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Registro Completo |
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
03/11/2009 |
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Data da última atualização: |
03/11/2009 |
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Tipo da produção científica: |
Artigo em Anais de Congresso / Nota Técnica |
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Autoria: |
MEINKE, H.; BASTIAANS, L.; BOUMAN, B.; DINGKUHN, M.; GAYDON, D.; HASEGAWA, T.; HEINEMANN, A. B.; KIEPE, P.; LAFARGE, T.; LUQUET, D.; MASOOD, A.; OORT, P. van; RODENBURG, J.; YAN, J.; YIN, X. |
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Afiliação: |
H. MEINKE; L. BASTIAANS; B. BOUMAN; M. DINGKUHN; D. GAYDON; T. HASEGAWA; ALEXANDRE BRYAN HEINEMANN, CNPAF; P. KIEPE; T. LAFARGE; D. LUQUET; A. MASOOD; P. VAN OORT; J. RODENBURG; J. YAN; X. YIN. |
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Título: |
An international collaborative research network helps to design climate robust rice systems. |
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Ano de publicação: |
2009 |
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Fonte/Imprenta: |
In: MARCO SYMPOSIUM 2009: WORKSHOP 2: CROP PRODUCTION UNDER HEAT STRESS: MONITORING, IMPACT ASSESSMENT AND ADAPTATION, 2009, Tsukuba. Challenges for agro-environmental research in Monsoon Asia. Tsukuba: National Institute for AGr-Environmental Sciences, 2009. |
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Páginas: |
11 p. |
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Idioma: |
Inglês |
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Conteúdo: |
The supply challenges. The environmental challenges: risks and opportunities. The scale challenges. Responding to the impacts of climate variability and change. Modelling rice-based systems - a pivotal technology for innovation in R4D. Concluding remarks. |
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Palavras-Chave: |
Modelo; Segurança do alimento. |
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Thesagro: |
Arroz; Clima; Fisiologia; Oryza sativa. |
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Thesaurus Nal: |
Climate change; Drought; Food security; Models; Rice. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 01530naa a2200433 a 4500
001 1573820
005 2009-11-03
008 2009 bl uuuu u00u1 u #d
100 1 $aMEINKE, H.
245 $aAn international collaborative research network helps to design climate robust rice systems.
260 $c2009
300 $a11 p.
520 $aThe supply challenges. The environmental challenges: risks and opportunities. The scale challenges. Responding to the impacts of climate variability and change. Modelling rice-based systems - a pivotal technology for innovation in R4D. Concluding remarks.
650 $aClimate change
650 $aDrought
650 $aFood security
650 $aModels
650 $aRice
650 $aArroz
650 $aClima
650 $aFisiologia
650 $aOryza sativa
653 $aModelo
653 $aSegurança do alimento
700 1 $aBASTIAANS, L.
700 1 $aBOUMAN, B.
700 1 $aDINGKUHN, M.
700 1 $aGAYDON, D.
700 1 $aHASEGAWA, T.
700 1 $aHEINEMANN, A. B.
700 1 $aKIEPE, P.
700 1 $aLAFARGE, T.
700 1 $aLUQUET, D.
700 1 $aMASOOD, A.
700 1 $aOORT, P. van
700 1 $aRODENBURG, J.
700 1 $aYAN, J.
700 1 $aYIN, X.
773 $tIn: MARCO SYMPOSIUM 2009: WORKSHOP 2: CROP PRODUCTION UNDER HEAT STRESS: MONITORING, IMPACT ASSESSMENT AND ADAPTATION, 2009, Tsukuba. Challenges for agro-environmental research in Monsoon Asia. Tsukuba: National Institute for AGr-Environmental Sciences, 2009.
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Registro original: |
Embrapa Arroz e Feijão (CNPAF) |
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Biblioteca |
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Origem |
Tipo/Formato |
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Cutter |
Registro |
Volume |
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URL |
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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: |
03/10/2018 |
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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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Circulação/Nível: |
A - 1 |
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Autoria: |
BARBEDO, J. G. A. |
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Afiliação: |
JAYME GARCIA ARNAL BARBEDO, CNPTIA. |
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Título: |
Factors influencing the use of deep learning for plant disease recognition. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Biosystems Engineering, v. 172, p. 84-91, Aug. 2018. |
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DOI: |
https://doi.org/10.1016/j.biosystemseng.2018.05.013 |
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Idioma: |
Inglês |
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Conteúdo: |
Deep learning is quickly becoming one of the most important tools for image classification. This technology is now beginning to be applied to the tasks of plant disease classification and recognition. The positive results that are being obtained using this approach hide some issues that are seldom taken into account in the respective experiments. This article presents an investigation into the main factors that affect the design and effectiveness of deep neural nets applied to plant pathology. An in-depth analysis of the subject, in which advantages and shortcomings are highlighted, should lead to more realistic conclusions on the subject. The arguments used throughout the text are built upon both studies found in the literature and experiments carried out using an image database carefully built to reflect and reproduce many of the conditions expected to be found in practice. This database, which contains almost 50,000 images, is being made freely available for academic purposes. |
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Palavras-Chave: |
Deep neural nets; Disease classification; Image database; Image processing; Inteligência artificial; Processamento de imagem; Redes neurais; Transfer learning. |
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Thesagro: |
Doença de Planta. |
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Thesaurus NAL: |
Artificial intelligence; Image analysis; Neural networks; Plant diseases and disorders. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 01908naa a2200289 a 4500
001 2096829
005 2020-01-07
008 2018 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.biosystemseng.2018.05.013$2DOI
100 1 $aBARBEDO, J. G. A.
245 $aFactors influencing the use of deep learning for plant disease recognition.$h[electronic resource]
260 $c2018
520 $aDeep learning is quickly becoming one of the most important tools for image classification. This technology is now beginning to be applied to the tasks of plant disease classification and recognition. The positive results that are being obtained using this approach hide some issues that are seldom taken into account in the respective experiments. This article presents an investigation into the main factors that affect the design and effectiveness of deep neural nets applied to plant pathology. An in-depth analysis of the subject, in which advantages and shortcomings are highlighted, should lead to more realistic conclusions on the subject. The arguments used throughout the text are built upon both studies found in the literature and experiments carried out using an image database carefully built to reflect and reproduce many of the conditions expected to be found in practice. This database, which contains almost 50,000 images, is being made freely available for academic purposes.
650 $aArtificial intelligence
650 $aImage analysis
650 $aNeural networks
650 $aPlant diseases and disorders
650 $aDoença de Planta
653 $aDeep neural nets
653 $aDisease classification
653 $aImage database
653 $aImage processing
653 $aInteligência artificial
653 $aProcessamento de imagem
653 $aRedes neurais
653 $aTransfer learning
773 $tBiosystems Engineering$gv. 172, p. 84-91, Aug. 2018.
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Embrapa Agricultura Digital (CNPTIA) |
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