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| Registros recuperados : 36 | |
| 9. |  | PINTO, F. A.; FEITOSA, W. S.; SOUZA, V. S. de; SOARES, I.; SILVA, L. A. da. Condutividade elétrica da solução nutritiva para o cultivo de alface em substrato. Horticultura Brasileira, Brasília, v. 21, n. 2, jul. 2003. Suplemento 2. Trabalho apresentado no 43º Congresso Brasileiro de Olericultura, 2003. Publicado também como resumo em: Horticultura Brasileira, Brasília, v. 21, n. 2, p. 292, jul. 2003. Suplemento 1.| Biblioteca(s): Embrapa Hortaliças. |
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| 15. | | ZANDONADI, R. S.; PINTO, F. A. C.; SENA JUNIOR, D. G; QUEIROZ, D. M.; VIANA, P. A.; MANTOVANI, E. C. Identification of lesser cornstalk borer-attacked maize plants using infrared images Biosystems Engineering, London, v. 91, n. 4, p. 433-439, 2005.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 16. | | OLIVEIRA, R. A. de; FREITAS, W. da S.; GALVAO, J. C. C.; PINTO, F. A.; CECON, P. R. Efeito da aplicação de águas residuárias de suinocultura nas características nutricionais do milho. Revista Brasileira de Milho e Sorgo, Sete Lagoas, v. 3, n. 3, p. 357-369, 2004.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 17. | | FARIA, M. R.; GUIMARÃES, R. A.; PEREIRA, P. F.; PINTO, F. A. M. F.; BETTIOL, W.; MEDEIROS, H. V. Contribuição de resíduos orgânicos nas propriedades do solo e sobrevivência de Stenocarpella em colmos de milho. In: REUNIÃO BRASILEIRA DE CONTROLE BIOLÓGICO DE DOENÇAS DE PLANTAS, 12.; SIMPÓSIO DE MANEJO DE DOENÇAS DE PLANTAS, 16., 2016, Lavras. Integrando técnicas para entregar resultados: resumos... Lavras: NEFIT, 2016. p. 206.| Biblioteca(s): Embrapa Meio Ambiente. |
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| 18. | | PINTO, F. A.; TEIXEIRA, E. W.; GUIMARÃES-CESTARO, L.; MARTINS, M. F.; ALVES, M. L. T. M. F.; MESSAGE, D. Varroa destructor in Africanized honey bees in Brazil: genetic and reproductive profile. Sociobiology, v. 69, n. 1, e7340, 2022.| Biblioteca(s): Embrapa Gado de Leite. |
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| 19. | | CRUZ, E. S.; CARVALHO, D. F.; VARELLA, C. A. A.; SILVA, L. D. B.; SOUZA, W. E. S.; PINTO, F. A. C. Comparação de classificadores de imagens digitais na determinação da cobertura do solo. Engenharia Agrícola, v.28, n.2, p.237-244, abr./jun., 2008. Fascículo em CD| Biblioteca(s): Embrapa Algodão. |
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| 20. | | SANTOS, L. G.; ALVES, M. L. T. M. F.; MESSAGE, D.; PINTO, F. A.; SILVA, M. V. G. B.; TEIXEIRA, E. W. Honey Bee Health in Apiaries in the Vale do Paraíba, São Paulo State, Southeastern Brazil. Sociobiology, v. 61, n. 3, p. 307-312, 2014.| Biblioteca(s): Embrapa Gado de Leite. |
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| Registros recuperados : 36 | |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Milho e Sorgo. Para informações adicionais entre em contato com cnpms.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Milho e Sorgo. |
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Data corrente: |
26/01/2006 |
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Data da última atualização: |
28/05/2018 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
ZANDONADI, R. S.; PINTO, F. A. C.; SENA JUNIOR, D. G; QUEIROZ, D. M.; VIANA, P. A.; MANTOVANI, E. C. |
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Afiliação: |
PAULO AFONSO VIANA, CNPMS; EVANDRO CHARTUNI MANTOVANI, CNPMS. |
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Título: |
Identification of lesser cornstalk borer-attacked maize plants using infrared images |
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Ano de publicação: |
2005 |
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Fonte/Imprenta: |
Biosystems Engineering, London, v. 91, n. 4, p. 433-439, 2005. |
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DOI: |
10.1016/j.biosystemseng.2005.05.002 |
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Idioma: |
Inglês |
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Conteúdo: |
The lesser cornstalk borer (Elasmopalpus lignosellus) is a pest that damages the maize plants in the initial growing phase causing stand reduction that can result in yield decrease. The machine vision system could be an alternative for the development of site-specific management of this pest. The objective of this work was to develop and evaluate a machine vision algorithm for identifying maize plants attacked by lesser cornstalk borer based on colour infrared images. To develop the algorithm, images of 40 maize plants were taken on different days after emergency. The plants were grown in pots, and 25 of them were infested with lesser cornstalk borer larvae and 15 were left healthy. The algorithm had three stages: leaf identification, image block classification, and plant classification. In the leaf identification stage, the plant leaves were segmented by thresholding the normalised difference vegetation index image. For the block classification stage, different neural network architectures and block sizes were tested for identification of non-attacked and attacked plant image blocks. Then, in the plant classification stage, discriminating functions were used to classify the scene as either a healthy or an attacked plant. The algorithm performance was compared with the performance of four human experts by using the Kappa coefficient of agreement. The largest size of image block, 9 by 9 pixels, was chosen because of its less computational exigency and because its performance was not significantly different from the other tested block sizes. The algorithm performance was significantly better than just one human expert. The Kappa coefficients for the algorithm and the three best human experts were 63.0 and 49.7%, respectively. The overall accuracy of the algorithm and the best three human experts was 81.6 and 73.4%, respectively. (c) 2005 Silsoe Research Institute. All rights reserved Published by Elsevier Ltd. MenosThe lesser cornstalk borer (Elasmopalpus lignosellus) is a pest that damages the maize plants in the initial growing phase causing stand reduction that can result in yield decrease. The machine vision system could be an alternative for the development of site-specific management of this pest. The objective of this work was to develop and evaluate a machine vision algorithm for identifying maize plants attacked by lesser cornstalk borer based on colour infrared images. To develop the algorithm, images of 40 maize plants were taken on different days after emergency. The plants were grown in pots, and 25 of them were infested with lesser cornstalk borer larvae and 15 were left healthy. The algorithm had three stages: leaf identification, image block classification, and plant classification. In the leaf identification stage, the plant leaves were segmented by thresholding the normalised difference vegetation index image. For the block classification stage, different neural network architectures and block sizes were tested for identification of non-attacked and attacked plant image blocks. Then, in the plant classification stage, discriminating functions were used to classify the scene as either a healthy or an attacked plant. The algorithm performance was compared with the performance of four human experts by using the Kappa coefficient of agreement. The largest size of image block, 9 by 9 pixels, was chosen because of its less computational exigency and because its performance ... Mostrar Tudo |
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Thesagro: |
Milho; Praga de planta; Zea mays. |
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Categoria do assunto: |
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Marc: |
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024 7 $a10.1016/j.biosystemseng.2005.05.002$2DOI
100 1 $aZANDONADI, R. S.
245 $aIdentification of lesser cornstalk borer-attacked maize plants using infrared images$h[electronic resource]
260 $c2005
520 $aThe lesser cornstalk borer (Elasmopalpus lignosellus) is a pest that damages the maize plants in the initial growing phase causing stand reduction that can result in yield decrease. The machine vision system could be an alternative for the development of site-specific management of this pest. The objective of this work was to develop and evaluate a machine vision algorithm for identifying maize plants attacked by lesser cornstalk borer based on colour infrared images. To develop the algorithm, images of 40 maize plants were taken on different days after emergency. The plants were grown in pots, and 25 of them were infested with lesser cornstalk borer larvae and 15 were left healthy. The algorithm had three stages: leaf identification, image block classification, and plant classification. In the leaf identification stage, the plant leaves were segmented by thresholding the normalised difference vegetation index image. For the block classification stage, different neural network architectures and block sizes were tested for identification of non-attacked and attacked plant image blocks. Then, in the plant classification stage, discriminating functions were used to classify the scene as either a healthy or an attacked plant. The algorithm performance was compared with the performance of four human experts by using the Kappa coefficient of agreement. The largest size of image block, 9 by 9 pixels, was chosen because of its less computational exigency and because its performance was not significantly different from the other tested block sizes. The algorithm performance was significantly better than just one human expert. The Kappa coefficients for the algorithm and the three best human experts were 63.0 and 49.7%, respectively. The overall accuracy of the algorithm and the best three human experts was 81.6 and 73.4%, respectively. (c) 2005 Silsoe Research Institute. All rights reserved Published by Elsevier Ltd.
650 $aMilho
650 $aPraga de planta
650 $aZea mays
700 1 $aPINTO, F. A. C.
700 1 $aSENA JUNIOR, D. G
700 1 $aQUEIROZ, D. M.
700 1 $aVIANA, P. A.
700 1 $aMANTOVANI, E. C.
773 $tBiosystems Engineering, London$gv. 91, n. 4, p. 433-439, 2005.
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