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 | Acesso ao texto completo restrito à biblioteca da Embrapa Instrumentação. Para informações adicionais entre em contato com cnpdia.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Instrumentação. |
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Data corrente: |
02/12/2021 |
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Data da última atualização: |
24/11/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
OLIVEIRA, I. C.; FRANCA, T.; NICOLODELLI, G.; MORAIS, C. P.; MARANGONI, B.; BACCHETTA, G.; MILORI, D. M. B. P.; ALVES, C. Z.; CENA, C. |
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Afiliação: |
DEBORA MARCONDES BASTOS PEREIRA, CNPDIA. |
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Título: |
Fast and accurate discrimination of Brachiaria brizantha (A.Rich.) stapf seeds by molecular spectroscopy and machine learning. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
ACS Agricultural Science & Technology, v. 1, 2021. |
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Páginas: |
443?448 |
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DOI: |
https://doi.org/10.1021/acsagscitech.1c00067 |
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Idioma: |
Inglês |
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Conteúdo: |
: Brachiaria brizantha is the most common forage plant used in cattle pastures. The field plant population is the main parameter for cattle nutrition; it is mainly determined by the genotype and physiological quality of the seed, that is, seed vigor. Seed vigor standard tests are considered time-consuming and laborious. Fourier transformed infrared (FTIR) spectroscopy was used to classify the seed cultivar and vigor from two different genotypes of B. brizantha, namely, Marandu and Paiaguas cultivars. Two ́ batches from each group were used for classification by applying FTIR and machine learning algorithms. The algorithms with a higher overall accuracy in the leave-one-out cross-validation were also validated by an external validation using a dedicated set of samples exclusively separated for this purpose. The results indicate that molecular spectroscopy combined with machine learning analysis presents great potential for the classification of B. brizantha seeds. |
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Palavras-Chave: |
FTIR spectroscopy; Machine learning; Seed vigor. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 01826naa a2200277 a 4500
001 2137033
005 2022-11-24
008 2021 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1021/acsagscitech.1c00067$2DOI
100 1 $aOLIVEIRA, I. C.
245 $aFast and accurate discrimination of Brachiaria brizantha (A.Rich.) stapf seeds by molecular spectroscopy and machine learning.$h[electronic resource]
260 $c2021
300 $a443?448
520 $a: Brachiaria brizantha is the most common forage plant used in cattle pastures. The field plant population is the main parameter for cattle nutrition; it is mainly determined by the genotype and physiological quality of the seed, that is, seed vigor. Seed vigor standard tests are considered time-consuming and laborious. Fourier transformed infrared (FTIR) spectroscopy was used to classify the seed cultivar and vigor from two different genotypes of B. brizantha, namely, Marandu and Paiaguas cultivars. Two ́ batches from each group were used for classification by applying FTIR and machine learning algorithms. The algorithms with a higher overall accuracy in the leave-one-out cross-validation were also validated by an external validation using a dedicated set of samples exclusively separated for this purpose. The results indicate that molecular spectroscopy combined with machine learning analysis presents great potential for the classification of B. brizantha seeds.
653 $aFTIR spectroscopy
653 $aMachine learning
653 $aSeed vigor
700 1 $aFRANCA, T.
700 1 $aNICOLODELLI, G.
700 1 $aMORAIS, C. P.
700 1 $aMARANGONI, B.
700 1 $aBACCHETTA, G.
700 1 $aMILORI, D. M. B. P.
700 1 $aALVES, C. Z.
700 1 $aCENA, C.
773 $tACS Agricultural Science & Technology$gv. 1, 2021.
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| 1. |  | PANTOJA, N.; BEUCHLE, R.; OLIVEIRA, M. V. N. d'; CASSOL, H.; LUI, G. The potential for operational monitoring of selectively logged forest using vegetation index in the brazilian Amazon. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 19., 2019, Santos, SP. Anais... São José dos Campos: Inpe, 2019. 4 p. Editores: Douglas Francisco Marcolino Gherardi; Ieda Del'Arco Sanches; Luiz Eduardo Oliveira e Cruz de Aragão.| Tipo: Artigo em Anais de Congresso |
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| 2. | | BEUCHLE, R.; EVA, H. D.; MIRANDA, E. E. de; HOLLER, W. A.; OSHIRO, O. T.; ACHARD, F. Global tropical forest cover change assessment with medium spatial satellite imagery using a systematic sample grid - data, methods and first results. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 15., 2011, Curitiba. Anais... São José dos Campos: INPE, 2011. p. 2981-2988.| Tipo: Artigo em Anais de Congresso |
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| 4. | | EVA, H. D.; ACHARD, F.; BEUCHLE, R.; MIRANDA, E. E. de; CARBONI, S.; SELIGER, R.; VOLLMAR, M.; HOLLER, W. A.; OSHIRO, O. T.; ARROYO, V. B.; GALLEGO, J. Forest Cover Changes in Tropical South and Central America from 1990 to 2005 and Related Carbon Emissions and Removals. Remote Sensing, v. 4, p. 1369-1391, 2012. p. 1369-1391.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 4 |
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| 5. | | SILVA JUNIOR, C. H. L.; CARVALHO, N. S.; PESSÔA, A. C. M.; REIS, J. B. C.; PONTES-LOPES, A.; DOBLAS, J.; HEINRICH, V.; CAMPANHARO, W.; ALENCAR, A.; SILVA, C.; LAPOLA, D. M.; ARMENTERAS, D.; MATRICARDI, E. A. T.; BERENGUER, E.; CASSOL, H.; NUMATA, I.; HOUSE, J.; FERREIRA, J. N.; BARLOW, J.; GATTI, L.; BRANDO, P.; FEARNSIDE, P. M.; SAATCHI, S.; SILVA, S.; SITCH, S.; AGUIAR, A. P.; SILVA, C. A.; VANCUTSEM, C.; ACHARD, F.; BEUCHLE, R.; SHIMABUKURO, Y. E.; ANDERSON, L. O.; ARAGÃO, L. E. O. C. Amazonian forest degradation must be incorporated into the COP26 agenda. Nature Geoscience, v. 14, p. 634-635, Sep. 2021.| Tipo: Nota Técnica/Nota Científica |
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