| |
|
|
|
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 |
|
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 |
URL |
Voltar
|
|
|
 | Acesso ao texto completo restrito à biblioteca da Embrapa Agroindústria de Alimentos. Para informações adicionais entre em contato com ctaa.biblioteca@embrapa.br. |
Registro Completo
|
Biblioteca(s): |
Embrapa Agroindústria de Alimentos; Embrapa Instrumentação. |
|
Data corrente: |
18/01/2021 |
|
Data da última atualização: |
25/05/2022 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Circulação/Nível: |
A - 2 |
|
Autoria: |
LIMA, E. M. B.; MIDDEA, A.; NEUMANN, R.; THIRÉ, R. M. da S. M.; PEREIRA, J. F.; FREITAS, S. C. de; STEPHAN, M. P.; LIMA, A. M.; MINGUITA, A. P. da S.; MATTOS, M. da C.; TEIXEIRA, A. da S.; PEREIRA, I. C. S.; SANTOS, N. R. R. dos; MARCONCINI, J. M.; OLIVEIRA, R. N.; CORREA, A. C. |
|
Afiliação: |
EDLA MARIA BEZERRA LIMA, CTAA; Antonieta Middea, CETEM; Reiner Neumann, CETEM; Rossana Mara da Silva Moreira Thiré, UFRJ; Jéssica Fernandes Pereira; SIDINEA CORDEIRO DE FREITAS, CTAA; MARILIA PENTEADO STEPHAN, CTAA; Aline Muniz Lima; ADRIANA PAULA DA SILVA MINGUITA, CTAA; MARIANA DA COSTA MATTOS, CTAA; ALESSANDRA DA SILVA TEIXEIRA, CTAA; Ingrid Cristina Soares Pereira; Natália Rodrigues Rojas dos Santos; José Manoel Marconcini; Renata Nunes Oliveira, UFRRJ; Ana Carolina Correa, UFSCar. |
|
Título: |
Biocomposites of PLA and Mango Seed Waste: Potential Material for Food Packaging and a Technological Alternative to Reduce Environmental Impact. |
|
Ano de publicação: |
2021 |
|
Fonte/Imprenta: |
Starch, v. 73, n. 5/6, e2000118, 2021. |
|
DOI: |
https://doi.org/10.1002/star.202000118 |
|
Idioma: |
Inglês |
|
Conteúdo: |
Mango seeds from agro-industry represents an environmental problem due to the amounts of by-products produced. Conversely, poly (lactic acid) (PLA) is a potential green alternative to conventional plastics. The goal of this study aimed to develop a biocomposite based on PLA and mango's by?product for rigid packaging. Six biocomposites were obtained by extrusion/injection processing using formulations with PLA as a matrix and up to 20% by weight of mango seed's by-products (integument or/and kernel). The materials were characterized by chemical analysis; helium pycnometry; particle size distribution; scanning electron microscopy/energy dispersive X-Ray (SEM/EDX), X-Ray diffraction (XRD); Fourier transform infrared spectroscopy (FTIR); thermal gravimetric analysis/differential thermogravimetry (TGA/DTG); differential scanning calorimetry (DSC), and mechanical analysis. Fourier Transform Infrared Spectroscopy (FTIR) bands and DSC transitions related to starch were higher in the kernel, while more cellulose bands were found in the integument. Kernel presented thermal degradation in the biocomposites, specifically the sample PLA+20 wt% kernel. For the other compositions, it was possible to observe that they could keep their morphology. Significant improvements in both mechanical and barrier properties were found in the formulation with 20 wt% integument (an increase of up to 38% in elastic modulus). Therefore, this study suggests that biocomposites developed from PLA / Integument / Kernel have potential as a new biomaterial for rigid food packaging systems. MenosMango seeds from agro-industry represents an environmental problem due to the amounts of by-products produced. Conversely, poly (lactic acid) (PLA) is a potential green alternative to conventional plastics. The goal of this study aimed to develop a biocomposite based on PLA and mango's by?product for rigid packaging. Six biocomposites were obtained by extrusion/injection processing using formulations with PLA as a matrix and up to 20% by weight of mango seed's by-products (integument or/and kernel). The materials were characterized by chemical analysis; helium pycnometry; particle size distribution; scanning electron microscopy/energy dispersive X-Ray (SEM/EDX), X-Ray diffraction (XRD); Fourier transform infrared spectroscopy (FTIR); thermal gravimetric analysis/differential thermogravimetry (TGA/DTG); differential scanning calorimetry (DSC), and mechanical analysis. Fourier Transform Infrared Spectroscopy (FTIR) bands and DSC transitions related to starch were higher in the kernel, while more cellulose bands were found in the integument. Kernel presented thermal degradation in the biocomposites, specifically the sample PLA+20 wt% kernel. For the other compositions, it was possible to observe that they could keep their morphology. Significant improvements in both mechanical and barrier properties were found in the formulation with 20 wt% integument (an increase of up to 38% in elastic modulus). Therefore, this study suggests that biocomposites developed from PLA / Integument... Mostrar Tudo |
|
Palavras-Chave: |
Biomaterials; Mango; PLA. |
|
Thesaurus NAL: |
Extrusion. |
|
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
|
Marc: |
LEADER 02656naa a2200361 a 4500
001 2129433
005 2022-05-25
008 2021 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1002/star.202000118$2DOI
100 1 $aLIMA, E. M. B.
245 $aBiocomposites of PLA and Mango Seed Waste$bPotential Material for Food Packaging and a Technological Alternative to Reduce Environmental Impact.$h[electronic resource]
260 $c2021
520 $aMango seeds from agro-industry represents an environmental problem due to the amounts of by-products produced. Conversely, poly (lactic acid) (PLA) is a potential green alternative to conventional plastics. The goal of this study aimed to develop a biocomposite based on PLA and mango's by?product for rigid packaging. Six biocomposites were obtained by extrusion/injection processing using formulations with PLA as a matrix and up to 20% by weight of mango seed's by-products (integument or/and kernel). The materials were characterized by chemical analysis; helium pycnometry; particle size distribution; scanning electron microscopy/energy dispersive X-Ray (SEM/EDX), X-Ray diffraction (XRD); Fourier transform infrared spectroscopy (FTIR); thermal gravimetric analysis/differential thermogravimetry (TGA/DTG); differential scanning calorimetry (DSC), and mechanical analysis. Fourier Transform Infrared Spectroscopy (FTIR) bands and DSC transitions related to starch were higher in the kernel, while more cellulose bands were found in the integument. Kernel presented thermal degradation in the biocomposites, specifically the sample PLA+20 wt% kernel. For the other compositions, it was possible to observe that they could keep their morphology. Significant improvements in both mechanical and barrier properties were found in the formulation with 20 wt% integument (an increase of up to 38% in elastic modulus). Therefore, this study suggests that biocomposites developed from PLA / Integument / Kernel have potential as a new biomaterial for rigid food packaging systems.
650 $aExtrusion
653 $aBiomaterials
653 $aMango
653 $aPLA
700 1 $aMIDDEA, A.
700 1 $aNEUMANN, R.
700 1 $aTHIRÉ, R. M. da S. M.
700 1 $aPEREIRA, J. F.
700 1 $aFREITAS, S. C. de
700 1 $aSTEPHAN, M. P.
700 1 $aLIMA, A. M.
700 1 $aMINGUITA, A. P. da S.
700 1 $aMATTOS, M. da C.
700 1 $aTEIXEIRA, A. da S.
700 1 $aPEREIRA, I. C. S.
700 1 $aSANTOS, N. R. R. dos
700 1 $aMARCONCINI, J. M.
700 1 $aOLIVEIRA, R. N.
700 1 $aCORREA, A. C.
773 $tStarch$gv. 73, n. 5/6, e2000118, 2021.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Agroindústria de Alimentos (CTAA) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
| Expressão de busca inválida. Verifique!!! |
|
|
