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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: |
07/02/2006 |
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Data da última atualização: |
17/01/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
-- - -- |
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Autoria: |
CAMARGO NETO, J.; MEYER, G. E.; JONES, D. D.; SAMAL, A. K. |
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Afiliação: |
JOAO CAMARGO NETO, CNPTIA; GEORGE E. MEYER, University of Nebraska; DAVID D. JONES, University of Nebraska; ASHOK K. SAMAL, University of Nebraska. |
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Título: |
Plant species identification using Eliptic Fourier leaf shape analysis. |
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Ano de publicação: |
2006 |
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Fonte/Imprenta: |
Computers and Electronics in Agriculture, v. 50, n. 2, p. 121-134, 2006. |
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DOI: |
https://doi.org/10.1016/j.compag.2005.09.004 |
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Idioma: |
Inglês |
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Conteúdo: |
Elliptic Fourier (EF) and discriminant analyses were used to identify young soybean (Glycine max (L.) merrill), sunflower (Helianthus pumilus), redroot pigweed (Amaranthus retroflexus) and velvetleaf (Abutilon theophrasti Medicus) plants, based on leaf shape. Chain encoded, Elliptic Fourier harmonic functions were generated based on leaf boundary. A complexity index of the leaf shape was computed using the variation between consecutive EF functions. Principle component analysis was used to select the Fourier coefficients with the best discriminatory power. Canonical discriminant analysis was used to develop species identification models based on leaf shapes extracted from plant color images during the second and third weeks after germination. The classification results showed that plant species during the third week were successfully identified with an average of correct classification rate of 89.4%. The discriminant model correctly classified on average: 77.9% of redroot pigweed, 93.8% of sunflower, 89.4% of velvetleaf and 96.5% of soybean. Using all of the leaves extracted from the second and the third weeks, the overall classification accuracy was 89.2%. The discriminant model correctly classified 76.4% of redroot pigweed, 93.6% of sunflower, 81.6% of velvetleaf, 91.5% of soybean leaf extracted from trifoliolate and 90.9% of soybean unifoliolate leaves. The Elliptic Fourier shape feature analysis could be an important and accurate tool for weed species identification and mapping. MenosElliptic Fourier (EF) and discriminant analyses were used to identify young soybean (Glycine max (L.) merrill), sunflower (Helianthus pumilus), redroot pigweed (Amaranthus retroflexus) and velvetleaf (Abutilon theophrasti Medicus) plants, based on leaf shape. Chain encoded, Elliptic Fourier harmonic functions were generated based on leaf boundary. A complexity index of the leaf shape was computed using the variation between consecutive EF functions. Principle component analysis was used to select the Fourier coefficients with the best discriminatory power. Canonical discriminant analysis was used to develop species identification models based on leaf shapes extracted from plant color images during the second and third weeks after germination. The classification results showed that plant species during the third week were successfully identified with an average of correct classification rate of 89.4%. The discriminant model correctly classified on average: 77.9% of redroot pigweed, 93.8% of sunflower, 89.4% of velvetleaf and 96.5% of soybean. Using all of the leaves extracted from the second and the third weeks, the overall classification accuracy was 89.2%. The discriminant model correctly classified 76.4% of redroot pigweed, 93.6% of sunflower, 81.6% of velvetleaf, 91.5% of soybean leaf extracted from trifoliolate and 90.9% of soybean unifoliolate leaves. The Elliptic Fourier shape feature analysis could be an important and accurate tool for weed species identification and ... Mostrar Tudo |
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Palavras-Chave: |
Análise de forma de folha; Elliptic Fourier; Espécies de planta; Machine vision; Reconhecimento padrão; Shape features. |
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Thesaurus NAL: |
Computer vision; discriminant analysis; Leaves. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02359naa a2200277 a 4500
001 1009265
005 2020-01-17
008 2006 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.compag.2005.09.004$2DOI
100 1 $aCAMARGO NETO, J.
245 $aPlant species identification using Eliptic Fourier leaf shape analysis.$h[electronic resource]
260 $c2006
520 $aElliptic Fourier (EF) and discriminant analyses were used to identify young soybean (Glycine max (L.) merrill), sunflower (Helianthus pumilus), redroot pigweed (Amaranthus retroflexus) and velvetleaf (Abutilon theophrasti Medicus) plants, based on leaf shape. Chain encoded, Elliptic Fourier harmonic functions were generated based on leaf boundary. A complexity index of the leaf shape was computed using the variation between consecutive EF functions. Principle component analysis was used to select the Fourier coefficients with the best discriminatory power. Canonical discriminant analysis was used to develop species identification models based on leaf shapes extracted from plant color images during the second and third weeks after germination. The classification results showed that plant species during the third week were successfully identified with an average of correct classification rate of 89.4%. The discriminant model correctly classified on average: 77.9% of redroot pigweed, 93.8% of sunflower, 89.4% of velvetleaf and 96.5% of soybean. Using all of the leaves extracted from the second and the third weeks, the overall classification accuracy was 89.2%. The discriminant model correctly classified 76.4% of redroot pigweed, 93.6% of sunflower, 81.6% of velvetleaf, 91.5% of soybean leaf extracted from trifoliolate and 90.9% of soybean unifoliolate leaves. The Elliptic Fourier shape feature analysis could be an important and accurate tool for weed species identification and mapping.
650 $aComputer vision
650 $adiscriminant analysis
650 $aLeaves
653 $aAnálise de forma de folha
653 $aElliptic Fourier
653 $aEspécies de planta
653 $aMachine vision
653 $aReconhecimento padrão
653 $aShape features
700 1 $aMEYER, G. E.
700 1 $aJONES, D. D.
700 1 $aSAMAL, A. K.
773 $tComputers and Electronics in Agriculture$gv. 50, n. 2, p. 121-134, 2006.
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