| |
|
|
 | 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: |
11/03/2016 |
|
Data da última atualização: |
24/05/2023 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Autoria: |
BARBEDO, J. G. A. |
|
Afiliação: |
JAYME GARCIA ARNAL BARBEDO, CNPTIA. |
|
Título: |
A review on the main challenges in automatic plant disease identification based on visible range images. |
|
Ano de publicação: |
2016 |
|
Fonte/Imprenta: |
Biosystems Engineering, London, v. 144, p. 52-60, Apr. 2016. |
|
DOI: |
https://doi.org/10.1016/j.biosystemseng.2016.01.017 |
|
Idioma: |
Inglês |
|
Conteúdo: |
The problem associated with automatic plant disease identification using visible range images has received considerable attention in the last two decades, however the techniques proposed so far are usually limited in their scope and dependent on ideal capture conditions in order to work properly. This apparent lack of significant advancements may be partially explained by some difficult challenges posed by the subject: presence of complex backgrounds that cannot be easily separated from the region of interest (usually leaf and stem), boundaries of the symptoms often are not well defined, uncontrolled capture conditions may present characteristics that make the image analysis more difficult, certain diseases produce symptoms with a wide range of characteristics, the symptoms produced by different diseases may be very similar, and they may be present simultaneously. This paper provides an analysis of each one of those challenges, emphasizing both the problems that they may cause and how they may have potentially affected the techniques proposed in the past. Some possible solutions capable of overcoming at least some of those challenges are proposed. |
|
Palavras-Chave: |
Automatic identification; Imagem digital; Processamento de imagens digitais; Visible symptoms. |
|
Thesagro: |
Doença de planta; Sintoma. |
|
Thesaurus Nal: |
Digital images; Image analysis; Plant diseases and disorders; Signs and symptoms (plants). |
|
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
|
Marc: |
LEADER 02017naa a2200253 a 4500
001 2040545
005 2023-05-24
008 2016 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.biosystemseng.2016.01.017$2DOI
100 1 $aBARBEDO, J. G. A.
245 $aA review on the main challenges in automatic plant disease identification based on visible range images.$h[electronic resource]
260 $c2016
520 $aThe problem associated with automatic plant disease identification using visible range images has received considerable attention in the last two decades, however the techniques proposed so far are usually limited in their scope and dependent on ideal capture conditions in order to work properly. This apparent lack of significant advancements may be partially explained by some difficult challenges posed by the subject: presence of complex backgrounds that cannot be easily separated from the region of interest (usually leaf and stem), boundaries of the symptoms often are not well defined, uncontrolled capture conditions may present characteristics that make the image analysis more difficult, certain diseases produce symptoms with a wide range of characteristics, the symptoms produced by different diseases may be very similar, and they may be present simultaneously. This paper provides an analysis of each one of those challenges, emphasizing both the problems that they may cause and how they may have potentially affected the techniques proposed in the past. Some possible solutions capable of overcoming at least some of those challenges are proposed.
650 $aDigital images
650 $aImage analysis
650 $aPlant diseases and disorders
650 $aSigns and symptoms (plants)
650 $aDoença de planta
650 $aSintoma
653 $aAutomatic identification
653 $aImagem digital
653 $aProcessamento de imagens digitais
653 $aVisible symptoms
773 $tBiosystems Engineering, London$gv. 144, p. 52-60, Apr. 2016.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
|
Registro Completo
|
Biblioteca(s): |
Embrapa Pecuária Sudeste. |
|
Data corrente: |
10/03/2020 |
|
Data da última atualização: |
20/04/2020 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Circulação/Nível: |
A - 1 |
|
Autoria: |
GIGLIOTI, R.; GUTMANIS, G.; KATIKI, L. M.; OKINO, C. H.; OLIVEIRA, M. C. de S.; VERCESI FILHO, A. E. |
|
Afiliação: |
Rodrigo Giglioti, Instituto de Zootecnia; Gunta Gutmanis, Instituto de Zootecnia; Luciana Morita Katiki, Instituto de Zootecnia; CINTIA HIROMI OKINO, CPPSE; MARCIA CRISTINA DE SENA OLIVEIRA, CPPSE; Anibal Eugênio Vercesi Filho, Instituto de Zootecnia. |
|
Título: |
New high-sensitive rhAmp method for A1 allele detection in A2 milk samples. |
|
Ano de publicação: |
2020 |
|
Fonte/Imprenta: |
Food Chemistry, v. 313, 2020. 126167. |
|
Páginas: |
7 p. |
|
ISSN: |
0308-8146 |
|
DOI: |
https://doi.org/10.1016/j.foodchem.2020.126167 |
|
Idioma: |
Inglês |
|
Conteúdo: |
Cows milk may contain two types of - casein: A1 and A2. A1 digestion is associated with the release of - casomorphine-7 peptide, which can cause adverse gastrointestinal effects. Two methods high-resolution melting (HRM) and rhAmp® SNP genotyping - were developed to identify the - casein gene (CSN2) A1 and A2 alleles directly in milk. DNA milk samples from 45 animals were examined and 10 samples were also sequenced to confirm the accuracy of the assays. The analytical sensitivities of both strategies for A1 allele identification were evaluated by testing decreasing dilutions of A1 allele DNA copies (500 - 5 copies) in the A2 sample. The limits of detection for A1 in A2 samples were 10% (100 copies) and 2% (10 copies) for HRM and rhAmp, respectively. Both techniques were specific, differentiating between A1 and A2 alleles. However, we recommend rhAmp genotyping testing over HRM because of its enhanced sensitivity for A1. |
|
Palavras-Chave: |
B casein; HRM; RhAmp. |
|
Thesaurus NAL: |
Alleles; Genotyping. |
|
Categoria do assunto: |
G Melhoramento Genético |
|
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/212129/1/NewHighSensitive.pdf
|
|
Marc: |
LEADER 01688naa a2200277 a 4500
001 2121088
005 2020-04-20
008 2020 bl uuuu u00u1 u #d
022 $a0308-8146
024 7 $ahttps://doi.org/10.1016/j.foodchem.2020.126167$2DOI
100 1 $aGIGLIOTI, R.
245 $aNew high-sensitive rhAmp method for A1 allele detection in A2 milk samples.$h[electronic resource]
260 $c2020
300 $a7 p.
520 $aCows milk may contain two types of - casein: A1 and A2. A1 digestion is associated with the release of - casomorphine-7 peptide, which can cause adverse gastrointestinal effects. Two methods high-resolution melting (HRM) and rhAmp® SNP genotyping - were developed to identify the - casein gene (CSN2) A1 and A2 alleles directly in milk. DNA milk samples from 45 animals were examined and 10 samples were also sequenced to confirm the accuracy of the assays. The analytical sensitivities of both strategies for A1 allele identification were evaluated by testing decreasing dilutions of A1 allele DNA copies (500 - 5 copies) in the A2 sample. The limits of detection for A1 in A2 samples were 10% (100 copies) and 2% (10 copies) for HRM and rhAmp, respectively. Both techniques were specific, differentiating between A1 and A2 alleles. However, we recommend rhAmp genotyping testing over HRM because of its enhanced sensitivity for A1.
650 $aAlleles
650 $aGenotyping
653 $aB casein
653 $aHRM
653 $aRhAmp
700 1 $aGUTMANIS, G.
700 1 $aKATIKI, L. M.
700 1 $aOKINO, C. H.
700 1 $aOLIVEIRA, M. C. de S.
700 1 $aVERCESI FILHO, A. E.
773 $tFood Chemistry$gv. 313, 2020. 126167.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Pecuária Sudeste (CPPSE) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
| Nenhum registro encontrado para a expressão de busca informada. |
|
|
