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| 8. |  | MEYER, G. E.; CAMARGO NETO, J.; JONES, D. D.; HINDMAN, T. W. Intensified fuzzy clusters for classifying plant, soil, and residue regions of interest from color images. Computers and Electronics in Agriculture, New York, v. 42, p. 161-180, 2004.| Biblioteca(s): Embrapa Agricultura Digital. |
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| Registros recuperados : 8 | |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
Registro Completo
|
Biblioteca(s): |
Embrapa Agricultura Digital. |
|
Data corrente: |
18/04/2005 |
|
Data da última atualização: |
17/01/2020 |
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Autoria: |
CAMARGO NETO, J.; MEYER, G. E.; JONES, D. D. |
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Afiliação: |
JOAO CAMARGO NETO, CNPTIA; GEORGE E. MEYER; DAVID D. JONES. |
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Título: |
Advances in color image segmentation of plants for weed control. |
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Ano de publicação: |
2004 |
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Fonte/Imprenta: |
In: ASAE/CSAE ANNUAL INTERNATIONAL MEETING, 2004, Ottawa. Dynamic partnerships for an environmentally safe and healthy world. St. Joseph, MI: American Society of Agricultural Engineers, 2004. |
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Páginas: |
p. 1-16. |
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Idioma: |
Inglês |
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Notas: |
Paper number: 043060. |
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Conteúdo: |
Abstract. A new and improved unsupervised plant segmentation index (ExGExR) is introduced. ExGRxR performance was compared with the traditional excess green segmentation method (ExG), using Otsu's method for automatic threshold. Segmentation accuracies were measured with a quality index, using a ground truth target plant mask, hand-generated for each image. Statistical results showed that there were significant differences in performance with bare soil backgrounds during the first week for pigweed and velvetleaf. For corn stalk residue, there were significant differences for all species during the first week and also significant differences during the second week for pigweed and velvetleaf. Using wheat straw residue background, there were significant differences for all species all three weeks. In general, the algorithm performed better than the ExG index for segmenting plants from soil-residues background. |
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Palavras-Chave: |
Análise de imagem; Color index; Índice de cor; Segmentação; Segmentation. |
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Thesagro: |
Planta; Resíduo. |
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Thesaurus NAL: |
Image analysis; No-tillage. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 01783nam a2200265 a 4500
001 1009026
005 2020-01-17
008 2004 bl uuuu u00u1 u #d
100 1 $aCAMARGO NETO, J.
245 $aAdvances in color image segmentation of plants for weed control.$h[electronic resource]
260 $aIn: ASAE/CSAE ANNUAL INTERNATIONAL MEETING, 2004, Ottawa. Dynamic partnerships for an environmentally safe and healthy world. St. Joseph, MI: American Society of Agricultural Engineers$c2004
300 $ap. 1-16.
500 $aPaper number: 043060.
520 $aAbstract. A new and improved unsupervised plant segmentation index (ExGExR) is introduced. ExGRxR performance was compared with the traditional excess green segmentation method (ExG), using Otsu's method for automatic threshold. Segmentation accuracies were measured with a quality index, using a ground truth target plant mask, hand-generated for each image. Statistical results showed that there were significant differences in performance with bare soil backgrounds during the first week for pigweed and velvetleaf. For corn stalk residue, there were significant differences for all species during the first week and also significant differences during the second week for pigweed and velvetleaf. Using wheat straw residue background, there were significant differences for all species all three weeks. In general, the algorithm performed better than the ExG index for segmenting plants from soil-residues background.
650 $aImage analysis
650 $aNo-tillage
650 $aPlanta
650 $aResíduo
653 $aAnálise de imagem
653 $aColor index
653 $aÍndice de cor
653 $aSegmentação
653 $aSegmentation
700 1 $aMEYER, G. E.
700 1 $aJONES, D. D.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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