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Registro Completo |
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Biblioteca(s): |
Embrapa Semiárido. |
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Data corrente: |
07/11/2012 |
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Data da última atualização: |
15/03/2023 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
OLIVEIRA, J. E. de M.; FERNANDES, M. H. de A.; SOUZA, I. D. de; OLIVEIRA, A. C.; SILVA, J. C. da; PINTO JÚNIOR, E. dos S. |
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Afiliação: |
JOSE EUDES DE MORAIS OLIVEIRA, CPATSA; MARIA HERLÂNDIA DE ARAÚJO FERNANDES; INGRIDE DAYANE DE SOUZA; ANDRÉA COSTA OLIVEIRA; JOCÉLIA GONÇALVES DA SILVA; EZIO DOS SANTOS PINTO JUNIOR. |
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Título: |
Flutuação populacional de Cryptobables gnidiella em videira no sistema de produção integrada. |
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Ano de publicação: |
2012 |
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Fonte/Imprenta: |
In: CONGRESSO BRASILEIRO DE FRUTICULTURA, 22., 2012, Bento Gonçalves. Anais... Bento Gonçalves: SBF, 2012. |
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Descrição Física: |
1 CD-ROM. |
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Idioma: |
Português |
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Conteúdo: |
Este trabalhou foi realizado com o objetivo de estudar e comparar a flutuação populacional de C. gnidiella em diferentes sistemas de produção, convencional e Produção Integrada (PI), em cultivares de uva destinada para elaboração de vinhos. |
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Palavras-Chave: |
Traça-dos-cachos; Uva de vinho; Vale do São Francisco. |
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Thesagro: |
Doença; Praga; Produção Integrada; Uva; Vitis Vinifera. |
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Thesaurus Nal: |
Cryptoblabes gnidiella; Grapes. |
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Categoria do assunto: |
O Insetos e Entomologia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/69659/1/Eudes.pdf
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Marc: |
LEADER 01163nam a2200301 a 4500
001 1939096
005 2023-03-15
008 2012 bl uuuu u00u1 u #d
100 1 $aOLIVEIRA, J. E. de M.
245 $aFlutuação populacional de Cryptobables gnidiella em videira no sistema de produção integrada.
260 $aIn: CONGRESSO BRASILEIRO DE FRUTICULTURA, 22., 2012, Bento Gonçalves. Anais... Bento Gonçalves: SBF$c2012
300 $c1 CD-ROM.
520 $aEste trabalhou foi realizado com o objetivo de estudar e comparar a flutuação populacional de C. gnidiella em diferentes sistemas de produção, convencional e Produção Integrada (PI), em cultivares de uva destinada para elaboração de vinhos.
650 $aCryptoblabes gnidiella
650 $aGrapes
650 $aDoença
650 $aPraga
650 $aProdução Integrada
650 $aUva
650 $aVitis Vinifera
653 $aTraça-dos-cachos
653 $aUva de vinho
653 $aVale do São Francisco
700 1 $aFERNANDES, M. H. de A.
700 1 $aSOUZA, I. D. de
700 1 $aOLIVEIRA, A. C.
700 1 $aSILVA, J. C. da
700 1 $aPINTO JÚNIOR, E. dos S.
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Registro original: |
Embrapa Semiárido (CPATSA) |
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Registro Completo
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Biblioteca(s): |
Embrapa Pecuária Sudeste. |
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Data corrente: |
21/05/2019 |
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Data da última atualização: |
13/03/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
SANTOS, I. G. dos; CRUZ, C. D.; NASCIMENTO, M.; FERREIRA, R. de P. |
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Afiliação: |
Iara Gonçalves dos Santos, UFV; Cosme Damião Cruz, UFV; Moysés Nascimento, UFV; REINALDO DE PAULA FERREIRA, CPPSE. |
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Título: |
Selection index as a priori information for using artificial neural networks to classify alfalfa genotypes. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Genetics and Molecular Research, v. 18, n. 2, gmr18221, 2019. |
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DOI: |
doi.org/10.4238/gmr18221 |
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Idioma: |
Inglês |
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Conteúdo: |
The efficiency of a selection index generally depends on the quality of the variance matrixes, which demands controlled experiments. Using Artificial Neural Networks (ANNs) trained from a selection index is advantageous for selecting genotypes since an ANN has the capacity to classify genotypes in an automated way. We propose the use of ANNs for the selection of alfalfa genotypes, based on a selection index. Data were collected from 77 alfalfa genotypes evaluated based on nine traits from four cuttings. The traits were divided into forage yield and nutritive value groups. In order for the ANNs to learn the classification pattern, the Tai index was used, which allows secondary traits to be included in the index to improve the gains of the main traits. An index was established for each group of traits, and based on the index scores the genotypes were subdivided into four classes (optimal, good, medium, and bad). After testing different topologies, ANNs were established for each index, according to the apparent error rates. The chosen ANNs were efficient in classifying the genotypes since the highest apparent error rate reached 15%, meaning that the ANNs efficiently captured the data pattern. Considering the ANN classification for both groups of traits, there was a high degree of agreement with the classification obtained from the Tai index, as expected. Even in the cuttings where the ANNs presented the worst performance, their potential to classify alfalfa genotypes was clear, because the wrong classifications were placed in groups close to the correct ones. This ensured that the best genotypes did not run the risk of being discarded, since they would not classified in the group of bad genotypes. The ANNs that were developed have good potential for use in alfalfa breeding programs. MenosThe efficiency of a selection index generally depends on the quality of the variance matrixes, which demands controlled experiments. Using Artificial Neural Networks (ANNs) trained from a selection index is advantageous for selecting genotypes since an ANN has the capacity to classify genotypes in an automated way. We propose the use of ANNs for the selection of alfalfa genotypes, based on a selection index. Data were collected from 77 alfalfa genotypes evaluated based on nine traits from four cuttings. The traits were divided into forage yield and nutritive value groups. In order for the ANNs to learn the classification pattern, the Tai index was used, which allows secondary traits to be included in the index to improve the gains of the main traits. An index was established for each group of traits, and based on the index scores the genotypes were subdivided into four classes (optimal, good, medium, and bad). After testing different topologies, ANNs were established for each index, according to the apparent error rates. The chosen ANNs were efficient in classifying the genotypes since the highest apparent error rate reached 15%, meaning that the ANNs efficiently captured the data pattern. Considering the ANN classification for both groups of traits, there was a high degree of agreement with the classification obtained from the Tai index, as expected. Even in the cuttings where the ANNs presented the worst performance, their potential to classify alfalfa genotypes was clear,... Mostrar Tudo |
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Palavras-Chave: |
Computational intelligence; Tai index. |
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Thesagro: |
Medicago Sativa. |
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Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/197607/1/gmr18221-selection-index-priori-information-using.pdf
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Marc: |
LEADER 02470naa a2200205 a 4500
001 2109207
005 2023-03-13
008 2019 bl uuuu u00u1 u #d
024 7 $adoi.org/10.4238/gmr18221$2DOI
100 1 $aSANTOS, I. G. dos
245 $aSelection index as a priori information for using artificial neural networks to classify alfalfa genotypes.$h[electronic resource]
260 $c2019
520 $aThe efficiency of a selection index generally depends on the quality of the variance matrixes, which demands controlled experiments. Using Artificial Neural Networks (ANNs) trained from a selection index is advantageous for selecting genotypes since an ANN has the capacity to classify genotypes in an automated way. We propose the use of ANNs for the selection of alfalfa genotypes, based on a selection index. Data were collected from 77 alfalfa genotypes evaluated based on nine traits from four cuttings. The traits were divided into forage yield and nutritive value groups. In order for the ANNs to learn the classification pattern, the Tai index was used, which allows secondary traits to be included in the index to improve the gains of the main traits. An index was established for each group of traits, and based on the index scores the genotypes were subdivided into four classes (optimal, good, medium, and bad). After testing different topologies, ANNs were established for each index, according to the apparent error rates. The chosen ANNs were efficient in classifying the genotypes since the highest apparent error rate reached 15%, meaning that the ANNs efficiently captured the data pattern. Considering the ANN classification for both groups of traits, there was a high degree of agreement with the classification obtained from the Tai index, as expected. Even in the cuttings where the ANNs presented the worst performance, their potential to classify alfalfa genotypes was clear, because the wrong classifications were placed in groups close to the correct ones. This ensured that the best genotypes did not run the risk of being discarded, since they would not classified in the group of bad genotypes. The ANNs that were developed have good potential for use in alfalfa breeding programs.
650 $aMedicago Sativa
653 $aComputational intelligence
653 $aTai index
700 1 $aCRUZ, C. D.
700 1 $aNASCIMENTO, M.
700 1 $aFERREIRA, R. de P.
773 $tGenetics and Molecular Research$gv. 18, n. 2, gmr18221, 2019.
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Embrapa Pecuária Sudeste (CPPSE) |
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