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Registro Completo |
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Biblioteca(s): |
Epagri-Sede. |
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Data corrente: |
08/08/2017 |
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Data da última atualização: |
08/08/2017 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
ALVES, D. P.; TOMAZ, R. S.; LAURINDO, B. S.; LAURINDO, R. D. F.; SILVA, F. F.; CRUZ, C. D.; NICK, C.; SILVA, D. J. H. |
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Título: |
Artifcial neural network for prediction of the area under the disease progress curve of tomato late blight. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
Scientia Agricola, Piracicaba, SP, v. 74, n. 1, p. 51-59, 2017. |
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Idioma: |
Inglês |
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Conteúdo: |
Artifcial neural networks (ANN) are computational models inspired by the neural
systems of living beings capable of learning from examples and using them to solve problems
such as non-linear prediction, and pattern recognition, in addition to several other applications.
In this study, ANN were used to predict the value of the area under the disease progress curve
(AUDPC) for the tomato late blight pathosystem. The AUDPC is widely used by epidemiologic
studies of polycyclic diseases, especially those regarding quantitative resistance of genotypes.
However, a series of six evaluations over time is necessary to obtain the fnal area value for this
pathosystem. This study aimed to investigate the utilization of ANN to construct an AUDPC in the
tomato late blight pathosystem, using a reduced number of severity evaluations. For this, four independent experiments were performed giving a total of 1836 plants infected with Phytophthora
infestans pathogen. They were assessed every three days, comprised six opportunities and
AUDPC calculations were performed by the conventional method. After the ANN were created it
was possible to predict the AUDPC with correlations of 0.97 and 0.84 when compared to conventional methods, using 50 % and 67 % of the genotype evaluations, respectively. When using
the ANN created in an experiment to predict the AUDPC of the other experiments the average
correlation was 0.94, with two evaluations, 0.96, with three evaluations, between the predicted
values of the ANN and they were observed in six evaluations. We present in this study a new
paradigm for the use of AUDPC information in tomato experiments faced with P. infestans. This
new proposed paradigm might be adapted to different pathosystems MenosArtifcial neural networks (ANN) are computational models inspired by the neural
systems of living beings capable of learning from examples and using them to solve problems
such as non-linear prediction, and pattern recognition, in addition to several other applications.
In this study, ANN were used to predict the value of the area under the disease progress curve
(AUDPC) for the tomato late blight pathosystem. The AUDPC is widely used by epidemiologic
studies of polycyclic diseases, especially those regarding quantitative resistance of genotypes.
However, a series of six evaluations over time is necessary to obtain the fnal area value for this
pathosystem. This study aimed to investigate the utilization of ANN to construct an AUDPC in the
tomato late blight pathosystem, using a reduced number of severity evaluations. For this, four independent experiments were performed giving a total of 1836 plants infected with Phytophthora
infestans pathogen. They were assessed every three days, comprised six opportunities and
AUDPC calculations were performed by the conventional method. After the ANN were created it
was possible to predict the AUDPC with correlations of 0.97 and 0.84 when compared to conventional methods, using 50 % and 67 % of the genotype evaluations, respectively. When using
the ANN created in an experiment to predict the AUDPC of the other experiments the average
correlation was 0.94, with two evaluations, 0.96, with three evaluations, between the predicted
values of... Mostrar Tudo |
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Palavras-Chave: |
ANN; artifcial intelligence; AUDPC; Phytophthora infestans; plant breeding. |
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Categoria do assunto: |
G Melhoramento Genético |
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Marc: |
LEADER 02517naa a2200265 a 4500
001 1126489
005 2017-08-08
008 2017 bl uuuu u00u1 u #d
100 1 $aALVES, D. P.
245 $aArtifcial neural network for prediction of the area under the disease progress curve of tomato late blight.$h[electronic resource]
260 $c2017
520 $aArtifcial neural networks (ANN) are computational models inspired by the neural systems of living beings capable of learning from examples and using them to solve problems such as non-linear prediction, and pattern recognition, in addition to several other applications. In this study, ANN were used to predict the value of the area under the disease progress curve (AUDPC) for the tomato late blight pathosystem. The AUDPC is widely used by epidemiologic studies of polycyclic diseases, especially those regarding quantitative resistance of genotypes. However, a series of six evaluations over time is necessary to obtain the fnal area value for this pathosystem. This study aimed to investigate the utilization of ANN to construct an AUDPC in the tomato late blight pathosystem, using a reduced number of severity evaluations. For this, four independent experiments were performed giving a total of 1836 plants infected with Phytophthora infestans pathogen. They were assessed every three days, comprised six opportunities and AUDPC calculations were performed by the conventional method. After the ANN were created it was possible to predict the AUDPC with correlations of 0.97 and 0.84 when compared to conventional methods, using 50 % and 67 % of the genotype evaluations, respectively. When using the ANN created in an experiment to predict the AUDPC of the other experiments the average correlation was 0.94, with two evaluations, 0.96, with three evaluations, between the predicted values of the ANN and they were observed in six evaluations. We present in this study a new paradigm for the use of AUDPC information in tomato experiments faced with P. infestans. This new proposed paradigm might be adapted to different pathosystems
653 $aANN
653 $aartifcial intelligence
653 $aAUDPC
653 $aPhytophthora infestans
653 $aplant breeding
700 1 $aTOMAZ, R. S.
700 1 $aLAURINDO, B. S.
700 1 $aLAURINDO, R. D. F.
700 1 $aSILVA, F. F.
700 1 $aCRUZ, C. D.
700 1 $aNICK, C.
700 1 $aSILVA, D. J. H.
773 $tScientia Agricola, Piracicaba, SP$gv. 74, n. 1, p. 51-59, 2017.
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| Registros recuperados : 3 | |
| 1. |  | ALVES, D. P.; ARAÚJO, E. R.; WAMSER, G. H.; GONÇALVES, P. A. S.; MARINHO, C. D.; TOMAZ, R. S. Field performance and screening for resistance to Peronospora destructor of 46 onion cultivars in Brazil. Australasian Plant Disease Notes , Berlin, v. 13, p. 1-6, 2018.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: Internacional - B |
| Biblioteca(s): Epagri-Sede. |
|   |
| 2. | | ALVES, D. P.; TOMAZ, R. S.; LAURINDO, B. S.; LAURINDO, R. D. F.; SILVA, F. F.; CRUZ, C. D.; NICK, C.; SILVA, D. J. H. Artifcial neural network for prediction of the area under the disease progress curve of tomato late blight. Scientia Agricola, Piracicaba, SP, v. 74, n. 1, p. 51-59, 2017.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: Nacional - B |
| Biblioteca(s): Epagri-Sede. |
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| 3. | | FRIAS, Y. A.; FERREIRA, E. A.; CRUZ, V. H.; ALVES, D. P.; PRADO, E. P.; LIMA, R. C.; CRUZ, C. D.; TOMAZ, R. S. Early selection efficiency for recommendation of Eucalyptus sp. International Journal for Innovation Education and Research, Bangladesh, v. 8, n. 3, p. 304-315, 2020.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: Internacional - A |
| Biblioteca(s): Epagri-Sede. |
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| Registros recuperados : 3 | |
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