| |
|
|
| Registros recuperados : 457 | |
| 47. |  | VILAS BOAS, S. A.; HOHENFELD, C. S.; OLIVEIRA, E. J. de; OLIVEIRA, S. A. S. de. Identificação de genótipos de mandioca com resistência a diferentes gêneros causadores de podridão radicular. In: JORNADA CIENTÍFICA EMBRAPA MANDIOCA E FRUTICULTURA, 8., 2014, Cruz das Almas, Ba. Pesquisa: despertando mentes para a inovação e transformando o futuro : [anais]. Cruz das Almas, BA, Embrapa Mandioca e Fruticultura, 2014.| Biblioteca(s): Embrapa Mandioca e Fruticultura. |
|    |
| Registros recuperados : 457 | |
|
|
|
 | Acesso ao texto completo restrito à biblioteca da Embrapa Mandioca e Fruticultura. Para informações adicionais entre em contato com cnpmf.biblioteca@embrapa.br. |
Registro Completo
|
Biblioteca(s): |
Embrapa Mandioca e Fruticultura. |
|
Data corrente: |
11/05/2020 |
|
Data da última atualização: |
02/06/2020 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Circulação/Nível: |
A - 2 |
|
Autoria: |
FREITAS, E. L.; BRITO, A. C.; OLIVEIRA, S. A. S. de; OLIVEIRA, E. J. de. |
|
Afiliação: |
EMILE LEMOS FREITAS; ANA CARLA BRITO; SAULO ALVES SANTOS DE OLIVEIRA, CNPMF; EDER JORGE DE OLIVEIRA, CNPMF. |
|
Título: |
Early diagnosis of cassava frog skin disease in powdered tissue samples using near-infrared spectroscopy. |
|
Ano de publicação: |
2020 |
|
Fonte/Imprenta: |
European Journal of Plant Pathology, v.156, p.547–558, |
|
ISSN: |
0929-1873 |
|
DOI: |
https://doi.org/10.1007/s10658-019-01904-x |
|
Idioma: |
Português |
|
Conteúdo: |
Cassava frog skin disease (CFSD) causes significant yield losses in cassava (Manihot esculenta Crantz). One issue with control is late diagnosis. The objective of this work was to test near-infrared spectrometry (NIRS) and compare calibration and classification models for early detection of CFSD. NIRS analysis was performed on 238 cassava accessions (120 healthy and 118 with CFSD). Six classification models were used: a support vector machine with a linear kernel (SVM), a Bayesian generalized linear model (BGLM), a parallel random forest (PRANDF), an extreme learning machine (ELM), a high dimensional discriminant analysis (HDDA), and a partial least squares (PLS) model. Their predictive abilities were evaluated based on their accuracy and agreement (based on Cohen’s Kappa coefficient). The models demonstrated high efficiency at distinguishing diseased and healthy accessions, with an overall accuracy >80%. The Cohen’s Kappa coefficient values (0.83–0.98) for four of the models (the SVM, BGLM, PRANDF and PLS) indicated that there was almost perfect agreement between their classification results. The SVM and BGLM models exhibited high accuracy (99.07 and 98.92%, respectively) and reproducibility (both 0.98) in classifying the accessions according to their CFSD status. NIRS is a viable alternative for the detection of CFSD with the advantages of early and accurate detection, high speed and low cost compared to traditional diagnostic methods. |
|
Thesagro: |
Mandioca. |
|
Thesaurus NAL: |
Cassava. |
|
Categoria do assunto: |
-- |
|
Marc: |
LEADER 02118naa a2200205 a 4500
001 2122201
005 2020-06-02
008 2020 bl uuuu u00u1 u #d
022 $a0929-1873
024 7 $ahttps://doi.org/10.1007/s10658-019-01904-x$2DOI
100 1 $aFREITAS, E. L.
245 $aEarly diagnosis of cassava frog skin disease in powdered tissue samples using near-infrared spectroscopy.$h[electronic resource]
260 $c2020
520 $aCassava frog skin disease (CFSD) causes significant yield losses in cassava (Manihot esculenta Crantz). One issue with control is late diagnosis. The objective of this work was to test near-infrared spectrometry (NIRS) and compare calibration and classification models for early detection of CFSD. NIRS analysis was performed on 238 cassava accessions (120 healthy and 118 with CFSD). Six classification models were used: a support vector machine with a linear kernel (SVM), a Bayesian generalized linear model (BGLM), a parallel random forest (PRANDF), an extreme learning machine (ELM), a high dimensional discriminant analysis (HDDA), and a partial least squares (PLS) model. Their predictive abilities were evaluated based on their accuracy and agreement (based on Cohen’s Kappa coefficient). The models demonstrated high efficiency at distinguishing diseased and healthy accessions, with an overall accuracy >80%. The Cohen’s Kappa coefficient values (0.83–0.98) for four of the models (the SVM, BGLM, PRANDF and PLS) indicated that there was almost perfect agreement between their classification results. The SVM and BGLM models exhibited high accuracy (99.07 and 98.92%, respectively) and reproducibility (both 0.98) in classifying the accessions according to their CFSD status. NIRS is a viable alternative for the detection of CFSD with the advantages of early and accurate detection, high speed and low cost compared to traditional diagnostic methods.
650 $aCassava
650 $aMandioca
700 1 $aBRITO, A. C.
700 1 $aOLIVEIRA, S. A. S. de
700 1 $aOLIVEIRA, E. J. de
773 $tEuropean Journal of Plant Pathology$gv.156, p.547–558
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Mandioca e Fruticultura (CNPMF) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
| Nenhum registro encontrado para a expressão de busca informada. |
|
|
