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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
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
05/08/2008 |
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Data da última atualização: |
07/03/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
Internacional - A |
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Autoria: |
MEYER, G. E.; CAMARGO NETO, J. |
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Afiliação: |
GEORGE E. MEYER, University of Nebraska; JOAO CAMARGO NETO, CNPTIA. |
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Título: |
Verification of color vegetation indices for automated crop imaging applications. |
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Ano de publicação: |
2008 |
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Fonte/Imprenta: |
Computers and Electronics in Agriculture, v. 63, n. 2, p. 282-293, Oct. 2008. |
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DOI: |
10.1016/j.compag.2008.03.009 |
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Idioma: |
Inglês |
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Conteúdo: |
An accurate vegetation index is required to identify plant biomass versus soil and residue backgrounds for automated remote sensing and machine vision applications, plant ecological assessments, precision crop management, and weed control. An improved vegetation index, Excess Green minus Excess red (ExG - ExR) was compared to the commonly used Excess Green (ExG), and the normalized difference (NDI) indices. The latter two indices used an Otsu threshold value to convert the index near-binary a full binary image. The indices were tested with digital color image sets of single plants grown and taken in a greenhouse and field images of young soybean plants. Vegetative index accuracies using a separation quality factor algorithm were compared to hand-extracted plant region of interest. A quality factor of one represented a near perfect binary match of the computer extratect plant target compared to the hand-extracted plant region. The ExG - ExR index had the highest quality factor of 0.88 + 0.12 for all three weeks and soil-residue backgrouds for the greenhouse set. The ExG + Otsu and NDI - Otsu indices had similar but lower quality factors of 0.53 +_ 0.39 and 0.54 +_ 0.33 for the same sets, respectively. Field images of young soybeans against bare soil gave quality factors for bothExG - ExR and ExG + Otsu around 0.88 +_ 0.07. The quality factor of NDI + Otsu using the same field images was 0.25 +_ 0.08. The ExG - ExR index has a fixed, built-in zero threshold, so it does not need Otsu or any user select threshold value. The ExG - ExR index worked especially well for flesh wheat straw backgrounds, where it was generally 55% more accurate than the ExG + Otsu and NDI+ Otsu indices. Once a binary plant region of interest is identifield with a vegetation index, other advance image processing operations may be applied, such as identification of plant species for strategic weed control. MenosAn accurate vegetation index is required to identify plant biomass versus soil and residue backgrounds for automated remote sensing and machine vision applications, plant ecological assessments, precision crop management, and weed control. An improved vegetation index, Excess Green minus Excess red (ExG - ExR) was compared to the commonly used Excess Green (ExG), and the normalized difference (NDI) indices. The latter two indices used an Otsu threshold value to convert the index near-binary a full binary image. The indices were tested with digital color image sets of single plants grown and taken in a greenhouse and field images of young soybean plants. Vegetative index accuracies using a separation quality factor algorithm were compared to hand-extracted plant region of interest. A quality factor of one represented a near perfect binary match of the computer extratect plant target compared to the hand-extracted plant region. The ExG - ExR index had the highest quality factor of 0.88 + 0.12 for all three weeks and soil-residue backgrouds for the greenhouse set. The ExG + Otsu and NDI - Otsu indices had similar but lower quality factors of 0.53 +_ 0.39 and 0.54 +_ 0.33 for the same sets, respectively. Field images of young soybeans against bare soil gave quality factors for bothExG - ExR and ExG + Otsu around 0.88 +_ 0.07. The quality factor of NDI + Otsu using the same field images was 0.25 +_ 0.08. The ExG - ExR index has a fixed, built-in zero threshold, so it does not nee... Mostrar Tudo |
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Palavras-Chave: |
Color images; Índice de vegetação; Machine vision; Plant; Residue; Resíduos; Soils. |
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Thesagro: |
Planta; Solo. |
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Thesaurus NAL: |
Computer vision; Vegetation index. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02697naa a2200277 a 4500
001 1006684
005 2023-03-07
008 2008 bl uuuu u00u1 u #d
024 7 $a10.1016/j.compag.2008.03.009$2DOI
100 1 $aMEYER, G. E.
245 $aVerification of color vegetation indices for automated crop imaging applications.$h[electronic resource]
260 $c2008
520 $aAn accurate vegetation index is required to identify plant biomass versus soil and residue backgrounds for automated remote sensing and machine vision applications, plant ecological assessments, precision crop management, and weed control. An improved vegetation index, Excess Green minus Excess red (ExG - ExR) was compared to the commonly used Excess Green (ExG), and the normalized difference (NDI) indices. The latter two indices used an Otsu threshold value to convert the index near-binary a full binary image. The indices were tested with digital color image sets of single plants grown and taken in a greenhouse and field images of young soybean plants. Vegetative index accuracies using a separation quality factor algorithm were compared to hand-extracted plant region of interest. A quality factor of one represented a near perfect binary match of the computer extratect plant target compared to the hand-extracted plant region. The ExG - ExR index had the highest quality factor of 0.88 + 0.12 for all three weeks and soil-residue backgrouds for the greenhouse set. The ExG + Otsu and NDI - Otsu indices had similar but lower quality factors of 0.53 +_ 0.39 and 0.54 +_ 0.33 for the same sets, respectively. Field images of young soybeans against bare soil gave quality factors for bothExG - ExR and ExG + Otsu around 0.88 +_ 0.07. The quality factor of NDI + Otsu using the same field images was 0.25 +_ 0.08. The ExG - ExR index has a fixed, built-in zero threshold, so it does not need Otsu or any user select threshold value. The ExG - ExR index worked especially well for flesh wheat straw backgrounds, where it was generally 55% more accurate than the ExG + Otsu and NDI+ Otsu indices. Once a binary plant region of interest is identifield with a vegetation index, other advance image processing operations may be applied, such as identification of plant species for strategic weed control.
650 $aComputer vision
650 $aVegetation index
650 $aPlanta
650 $aSolo
653 $aColor images
653 $aÍndice de vegetação
653 $aMachine vision
653 $aPlant
653 $aResidue
653 $aResíduos
653 $aSoils
700 1 $aCAMARGO NETO, J.
773 $tComputers and Electronics in Agriculture$gv. 63, n. 2, p. 282-293, Oct. 2008.
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