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Registro Completo |
Biblioteca(s): |
Embrapa Gado de Corte. |
Data corrente: |
14/03/2001 |
Data da última atualização: |
14/03/2001 |
Autoria: |
BRANCIO, P. A.; NASCIMENTO JUNIOR, D. do; EUCLIDES, V. P. B.; REGAZZI, A. J.; ALMEIDA, R. G. de; FONSECA, D. M. da. |
Afiliação: |
UFV, estudante de doutorado em zootecnia. |
Título: |
Avaliacao de tres cultivares de Panicum maximum Jacq. sob pastejo 5. Tamanho do Bocado. |
Ano de publicação: |
2000 |
Fonte/Imprenta: |
In: REUNIAO ANUAL DA SOCIEDADE BRASILEIRA DE ZOOTECNIA, 37., 2000, Vicosa. Resumos dos trabalhos apresentados. Anais. Vicosa: SBZ, 2000. |
Páginas: |
p.39. |
Idioma: |
Português |
Notas: |
CNPGC. |
Conteúdo: |
Tres cultivares de Panicum maximum Jacq. foram avaliados ao longo do ano quanto ao tamanho de bocados, estimado utilizando-se bovinos esofago-fistulados e tambem por meio da divisao do consumo de forragem diario pelo numero de bocados realizados por dia. Os tratamentos constituiram em: 1) cv.Tanzaniacom adubacao nitrogenada, 2) cv Tanzaniza sem adubacao nitrogenada, 3) cv. Mombaca e 4) cv Massai, avaliadas em junho, setembro e novembro de 1998 e marco de 1999. Maiores tamanhos de boicados ocorreram no inicio do periodo chuvoso, associados a maiores proporcoes de folha verde e melhor qualidade do pasto. O tamanho de bocado foi em geral menor no trtamento cv. Massai, o que pode ser atribuido a maior proporcao de material morto e a distribuicao generalizada deste material ao longo do perfil da pastagem. Os valores para tamanho de bocado com o uso de animais fuistulados foram superestimados. |
Palavras-Chave: |
Animal behaviour; Bites; Bocado; Bovino de corte; Massai; Mombaca; Pastejo rotacionado. |
Thesagro: |
Comportamento Animal; Panicum Maximum; Pastagem. |
Thesaurus Nal: |
beef cattle; pastures; rotational grazing; Tanzania. |
Categoria do assunto: |
-- |
Marc: |
LEADER 01968naa a2200373 a 4500 001 1323981 005 2001-03-14 008 2000 bl uuuu u00u1 u #d 100 1 $aBRANCIO, P. A. 245 $aAvaliacao de tres cultivares de Panicum maximum Jacq. sob pastejo 5. Tamanho do Bocado. 260 $c2000 300 $ap.39. 500 $aCNPGC. 520 $aTres cultivares de Panicum maximum Jacq. foram avaliados ao longo do ano quanto ao tamanho de bocados, estimado utilizando-se bovinos esofago-fistulados e tambem por meio da divisao do consumo de forragem diario pelo numero de bocados realizados por dia. Os tratamentos constituiram em: 1) cv.Tanzaniacom adubacao nitrogenada, 2) cv Tanzaniza sem adubacao nitrogenada, 3) cv. Mombaca e 4) cv Massai, avaliadas em junho, setembro e novembro de 1998 e marco de 1999. Maiores tamanhos de boicados ocorreram no inicio do periodo chuvoso, associados a maiores proporcoes de folha verde e melhor qualidade do pasto. O tamanho de bocado foi em geral menor no trtamento cv. Massai, o que pode ser atribuido a maior proporcao de material morto e a distribuicao generalizada deste material ao longo do perfil da pastagem. Os valores para tamanho de bocado com o uso de animais fuistulados foram superestimados. 650 $abeef cattle 650 $apastures 650 $arotational grazing 650 $aTanzania 650 $aComportamento Animal 650 $aPanicum Maximum 650 $aPastagem 653 $aAnimal behaviour 653 $aBites 653 $aBocado 653 $aBovino de corte 653 $aMassai 653 $aMombaca 653 $aPastejo rotacionado 700 1 $aNASCIMENTO JUNIOR, D. do 700 1 $aEUCLIDES, V. P. B. 700 1 $aREGAZZI, A. J. 700 1 $aALMEIDA, R. G. de 700 1 $aFONSECA, D. M. da 773 $tIn: REUNIAO ANUAL DA SOCIEDADE BRASILEIRA DE ZOOTECNIA, 37., 2000, Vicosa. Resumos dos trabalhos apresentados. Anais. Vicosa: SBZ, 2000.
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Embrapa Gado de Corte (CNPGC) |
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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
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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.
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