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| Registros recuperados: 29 | |
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| BARBEDO, J. G. A.; KOENIGKAN, L. V.; SANTOS, T. T.; SANTOS, P. M.. |
| Abstract: Unmanned aerial vehicles (UAVs) are being increasingly viewed as valuable tools to aid the management of farms. This kind of technology can be particularly useful in the context of extensive cattle farming, as production areas tend to be expansive and animals tend to be more loosely monitored. With the advent of deep learning, and convolutional neural networks (CNNs) in particular, extracting relevant information from aerial images has become more effective. Despite the technological advancements in drone, imaging and machine learning technologies, the application of UAVs for cattle monitoring is far from being thoroughly studied, with many research gaps still remaining. In this context, the objectives of this study were threefold: (1) to... |
| Tipo: Artigo em periódico indexado (ALICE) |
Palavras-chave: Veículo aéreo não tripulado; Redes neurais; Drone; Aprendizado profundo; Convolutional neural networks; Deep learning; Canchim breed; Nelore breed; Gado de Corte; Gado Canchim; Gado Nelore; Cattle; Unmanned aerial vehicles. |
| Ano: 2019 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1116449 |
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| BARBEDO, J. G. A.; KOENIGKAN, L. V.; HALFELD-VIEIRA, B. de A.; COSTA, R. V. da; NECHET, K. de L.; GODOY, C. V.; LOBO JUNIOR, M.; PATRÍCIO, F. R. A.; TALAMINI, V.; CHITARRA, L. G.; OLIVEIRA, S. A. S. de; ISHIDA, A. K. N.; FERNANDES, J. M. C.; SANTOS, T. T.; CAVALCANTI, F. R.; TERAO, D.; ANGELOTTI, F.. |
| Over the last few years, considerable effort has been spent by Embrapa in the construction of a plant disease database representative enough for the development of effective methods for automatic plant disease detection and recognition. In October of 2016, this database, called PDDB, had 2326 images of 171 diseases and other disorders affecting 21 plant species. PDDB size, although considerable, is not enough to allow the use of powerful techniques such as deep learning. In order to increase its size, each image was subdivided according to certain criteria, increasing the number of images to 46,513. Both the original (PDDB) and subdivided (XDB)databases are now being made freely available for academic research purposes, thus supporting new studies and... |
| Tipo: Artigo em periódico indexado (ALICE) |
Palavras-chave: Patologia vegetal; Banco de dados; Aprendizagem profunda; Processamento de imagem; Deep learning; Doença de Planta; Plant pathology; Plant diseases and disorders; Databases; Image analysis. |
| Ano: 2018 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1094883 |
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| BARBEDO, J. G. A.; KOENIGKAN, L. V.; HALFELD-VIEIRA, B. de A.; COSTA, R. V. da; NECHET, K. de L.; GODOY, C. V.; LOBO JUNIOR, M.; PATRÍCIO, F. R. A.; TALAMINI, V.; CHITARRA, L. G.; OLIVEIRA, S. A. S. de; ISHIDA, A. K. N.; FERNANDES, J. M. C.; SANTOS, T. T.; CAVALCANTI, F. R.; TERAO, D.; ANGELOTTI, F.. |
| Over the last few years, considerable effort has been spent by Embrapa in the construction of a plant disease database representative enough for the development of effective methods for automatic plant disease detection and recognition. In October of 2016, this database, called PDDB, had 2326 images of 171 diseases and other disorders affecting 21 plant species. PDDB size, although considerable, is not enough to allow the use of powerful techniques such as deep learning. In order to increase its size, each imag e was subdivided according to certain criteria, increasing the number of images to 46,513. Both the original (PDDB) and subdivided (XDB)databases are now being made freely available for academic research purposes, thus supporting new studies and... |
| Tipo: Artigo em periódico indexado (ALICE) |
Palavras-chave: Patologia vegetal; Banco de dados; Aprendizagem profunda; Imagem em processamento; Doença de Planta; Plant pathology; Plant diseases and disorders; Databases. |
| Ano: 2018 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1093910 |
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| BARBEDO, J. G. A.; KOENIGKAN, L. V.; HALFELD-VIEIRA, B. de A.; COSTA, R. V. da; NECHET, K. de L.; GODOY, C. V.; LOBO JUNIOR, M.; PATRÍCIO, F. R. A.; TALAMINI, V.; CHITARRA, L. G.; OLIVEIRA, S. A. S. de; ISHIDA, A. K. N.; FERNANDES, J. M. C.; SANTOS, T. T.; CAVALCANTI, F. R.; TERAO, D.; ANGELOTTI, F.. |
| Over the last few years, considerable effort has been spent by Embrapa in the construction of a plant disease database representative enough for the development of effective methods for automatic plant disease detection and recognition. In October of 2016, this database, called PDDB, had 2326 images of 171 diseases and other disorders affecting 21 plant species. PDDB size, although considerable, is not enough to allow the use of powerful techniques such as deep learning. In order to increase its size, each image was subdivided according to certain criteria, increasing the number of images to 46,513. Both the original (PDDB) and subdivided (XDB)databases are now being made freely available for academic research purposes, thus supporting new studies and... |
| Tipo: Artigo em periódico indexado (ALICE) |
Palavras-chave: Patologia vegetal; Banco de dados; Aprendizagem profunda; Imagem em processamento; Doença de Planta; Plant pathology; Plant diseases and disorders; Databases. |
| Ano: 2018 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1097219 |
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| BARBEDO, J. G. A.; KOENIGKAN, L. V.; HALFELD-VIEIRA, B. de A.; COSTA, R. V. da; NECHET, K. de L.; GODOY, C. V.; LOBO JUNIOR, M.; PATRÍCIO, F. R. A.; TALAMINI, V.; CHITARRA, L. G.; OLIVEIRA, S. A. S. de; ISHIDA, A. K. N.; FERNANDES, J. M. C.; SANTOS, T. T.; CAVALCANTI, F. R.; TERAO, D.; ANGELOTTI, F.. |
| Over the last few years, considerable effort has been spent by Embrapa in the construction of a plant disease database representative enough for the development of effective methods for automatic plant disease detection and recognition. In October of 2016, this database, called PDDB, had 2326 images of 171 diseases and other disorders affecting 21 plant species. PDDB size, although considerable, is not enough to allow the use of powerful techniques such as deep learning. In order to increase its size, each imag e was subdivided according to certain criteria, increasing the number of images to 46,513. Both the original (PDDB) and subdivided (XDB)databases are now being made freely available for academic research purposes, thus supporting new studies and... |
| Tipo: Artigo em periódico indexado (ALICE) |
Palavras-chave: Patologia vegetal; Banco de dados; Aprendizagem profunda; Imagem em processamento; Doença de Planta; Plant pathology; Plant diseases and disorders; Databases. |
| Ano: 2018 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1093944 |
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| NOUHRA, B. B. C.; KOENIGKAN, L. V.. |
| Visando à melhoria no planejamento e na execução das ações de TI na Empresa Brasileira de Pesquisa Agropecuária (Embrapa) de maneira a consolidar a importância estratégica desta área, foi criado o I Plano Diretor de Tecnologia da Informação (PDTI). O PDTI I, feito para os anos de 2010 e 2011, definiu as práticas de desenvolvimento de software da Embrapa por meio do Modelo Corporativo de Processos de Software da Embrapa (MCPSE) e a padronização das arquiteturas tecnológicas. O MCPSE consiste na determinação de processos de software e no apoio ao seu desenvolvimento, possuindo as seguintes fases: planejamento, construção (versão) e encerramento (COELHO et al., 2014). Ele foi criado a partir da definição dos seguintes elementos: atividades, papéis, produtos... |
| Tipo: Resumo em anais de congresso (ALICE) |
Palavras-chave: Engenharia de software; Desenvolvimento de sistemas; Gerenciamento de projetos; Documentação de software. |
| Ano: 2014 |
URL: http://www.alice.cnptia.embrapa.br/handle/doc/1009748 |
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| BARBEDO, J. G. A.; KOENIGKAN, L. V.; SANTOS, P. M.; RIBEIRO, A. R. B.. |
| Abstract: The management of livestock in extensive production systems may be challenging, especially in large areas. Using Unmanned Aerial Vehicles (UAVs) to collect images from the area of interest is quickly becoming a viable alternative, but suitable algorithms for extraction of relevant information from the images are still rare. This article proposes a method for counting cattle which combines a deep learning model for rough animal location, color space manipulation to increase contrast between animals and background, mathematical morphology to isolate the animals and infer the number of individuals in clustered groups, and image matching to take into account image overlap. Using Nelore and Canchim breeds as a case study, the proposed approach yields... |
| Tipo: Artigo em periódico indexado (ALICE) |
Palavras-chave: Redes neurais; Rede neural convolucional; Veículo aéreo não tripulado; Canchim breed; Nelore breed; Convolutional neural networks; Mathematical morphology; Deep learning mode; Gado de Corte; Gado Nelore; Gado Canchim; Unmanned aerial vehicles; Neural networks. |
| Ano: 2020 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1121664 |
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| ROJAS, T. C. G.; MALUTA, R. P.; KOENIGKAN, L. V.; SILVEIRA, W. D. da. |
| Avian pathogenic Escherichia coli (APEC) infections are responsible for significant losses in the poultry industry worldwide. A zoonotic risk has been attributed to APEC strains because they present similarities to extraintestinal pathogenic E. coli (ExPEC) associated with illness in humans, mainly urinary tract infections and neonatal meningitis. Here, we present in silico analyses with pathogenic E. coli genome sequences, including recently available APEC genomes. The phylogenetic tree, based on multi-locus sequence typing (MLST) of seven housekeeping genes, revealed high diversity in the allelic composition. Nevertheless, despite this diversity, the phylogenetic tree was able to cluster the different pathotypes together. An in silico virulence gene... |
| Tipo: Artigo em periódico indexado (ALICE) |
Palavras-chave: Árvore filogenética; Avian pathogenic Escherichia coli; Tipagem de sequência multilocus; Análise in silico; Genes associados à virulência; Multi-locus Sequence Typing; Phylogenetic tree; Virulence genes; Escherichia coli. |
| Ano: 2014 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1077504 |
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| Registros recuperados: 29 | |
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