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Registro Completo |
Biblioteca(s): |
Embrapa Acre. |
Data corrente: |
19/01/2016 |
Data da última atualização: |
16/11/2023 |
Tipo da produção científica: |
Resumo em Anais de Congresso |
Autoria: |
MOTA, B. B.; RIBEIRO, T. O.; AZEVEDO, H. N. de; SANTOS, I. A. dos; ASSIS, G. M. L. de. |
Afiliação: |
Bárbara Barbosa Mota, bolsista PIBIC/CNPq; Tiago Oliveira Ribeiro, Colaborador; Hermeson Nunes de Azevedo, Colaborador; Ingrid Alencar dos Santos, Colaboradora; GISELLE MARIANO LESSA DE ASSIS, CPAF-AC. |
Título: |
Caracterização morfológica da população F2 da família V1 de amendoim forrageiro. |
Ano de publicação: |
2015 |
Fonte/Imprenta: |
In: CONGRESSO REGIONAL DE PESQUISA DO ESTADO DO ACRE; SEMINÁRIO DE INICIAÇÃO CIENTÍFICA DA UFAC, 24., 2015, Rio Branco. Anais... Rio Branco: CNPq; Ufac; Embrapa; Fapac; Ieval, 2015. |
Páginas: |
2 p. |
Idioma: |
Português |
Conteúdo: |
O amendoim forrageiro (Arachis pintoi) é uma planta perene, pertencente à família Fabaceae. Possui crescimento rasteiro, estolonífero, atingindo de 20 a 50 cm de altura. Originário do Brasil, possui boa adaptação ao clima tropical e subtropical e vem destacando-se na formação de pastagens no consórcio com gramíneas para alimentação animal. Porém, são poucos os materiais disponíveis no mercado, sendo necessário o desenvolvimento de novas cultivares por meio de programas de melhoramento genético. Este trabalho teve como objetivo verificar, através da caracterização morfológica, a variação existente na geração F2 da família V1, oriunda do cruzamento entre dois genótipos superiores e divergentes de A. pintoi. |
Palavras-Chave: |
Amendoim forrageiro; Caracterização morfológica; Família V1; Melhoramento genético; População F2. |
Thesagro: |
Leguminosa Forrageira; Pastagem. |
Thesaurus Nal: |
Arachis pintoi. |
Categoria do assunto: |
G Melhoramento Genético |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/137292/1/25883.pdf
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Marc: |
LEADER 01665nam a2200265 a 4500 001 2034403 005 2023-11-16 008 2015 bl uuuu u00u1 u #d 100 1 $aMOTA, B. B. 245 $aCaracterização morfológica da população F2 da família V1 de amendoim forrageiro.$h[electronic resource] 260 $aIn: CONGRESSO REGIONAL DE PESQUISA DO ESTADO DO ACRE; SEMINÁRIO DE INICIAÇÃO CIENTÍFICA DA UFAC, 24., 2015, Rio Branco. Anais... Rio Branco: CNPq; Ufac; Embrapa; Fapac; Ieval$c2015 300 $a2 p. 520 $aO amendoim forrageiro (Arachis pintoi) é uma planta perene, pertencente à família Fabaceae. Possui crescimento rasteiro, estolonífero, atingindo de 20 a 50 cm de altura. Originário do Brasil, possui boa adaptação ao clima tropical e subtropical e vem destacando-se na formação de pastagens no consórcio com gramíneas para alimentação animal. Porém, são poucos os materiais disponíveis no mercado, sendo necessário o desenvolvimento de novas cultivares por meio de programas de melhoramento genético. Este trabalho teve como objetivo verificar, através da caracterização morfológica, a variação existente na geração F2 da família V1, oriunda do cruzamento entre dois genótipos superiores e divergentes de A. pintoi. 650 $aArachis pintoi 650 $aLeguminosa Forrageira 650 $aPastagem 653 $aAmendoim forrageiro 653 $aCaracterização morfológica 653 $aFamília V1 653 $aMelhoramento genético 653 $aPopulação F2 700 1 $aRIBEIRO, T. O. 700 1 $aAZEVEDO, H. N. de 700 1 $aSANTOS, I. A. dos 700 1 $aASSIS, G. M. L. de
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Embrapa Acre (CPAF-AC) |
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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. |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
Data corrente: |
05/08/2008 |
Data da última atualização: |
07/03/2023 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
Internacional - A |
Autoria: |
MEYER, G. E.; CAMARGO NETO, J. |
Afiliação: |
GEORGE E. MEYER, University of Nebraska; JOAO CAMARGO NETO, CNPTIA. |
Título: |
Verification of color vegetation indices for automated crop imaging applications. |
Ano de publicação: |
2008 |
Fonte/Imprenta: |
Computers and Electronics in Agriculture, v. 63, n. 2, p. 282-293, Oct. 2008. |
DOI: |
10.1016/j.compag.2008.03.009 |
Idioma: |
Inglês |
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 |
Palavras-Chave: |
Color images; Índice de vegetação; Machine vision; Plant; Residue; Resíduos; Soils. |
Thesagro: |
Planta; Solo. |
Thesaurus NAL: |
Computer vision; Vegetation index. |
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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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