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Registros recuperados : 43 | |
6. | | ALVES, G. M.; CRUVINEL, P. E. Big Data environment for agricultural soil analysis from CT digital images. In: INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING ? ICSC, 10., 2016, Laguna Hills, California, USA. Proceedings... Los Alamitos, California, EUA: IEEE, 2016. p. 429-431. Biblioteca(s): Embrapa Instrumentação. |
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12. | | BRITO, A. R.; ALVES, G. M.; CRUVINEL, P. E. Método baseado em grafos para segmentação de sementes oleaginosas em imagens tomográficas de alta resolução. In: SIMPÓSIO NACIONAL DE INSTRUMENTAÇÃO AGROPECUÁRIA, 4., 2019, São Carlos, SP. Ciência, inovação e mercado: anais. São Carlos, SP: Embrapa Instrumentação, 2019. Editores: Paulino Ribeiro Villas-Boas, Maria Alice Martins, Débora Marcondes Bastos Pereira Milori, Ladislau Martin Neto. SIAGRO 2019. 43-47 Biblioteca(s): Embrapa Instrumentação. |
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17. | | ALVES, G. M.; CRUVINEL, P. E.; SOUZA, G. B.; MARANA, A. N.; LEVADA, A. L. M. A new approach for plant phenotyping and image segmentation based on contextual information. In: II Latin-American Conference on Plant Phenotyping and Phenomics for Plant Breeding, 2, 2017, São Carlos, SP. Proceedings... São Carlos: Embrapa Instrumentação, 2017. Biblioteca(s): Embrapa Instrumentação. |
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18. | | PEREIRA, M. F. L.; CRUVINEL, P. E.; ALVES, G. M.; BERALDO, J. M. G. Parallel computational structure and semantics for soil quality analysis based On LoRa and Apache Spark. In: IEEE INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING - ICSC, 14., 2020, California, USA. Proceedings... Laguna Hills, California, USA: IEEE, 2020. 332-336 Biblioteca(s): Embrapa Instrumentação. |
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19. | | PEREIRA, M. F. L.; ALVES, G. M.; BERALDO, J. M. G.; CRUVINEL, P. E. Organização de uma estrutura de processamento paralelo baseado em Apache Spark para gerenciamento de risco agrícola. In: SIMPÓSIO NACIONAL DE INSTRUMENTAÇÃO AGROPECUÁRIA, 4., 2019, São Carlos, SP. Ciência, inovação e mercado: anais. São Carlos, SP: Embrapa Instrumentação, 2019. Editores: Paulino Ribeiro Villas-Boas, Maria Alice Martins, Débora Marcondes Bastos Pereira Milori, Ladislau Martin Neto. SIAGRO 2019. 131-135 Biblioteca(s): Embrapa Instrumentação. |
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20. | | SOUZA, G. B.; ALVES, G. M.; LEVADA, A. L. M.; CRUVINEL, P. E.; MARANA, A. N. A Graph-Based approach for contextual image segmentation. In: Conference on Graphics, Patterns and Images, SIBGRAPI, 29., 2016, São José dos Campos. Proceedings... Los Alamitos: IEEE Computer Society Press, 2016. Biblioteca(s): Embrapa Instrumentação. |
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Registros recuperados : 43 | |
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| Acesso ao texto completo restrito à biblioteca da Embrapa Instrumentação. Para informações adicionais entre em contato com cnpdia.biblioteca@embrapa.br. |
Registro Completo
Biblioteca(s): |
Embrapa Instrumentação. |
Data corrente: |
15/01/2024 |
Data da última atualização: |
15/01/2024 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
ALVES, G. M.; CRUVINEL, P. E. |
Afiliação: |
Federal University of São Carlos (UFSCar); PAULO ESTEVAO CRUVINEL, CNPDIA. |
Título: |
Parallel and distributed processing for high resolution agricultural tomography based on big data. |
Ano de publicação: |
2024 |
Fonte/Imprenta: |
Multimedia Tools and Applications, v. 83, 2024. |
Páginas: |
10115–10146 |
DOI: |
https://doi.org/10.1007/s11042-023-15686-2 |
Idioma: |
Inglês |
Conteúdo: |
In the field of high-resolution tomography, there is currently a notable increase in the volume of tomographic projections and data produced. Such a context has been demanding new computational approaches to the process of reconstruction and processing of the resulting digital images. This paper presents a new approach to meet such a demand, such as optimizing the set of tomographic projections for the reconstruction process, parallelizing algorithm reconstruction, and processing the data in a distributed manner. In this context, a customized method for the high-resolution tomographic reconstruction of agricultural samples has been validated. Hence, tomographic projections with greater amounts of information based on measurements of the spectral density of the projections can be prioritized, and the reconstructive process parallelization using the known filtered back-projection can be considered (i.e., distributed data flow and the use of the Apache Spark environment). For the operation, such an approach based on the big data environment has been organized, that is considering a cluster installed on the Amazon Web Services platform, whose configuration has been defined after the evaluation of the speedup and efficiency metrics. The developed method proved to be useful for carrying out high-resolution tomography analyses of large quantities of agricultural samples, based on the paradigms of precision agriculture for gains in sustainability and competitiveness of the production process. MenosIn the field of high-resolution tomography, there is currently a notable increase in the volume of tomographic projections and data produced. Such a context has been demanding new computational approaches to the process of reconstruction and processing of the resulting digital images. This paper presents a new approach to meet such a demand, such as optimizing the set of tomographic projections for the reconstruction process, parallelizing algorithm reconstruction, and processing the data in a distributed manner. In this context, a customized method for the high-resolution tomographic reconstruction of agricultural samples has been validated. Hence, tomographic projections with greater amounts of information based on measurements of the spectral density of the projections can be prioritized, and the reconstructive process parallelization using the known filtered back-projection can be considered (i.e., distributed data flow and the use of the Apache Spark environment). For the operation, such an approach based on the big data environment has been organized, that is considering a cluster installed on the Amazon Web Services platform, whose configuration has been defined after the evaluation of the speedup and efficiency metrics. The developed method proved to be useful for carrying out high-resolution tomography analyses of large quantities of agricultural samples, based on the paradigms of precision agriculture for gains in sustainability and competitiveness of the productio... Mostrar Tudo |
Palavras-Chave: |
Big data; Image processing; Tomographic image reconstruction; Tomographic selection projections; · Precision agriculture. |
Categoria do assunto: |
-- |
Marc: |
LEADER 02224naa a2200217 a 4500 001 2160861 005 2024-01-15 008 2024 bl uuuu u00u1 u #d 024 7 $ahttps://doi.org/10.1007/s11042-023-15686-2$2DOI 100 1 $aALVES, G. M. 245 $aParallel and distributed processing for high resolution agricultural tomography based on big data.$h[electronic resource] 260 $c2024 300 $a10115–10146 520 $aIn the field of high-resolution tomography, there is currently a notable increase in the volume of tomographic projections and data produced. Such a context has been demanding new computational approaches to the process of reconstruction and processing of the resulting digital images. This paper presents a new approach to meet such a demand, such as optimizing the set of tomographic projections for the reconstruction process, parallelizing algorithm reconstruction, and processing the data in a distributed manner. In this context, a customized method for the high-resolution tomographic reconstruction of agricultural samples has been validated. Hence, tomographic projections with greater amounts of information based on measurements of the spectral density of the projections can be prioritized, and the reconstructive process parallelization using the known filtered back-projection can be considered (i.e., distributed data flow and the use of the Apache Spark environment). For the operation, such an approach based on the big data environment has been organized, that is considering a cluster installed on the Amazon Web Services platform, whose configuration has been defined after the evaluation of the speedup and efficiency metrics. The developed method proved to be useful for carrying out high-resolution tomography analyses of large quantities of agricultural samples, based on the paradigms of precision agriculture for gains in sustainability and competitiveness of the production process. 653 $aBig data 653 $aImage processing 653 $aTomographic image reconstruction 653 $aTomographic selection projections 653 $a· Precision agriculture 700 1 $aCRUVINEL, P. E. 773 $tMultimedia Tools and Applications$gv. 83, 2024.
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