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| 3. |  | QUEIROZ, D. M. de; DIAS, G. P.; MANTOVANI, E. C. Agricultura de precisão na produção de grãos. In: BOREM, A.; GIUDICE, M. P. del; QUEIROZ, D. M. de; MANTOVANI, E. C.; FERREIRA, L. R.; VALLE, F. X. R. do; GOMIDE, R. L. (Ed.). Agricultura de precisão. Viçosa: Universidade Federal de Vicosa, 2000. p. 1-42.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 13. | | ACUNA, R. L. D.; MANTOVANI, E. C.; MARTYN, P. J.; BERTAUX, S.; QUEIROZ, D. M. de. Comparação do coeficiente de tração e da eficiência tratória de um trator agrícola obtidos pela equação de Wismer e Luth e por ensaios de campo. In: CONGRESSO BRASILEIRO DE ENGENHARIA AGRÍCOLA, 24., 1995, Viçosa, MG. Resumos... Viçosa, MG: SBEA: UFV, 1995. p. 380.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 17. | | COUTO, S. M.; MAGALHAES, A. C.; QUEIROZ, D. M. de; BASTO, I. T. Massa especifica aparente e real e porosidade de graos de cafe em funcao do teor de umidade. Revista Brasileira de Engenharia Agricola e Ambiental, Campina Grande,PB, , V.3,n.1, p.61-68, 1999.| Biblioteca(s): Embrapa Algodão. |
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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
|
Biblioteca(s): |
Embrapa Milho e Sorgo. |
|
Data corrente: |
27/01/2006 |
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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: |
SENA JUNIOR, D. G.; PINTO, F. A. C.; QUEIROZ, D. M.; VIANA, P. A. |
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Afiliação: |
PAULO AFONSO VIANA, CNPMS. |
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Título: |
Fall armyworm damaged maize plant identification using digital images. |
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Ano de publicação: |
2003 |
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Fonte/Imprenta: |
Biosystems Engineering, London, v. 85, n. 4, p. 449-454, 2003. |
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DOI: |
10.1016/S1537-5110(03)00098-9 |
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Idioma: |
Inglês |
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Conteúdo: |
The objectives of precision agriculture are profit maximisation, agricultural input rationalisation and environmental damage reduction, by adjusting the agricultural practices to the site demands. The fall armyworm (Spodoptera frugiperda) is one of the most important maize pests in Brazil and the use of insecticide is the main control method. It is believed that site-specific control can be implemented by using a machine vision system. The objective of this work was to develop and evaluate an algorithm at simplified lighting conditions for identifying damaged maize plants by the fall armyworm using digital colour images. Images of damaged and non-damaged maize plants were taken in eight different stages and in three different light intensities. The proposed algorithm had two stages: the processing and the image analysis. During the first stage, the images were processed to create binary images where the leaves were segmented from the other pixels. At the second stage, the images were subdivided into blocks and classified as 'damaged' or 'non-damaged' depending on the number of objects found in each block. The algorithm correctly classified 94.72% of 720 images.. |
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Thesagro: |
Agricultura de Precisão; Praga de planta; Spodoptera frugiperda. |
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Categoria do assunto: |
-- |
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Marc: |
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001 1489192
005 2018-05-28
008 2003 bl uuuu u00u1 u #d
024 7 $a10.1016/S1537-5110(03)00098-9$2DOI
100 1 $aSENA JUNIOR, D. G.
245 $aFall armyworm damaged maize plant identification using digital images.$h[electronic resource]
260 $c2003
520 $aThe objectives of precision agriculture are profit maximisation, agricultural input rationalisation and environmental damage reduction, by adjusting the agricultural practices to the site demands. The fall armyworm (Spodoptera frugiperda) is one of the most important maize pests in Brazil and the use of insecticide is the main control method. It is believed that site-specific control can be implemented by using a machine vision system. The objective of this work was to develop and evaluate an algorithm at simplified lighting conditions for identifying damaged maize plants by the fall armyworm using digital colour images. Images of damaged and non-damaged maize plants were taken in eight different stages and in three different light intensities. The proposed algorithm had two stages: the processing and the image analysis. During the first stage, the images were processed to create binary images where the leaves were segmented from the other pixels. At the second stage, the images were subdivided into blocks and classified as 'damaged' or 'non-damaged' depending on the number of objects found in each block. The algorithm correctly classified 94.72% of 720 images..
650 $aAgricultura de Precisão
650 $aPraga de planta
650 $aSpodoptera frugiperda
700 1 $aPINTO, F. A. C.
700 1 $aQUEIROZ, D. M.
700 1 $aVIANA, P. A.
773 $tBiosystems Engineering, London$gv. 85, n. 4, p. 449-454, 2003.
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