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Registro Completo |
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Biblioteca(s): |
Embrapa Algodão; Embrapa Semiárido. |
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Data corrente: |
08/11/2016 |
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Data da última atualização: |
02/03/2017 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
MELO, E. B. S. de; LIMA, L. M. de; FERNANDES JUNIOR, P. I.; AIDAR, S. de T.; FREIRE, M. A. O.; FREIRE, R. M. M.; SANTOS, R. C. dos. |
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Afiliação: |
EMANUELLE BARROS SOBRAL de MELO, UNIVERSIDADE ESTAUDAL DA PARAÍBA; LIZIANE MARIA DE LIMA, CNPA; PAULO IVAN FERNANDES JUNIOR, CPATSA; SAULO DE TARSO AIDAR, CPATSA; MARIA ALINE OLIVEIRA FREIRE; ROSA MARIA MENDES FREIRE, CNPA; ROSEANE CAVALCANTI DOS SANTOS, CNPA. |
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Título: |
Nodulation, gas exchanges and production of peanut cultivated with Bradyrhizobium in soils with different textures. |
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Ano de publicação: |
2016 |
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Fonte/Imprenta: |
Comunicata scientiae, v, 7, n.2, p. 160-166, 2016. |
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ISSN: |
2177-5133 |
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Idioma: |
Inglês |
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Conteúdo: |
Nitrogen fertilization from biological source is an uncommon practice for peanut growers due to the limited results, mainly in environments with water restriction. In this study, the response of a commercial Bradyrhizobium was evaluated on the nodulation and production of peanuts grown in sandy and medium textured soils. Two experiments using different soils were carried out in the field during the dry season, in Campina Grande, Paraíba State, Brazil. Three peanut genotypes were submitted to the following treatments: 1-no nitrogen fertilization (control), 2- chemical fertilization (ammonium sulfate) and 3- inoculation with Bradyrhizobium [commercial strain BR 1405 (SEMIA 6144)]. A completely randomized 3x3 factorial design was adopted with five repetitions for both experiments. The evaluates variables were: height of the main stem, number of nodes/plant, root length, root dry weight, weight of pods/plant and number of pods/plant. In addition, gas exchanges were estimated using IRGA apparatus. Both genotypes (BRS Havana and L7 Bege) were benefited in relation to production due to an inoculation with SEMIA 6144. No physiological response was verified in genotypes or N-treatments to gas exchange, excepting for the Ci/Ca ratio in the medium textured soil experiment. BRS Havana showed low Ci/Ca ratio in Bradyrhizobium treatment, indicating that SEMIA 6144 improved the plants photosynthetic efficiency. |
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Palavras-Chave: |
Biological nitrogen fixation; Fertilização do solo; Fixação biológica de nitrogênio; Soil fertilization; Troca gasosa. |
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Thesagro: |
Amendoim; Arachis Hypogaea; Fertilidade do solo; Solo. |
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Thesaurus Nal: |
Bradyrhizobium; Gas exchange; Peanuts. |
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Categoria do assunto: |
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X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/156893/1/Nodulation-gas-exchanges.pdf
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/149643/1/Paulo-Ivan-2016.pdf
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Marc: |
LEADER 02464naa a2200349 a 4500
001 2065835
005 2017-03-02
008 2016 bl uuuu u00u1 u #d
022 $a2177-5133
100 1 $aMELO, E. B. S. de
245 $aNodulation, gas exchanges and production of peanut cultivated with Bradyrhizobium in soils with different textures.$h[electronic resource]
260 $c2016
520 $aNitrogen fertilization from biological source is an uncommon practice for peanut growers due to the limited results, mainly in environments with water restriction. In this study, the response of a commercial Bradyrhizobium was evaluated on the nodulation and production of peanuts grown in sandy and medium textured soils. Two experiments using different soils were carried out in the field during the dry season, in Campina Grande, Paraíba State, Brazil. Three peanut genotypes were submitted to the following treatments: 1-no nitrogen fertilization (control), 2- chemical fertilization (ammonium sulfate) and 3- inoculation with Bradyrhizobium [commercial strain BR 1405 (SEMIA 6144)]. A completely randomized 3x3 factorial design was adopted with five repetitions for both experiments. The evaluates variables were: height of the main stem, number of nodes/plant, root length, root dry weight, weight of pods/plant and number of pods/plant. In addition, gas exchanges were estimated using IRGA apparatus. Both genotypes (BRS Havana and L7 Bege) were benefited in relation to production due to an inoculation with SEMIA 6144. No physiological response was verified in genotypes or N-treatments to gas exchange, excepting for the Ci/Ca ratio in the medium textured soil experiment. BRS Havana showed low Ci/Ca ratio in Bradyrhizobium treatment, indicating that SEMIA 6144 improved the plants photosynthetic efficiency.
650 $aBradyrhizobium
650 $aGas exchange
650 $aPeanuts
650 $aAmendoim
650 $aArachis Hypogaea
650 $aFertilidade do solo
650 $aSolo
653 $aBiological nitrogen fixation
653 $aFertilização do solo
653 $aFixação biológica de nitrogênio
653 $aSoil fertilization
653 $aTroca gasosa
700 1 $aLIMA, L. M. de
700 1 $aFERNANDES JUNIOR, P. I.
700 1 $aAIDAR, S. de T.
700 1 $aFREIRE, M. A. O.
700 1 $aFREIRE, R. M. M.
700 1 $aSANTOS, R. C. dos
773 $tComunicata scientiae, v, 7$gn.2, p. 160-166, 2016.
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Registro original: |
Embrapa Algodão (CNPA) |
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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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Embrapa Agricultura Digital (CNPTIA) |
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