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| Registros recuperados : 92 | |
| 4. |  | DEL PONTE, E. M.; GHINI, R.; HAMADA, E.; ROSSI, P. Análise de risco de epidemias de ferrugem-asiática da soja sob cenário de mudança climática no Brasil. In: Summa Phytopathologica, Botucatu, v.34, supl. p.S42, 2008. Resumos do 21. Congresso Paulista de Fitopatologia, Campinas, 2008.| Biblioteca(s): Embrapa Meio Ambiente. |
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| 7. | | DEL PONTE, E. M.; YANG, X. B.; GODOY, C. V. A chegada aos EUA. Cultivar: grandes culturas, Pelotas, v. 6, n. 68, p. 30,32,34,36, dez./jan. 2004/2005.| Biblioteca(s): Embrapa Soja. |
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| 10. | | MAIA, A. de H. N.; DEL PONTE, E. M.; ESKER, P. D.; GODOY, C. V. Análise exploratória e seleção de ensaios para meta-análise de coeficientes de regressão linear. In: REUNIÃO ANUAL DA REGIÃO BRASILEIRA DA SOCIEDADE INTERNACIONAL DE BIOMETRIA - RBRAS, 54.; SIMPÓSIO DE ESTATÍSTICA APLICADA À EXPERIMENTAÇÃO AGRONÔMICA - SEAGRO, 13., 2009, São Carlos, SP. Anais.... São Carlos: UFSCar: Embrapa Pecuária Sudeste, 2009. 1 CD-ROM. N. 438. 5 p.| Biblioteca(s): Embrapa Meio Ambiente. |
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| 12. | | TIBOLA, C. S.; NICOLLI, C. P.; SPOLTI, P.; DEL PONTE, E. M.; FERNANDES, J. M. C. Avaliação do teste imunológico Quicktox®na quantificação de deoxinivalenol em trigo para segregação de lotes. In: REUNIÃO DA COMISSÃO BRASILEIRA DE PESQUISA DE TRIGO E TRITICALE, 8.; SEMINÁRIO TÉCNICO DO TRIGO, 9., 2014, Canela; REUNIÃO DA COMISSÃO BRASILEIRA DE PESQUISA DE TRIGO E TRITICALE, 9.; SEMINÁRIO TÉCNICO DO TRIGO, 10., 2015, Passo Fundo. Anais... Passo Fundo: Biotrigo Genética: Embrapa Trigo, 2015. 2014-Fitopatologia-Trabalho 68. 1 CD-ROM.| Biblioteca(s): Embrapa Trigo. |
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| 18. | | HECK, D. W.; RODRIGUEZ, M. A. D.; DEL PONTE, E. M.; MIZUBUTI, E. S. G. Spatial pattern analysis of Fusarium wilt of banana in Brazil. Fitosanidad, Havana, v. 21, n. especial, 2017. Edição dos resumos do VIII Seminario Científico Internacional de Sanidad Vegetal, Havana, 2017. Por la transición de la agricultura cubana hacia la sostenibilidad. Ref. Foc-10. p. 28-29.| Biblioteca(s): Embrapa Meio Ambiente. |
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| 20. | | SPOLTI, P.; VALDEBENITO-SANHUZA, R. M.; GLEASON, M. L.; DEL PONTE, E. M. Sooty blotch and flyspeck control with fungicide applications based on calendar, local IPM, and warning system. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 46, n. 7, p. 697-705, jun. 2011 Título em português: Controle de fuligem e sujeira?de?mosca com aplicações de fungicidas baseadas em calendário, MIP local e sistema de alerta| Biblioteca(s): Embrapa Unidades Centrais. |
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| Registros recuperados : 92 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
11/05/2020 |
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Data da última atualização: |
20/10/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
B - 5 |
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Autoria: |
BOCK, C. H.; BARBEDO, J. G. A.; DEL PONTE, E. M.; BOHNENKAMP, D.; MAHLEIN, A. K. |
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Afiliação: |
CLIVE H. BOCK, USDA; JAYME GARCIA ARNAL BARBEDO, CNPTIA; EMERSON M. DEL PONTE, UFV; DAVID BOHNENKAMP, University of Bonn; ANNE-KATRIN MAHLEIN, Institute of Sugar Beet Research, Germany. |
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Título: |
From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Phytopathology Research, v. 2, p. 1-30, 2020. |
|
DOI: |
https://doi.org/10.1186/s42483-020-00049-8 |
|
Idioma: |
Inglês |
|
Notas: |
Article 9. |
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Conteúdo: |
Abstract. The severity of plant diseases, traditionally the proportion of the plant tissue exhibiting symptoms, is a key quantitative variable to know for many diseases and is prone to error. Good quality disease severity data should be accurate (close to the true value). Earliest quantification of disease severity was by visual estimates. Sensor-based image analysis including visible spectrum and hyperspectral and multispectral sensors are established technologies that promise to substitute, or complement visual ratings. Indeed, these technologies have measured disease severity accurately under controlled conditions but are yet to demonstrate their full potential for accurate measurement under field conditions. Sensor technology is advancing rapidly, and artificial intelligence may help overcome issues for automating severity measurement under hyper-variable field conditions. The adoption of appropriate scales, training, instruction and aids (standard area diagrams) has contributed to improved accuracy of visual estimates. The apogee of accuracy for visual estimation is likely being approached, and any remaining increases in accuracy are likely to be small. Due to automation and rapidity, sensor-based measurement offers potential advantages compared with visual estimates, but the latter will remain important for years to come. Mobile, automated sensor-based systems will become increasingly common in controlled conditions and, eventually, in the field for measuring plant disease severity for the purpose of research and decision making. MenosAbstract. The severity of plant diseases, traditionally the proportion of the plant tissue exhibiting symptoms, is a key quantitative variable to know for many diseases and is prone to error. Good quality disease severity data should be accurate (close to the true value). Earliest quantification of disease severity was by visual estimates. Sensor-based image analysis including visible spectrum and hyperspectral and multispectral sensors are established technologies that promise to substitute, or complement visual ratings. Indeed, these technologies have measured disease severity accurately under controlled conditions but are yet to demonstrate their full potential for accurate measurement under field conditions. Sensor technology is advancing rapidly, and artificial intelligence may help overcome issues for automating severity measurement under hyper-variable field conditions. The adoption of appropriate scales, training, instruction and aids (standard area diagrams) has contributed to improved accuracy of visual estimates. The apogee of accuracy for visual estimation is likely being approached, and any remaining increases in accuracy are likely to be small. Due to automation and rapidity, sensor-based measurement offers potential advantages compared with visual estimates, but the latter will remain important for years to come. Mobile, automated sensor-based systems will become increasingly common in controlled conditions and, eventually, in the field for measuring plant dis... Mostrar Tudo |
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Palavras-Chave: |
Acurácia; Aprendizado de máquina; Aprendizado profundo; Assessment; Deep learning; Digital technologies; Dispositivo móvel; Inteligência artificial; Machine learning; Mobile device; Phenotyping; Precisão; Sensor; Severidade da doença; Tecnologias digitais. |
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Thesagro: |
Doença de Planta. |
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Thesaurus NAL: |
Accuracy; Artificial intelligence; Disease severity; Plant diseases and disorders; Precision; Precision agriculture. |
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Categoria do assunto: |
-- |
|
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/212860/1/AP-Phytopathology-Research-2020.pdf
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|
Marc: |
LEADER 02939naa a2200457 a 4500
001 2122199
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008 2020 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1186/s42483-020-00049-8$2DOI
100 1 $aBOCK, C. H.
245 $aFrom visual estimates to fully automated sensor-based measurements of plant disease severity$bstatus and challenges for improving accuracy.$h[electronic resource]
260 $c2020
500 $aArticle 9.
520 $aAbstract. The severity of plant diseases, traditionally the proportion of the plant tissue exhibiting symptoms, is a key quantitative variable to know for many diseases and is prone to error. Good quality disease severity data should be accurate (close to the true value). Earliest quantification of disease severity was by visual estimates. Sensor-based image analysis including visible spectrum and hyperspectral and multispectral sensors are established technologies that promise to substitute, or complement visual ratings. Indeed, these technologies have measured disease severity accurately under controlled conditions but are yet to demonstrate their full potential for accurate measurement under field conditions. Sensor technology is advancing rapidly, and artificial intelligence may help overcome issues for automating severity measurement under hyper-variable field conditions. The adoption of appropriate scales, training, instruction and aids (standard area diagrams) has contributed to improved accuracy of visual estimates. The apogee of accuracy for visual estimation is likely being approached, and any remaining increases in accuracy are likely to be small. Due to automation and rapidity, sensor-based measurement offers potential advantages compared with visual estimates, but the latter will remain important for years to come. Mobile, automated sensor-based systems will become increasingly common in controlled conditions and, eventually, in the field for measuring plant disease severity for the purpose of research and decision making.
650 $aAccuracy
650 $aArtificial intelligence
650 $aDisease severity
650 $aPlant diseases and disorders
650 $aPrecision
650 $aPrecision agriculture
650 $aDoença de Planta
653 $aAcurácia
653 $aAprendizado de máquina
653 $aAprendizado profundo
653 $aAssessment
653 $aDeep learning
653 $aDigital technologies
653 $aDispositivo móvel
653 $aInteligência artificial
653 $aMachine learning
653 $aMobile device
653 $aPhenotyping
653 $aPrecisão
653 $aSensor
653 $aSeveridade da doença
653 $aTecnologias digitais
700 1 $aBARBEDO, J. G. A.
700 1 $aDEL PONTE, E. M.
700 1 $aBOHNENKAMP, D.
700 1 $aMAHLEIN, A. K.
773 $tPhytopathology Research$gv. 2, p. 1-30, 2020.
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