| |
|
|
| Registros recuperados : 92 | |
| 27. |  | TIBOLA, C. S.; FERNANDES, J. M. C.; DEL PONTE, E. M.; SPOLTI, P.; PAVAN, W. Micotoxinas em trigo no Brasil: causas, panorama atual e perspectivas para o manejo. Revista Plantio Direto, Passo Fundo, v. 20, n. 124, p. 47-51, jul./ago. 2011.| Biblioteca(s): Embrapa Trigo. |
|    |
| 28. | | TIBOLA, C. S.; FERNANDES, J. M. C.; DEL PONTE, E. M.; SPOLTI, P.; PAVAN, W. Micotoxinas em trigo no Brasil: causas, panorama atual e perspectivas para o manejo. Revista Plantio Direto, Passo Fundo v. 20, n. 124, p. 47-51, jul./ago. 2011.| Biblioteca(s): Embrapa Unidades Centrais. |
| |
| 33. | | DEL PONTE, E. M.; GODOY, C. V.; CANTERI, M. G.; REIS, E. M.; YANG, X. B. Models and applications for risk assessment and prediction of Asian soybean rust epidemics. Fitopatologia Brasileira, Brasília, DF, v.31, n. 6, p. 533-544, Nov./Dec. 2006.| Biblioteca(s): Embrapa Soja. |
|  |
| 40. | | DEL PONTE, E. M.; MAIA, A. H. N.; ESKER, P. D.; GODOY, C. V. Sumarizando os coeficientes da regressão linear entre a severidade da ferrugem asiática e os danos na soja nos ensaios em rede no Brasil. Tropical Plant Pathology, Brasília, DF, v. 34, p. S118, ago. 2009. Suplemento, ref. 424. Edição dos Resumos do XLII Congresso Brasileiro de Fitopatologia; Annual Meeting of the Brazilian Phytopathological Society, Rio de Janeiro, ago. 2009.| Biblioteca(s): Embrapa Soja. |
| |
| Registros recuperados : 92 | |
|
|
|
Registro Completo
|
Biblioteca(s): |
Embrapa Agricultura Digital. |
|
Data corrente: |
11/05/2020 |
|
Data da última atualização: |
20/10/2021 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Circulação/Nível: |
B - 5 |
|
Autoria: |
BOCK, C. H.; BARBEDO, J. G. A.; DEL PONTE, E. M.; BOHNENKAMP, D.; MAHLEIN, A. K. |
|
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. |
|
Título: |
From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy. |
|
Ano de publicação: |
2020 |
|
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. |
|
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 |
|
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. |
|
Thesagro: |
Doença de Planta. |
|
Thesaurus NAL: |
Accuracy; Artificial intelligence; Disease severity; Plant diseases and disorders; Precision; Precision agriculture. |
|
Categoria do assunto: |
-- |
|
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/212860/1/AP-Phytopathology-Research-2020.pdf
|
|
Marc: |
LEADER 02939naa a2200457 a 4500
001 2122199
005 2021-10-20
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.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
| Nenhum registro encontrado para a expressão de busca informada. |
|
|
