conteúdo: |
A importância estratégica da agricultura; Análise prospectiva - tendências, cenários e implicações para a agricultura brasileira e para a Embrapa; Parte I: A estratégia de longo prazo - 2--8-2023; Parte II: A estratégia de médio prazo - 2008-2011. |
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Palavras-Chave: |
Agricultura; Agropecuária; Brasil; Brazil; Director plain; Embrapa; Embrapa - programa; Gestão; Pecuária; Pesquisa; Pesquisa Agropecuária; Planejamento; Planning; Plano Diretor; Research policies. |
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Thesagro: |
Agronegócio; Desenvolvimento sustentável; Pesquisa agrícola; Planejamento estratégico. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 01163nam a2200349 a 4500
001 1014365
005 2017-02-22
008 2008 bl uuuu u0uu1 u #d
100 1 $aEMBRAPA. Secretaria de Gestão e Estratégia.
245 $aV Plano Diretor da Embrapa$b2008-2011-2023.
260 $aBrasília, DF : EMBRAPA$c2008
300 $a43 p.
520 $aA importância estratégica da agricultura; Análise prospectiva - tendências, cenários e implicações para a agricultura brasileira e para a Embrapa; Parte I: A estratégia de longo prazo - 2--8-2023; Parte II: A estratégia de médio prazo - 2008-2011.
650 $aAgronegócio
650 $aDesenvolvimento sustentável
650 $aPesquisa agrícola
650 $aPlanejamento estratégico
653 $aAgricultura
653 $aAgropecuária
653 $aBrasil
653 $aBrazil
653 $aDirector plain
653 $aEmbrapa
653 $aEmbrapa - programa
653 $aGestão
653 $aPecuária
653 $aPesquisa
653 $aPesquisa Agropecuária
653 $aPlanejamento
653 $aPlanning
653 $aPlano Diretor
653 $aResearch policies
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Registro original: |
Biblioteca Rui Tendinha (BRT) |
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Registro Completo |
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Biblioteca(s): |
Biblioteca Rui Tendinha. |
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Data corrente: |
06/08/2019 |
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Data da última atualização: |
06/08/2019 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
- - - |
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Autoria: |
ESGARIO, J. G. M.; KROHLING, R. A.; VENTURA, J. A. |
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Afiliação: |
José G. M. Esgario, UFES; Renato A. Krohling, UFES; Jose Aires Ventura, Incaper. |
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Título: |
Deep learning for classification and severity estimation of Coffee leaf biotic stress. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
arXiv:1907.11561, p. 1-11, 26 jul 2019. |
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Idioma: |
Inglês |
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Conteúdo: |
Biotic stress consists of damage to plants through other living organisms. Efficient control of biotic agents such as pests and pathogens (viruses, fungi, bacteria, etc.) is closely related to the concept of agricultural sustainability. Agricultural sustainability promotes the development of new technologies that allow the reduction of environmental impacts, greater accessibility to farmers and, consequently, increase on productivity. The use of computer vision with deep learning methods allows the early and correct identification of the stress-causing agent. So, corrective measures can be applied as soon as possible to mitigate the problem. The objective of this work is to design an effective and practical system capable of identifying and estimating the stress severity caused by biotic agents on coffee leaves. The proposed approach consists of a multi-task system based on convolutional neural networks. In addition, we have explored the use of data augmentation techniques to make the system more robust and accurate. The experimental results obtained for classification as well as for severity estimation indicate that the proposed system might be a suitable tool to assist both experts and farmers in the identification and quantification of biotic
stresses in coffee plantations. |
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Thesaurus NAL: |
Agricultural sustainability; Biotic agents; Biotic stress; Coffee; Pathogens. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://biblioteca.incaper.es.gov.br/digital/bitstream/123456789/3652/1/deep-learning-classification-severty-coffee-ventura.pdf
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Marc: |
LEADER 01889naa a2200205 a 4500
001 1021504
005 2019-08-06
008 2019 bl uuuu u00u1 u #d
100 1 $aESGARIO, J. G. M.
245 $aDeep learning for classification and severity estimation of Coffee leaf biotic stress.$h[electronic resource]
260 $c2019
520 $aBiotic stress consists of damage to plants through other living organisms. Efficient control of biotic agents such as pests and pathogens (viruses, fungi, bacteria, etc.) is closely related to the concept of agricultural sustainability. Agricultural sustainability promotes the development of new technologies that allow the reduction of environmental impacts, greater accessibility to farmers and, consequently, increase on productivity. The use of computer vision with deep learning methods allows the early and correct identification of the stress-causing agent. So, corrective measures can be applied as soon as possible to mitigate the problem. The objective of this work is to design an effective and practical system capable of identifying and estimating the stress severity caused by biotic agents on coffee leaves. The proposed approach consists of a multi-task system based on convolutional neural networks. In addition, we have explored the use of data augmentation techniques to make the system more robust and accurate. The experimental results obtained for classification as well as for severity estimation indicate that the proposed system might be a suitable tool to assist both experts and farmers in the identification and quantification of biotic stresses in coffee plantations.
650 $aAgricultural sustainability
650 $aBiotic agents
650 $aBiotic stress
650 $aCoffee
650 $aPathogens
700 1 $aKROHLING, R. A.
700 1 $aVENTURA, J. A.
773 $tarXiv:1907.11561, p. 1-11, 26 jul 2019.
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