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Registro Completo |
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
13/03/2023 |
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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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Autoria: |
NUNES, P. H.; PIERANGELI, E. V.; SANTOS, M. O.; SILVEIRA, H. R. O.; MATOS, C. S. M. de; PEREIRA, A. B.; ALVES, H. M. R.; VOLPATO, M. M. L.; SILVA, V. A.; FERREIRA, D. D. |
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Afiliação: |
PEDRO HENRIQUE NUNES, UNIVERSIDADE FEDERAL DE LAVRAS; EDUARDO VILELA PIERANGELI, UNIVERSIDADE FEDERAL DE LAVRAS; MELINE OLIVEIRA SANTOS, EMPRESA DE PESQUISA AGROPECUÁRIA DE MINAS GERAIS; HELBERT REZENDE OLIVEIRA SILVEIRA, EMPRESA DE PESQUISA AGROPECUÁRIA DE MINAS GERAIS; CHRISTIANO SOUSA MACHADO DE MATOS, EMPRESA DE PESQUISA AGROPECUÁRIA DE MINAS GERAIS; ALESSANDRO BOTELHO PEREIRA, EMPRESA DE PESQUISA AGROPECUÁRIA DE MINAS GERAIS; HELENA MARIA RAMOS ALVES, CNPCa; MARGARETE MARIN LORDELO VOLPATO, EMPRESA DE PESQUISA AGROPECUÁRIA DE MINAS GERAIS; VÂNIA APARECIDA SILVA, EMPRESA DE PESQUISA AGROPECUÁRIA DE MINAS GERAIS; DANTON DIEGO FERREIRA, UNIVERSIDADE FEDERAL DE LAVRAS. |
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Título: |
Predicting coffee water potential from spectral reflectance indices with neural networks. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Smart Agricultural Technology, v. 4, 100213, 2023. |
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Páginas: |
6 p. |
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DOI: |
https://doi.org/10.1016/j.atech.2023.100213 |
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Idioma: |
Inglês |
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Conteúdo: |
Leaf water potential is one of the main parameters used to assess water relations in plants by revealing levels of tissue hydration. It is commonly measured with the Scholander pressure chamber; which demands hard work and a time-consuming process. On the other hand, there is a diversified literature demonstrating the assessments of several plant variables via indices of leaf reflectance, that also present direct and indirect relationships with water potential. The aim of this work is to exploit spectral variables to estimate the water potential of coffee plants by using computational intelligence approaches. Data was collected in the cities of Santo Antônio do Amparo and Diamantina, Brazil, from 2014 to 2018. Two neural networks (Multi-Layer Perceptron) were designed to estimate and classify leaf water potential based on spectral variables. Moreover, a classifier and an estimator based on decision tree were also developed. The results showed that the artificial neural network model was superior as an estimator when compared with the decision tree model, with an average confidence index of 0.8550. On the other hand, decision trees showed a slightly higher performance as a classifier, with an overall accuracy of 88.8% and a Kappa index of 70.07%. We concluded that the leaf reflectance indices may be properly used to build accurate models for estimating coffee water potential. The indices PRI, NDVI, CRI1 and SIPI were the most relevant ones for estimating and classifying the coffee water potential. MenosLeaf water potential is one of the main parameters used to assess water relations in plants by revealing levels of tissue hydration. It is commonly measured with the Scholander pressure chamber; which demands hard work and a time-consuming process. On the other hand, there is a diversified literature demonstrating the assessments of several plant variables via indices of leaf reflectance, that also present direct and indirect relationships with water potential. The aim of this work is to exploit spectral variables to estimate the water potential of coffee plants by using computational intelligence approaches. Data was collected in the cities of Santo Antônio do Amparo and Diamantina, Brazil, from 2014 to 2018. Two neural networks (Multi-Layer Perceptron) were designed to estimate and classify leaf water potential based on spectral variables. Moreover, a classifier and an estimator based on decision tree were also developed. The results showed that the artificial neural network model was superior as an estimator when compared with the decision tree model, with an average confidence index of 0.8550. On the other hand, decision trees showed a slightly higher performance as a classifier, with an overall accuracy of 88.8% and a Kappa index of 70.07%. We concluded that the leaf reflectance indices may be properly used to build accurate models for estimating coffee water potential. The indices PRI, NDVI, CRI1 and SIPI were the most relevant ones for estimating and classifying the c... Mostrar Tudo |
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Thesaurus Nal: |
Artificial intelligence; Coffea; Neural networks; Trees; Water potential. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1152292/1/Predicting-coffee-water-potential.pdf
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Marc: |
LEADER 02434naa a2200313 a 4500
001 2152292
005 2023-03-13
008 2023 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.atech.2023.100213$2DOI
100 1 $aNUNES, P. H.
245 $aPredicting coffee water potential from spectral reflectance indices with neural networks.$h[electronic resource]
260 $c2023
300 $a6 p.
520 $aLeaf water potential is one of the main parameters used to assess water relations in plants by revealing levels of tissue hydration. It is commonly measured with the Scholander pressure chamber; which demands hard work and a time-consuming process. On the other hand, there is a diversified literature demonstrating the assessments of several plant variables via indices of leaf reflectance, that also present direct and indirect relationships with water potential. The aim of this work is to exploit spectral variables to estimate the water potential of coffee plants by using computational intelligence approaches. Data was collected in the cities of Santo Antônio do Amparo and Diamantina, Brazil, from 2014 to 2018. Two neural networks (Multi-Layer Perceptron) were designed to estimate and classify leaf water potential based on spectral variables. Moreover, a classifier and an estimator based on decision tree were also developed. The results showed that the artificial neural network model was superior as an estimator when compared with the decision tree model, with an average confidence index of 0.8550. On the other hand, decision trees showed a slightly higher performance as a classifier, with an overall accuracy of 88.8% and a Kappa index of 70.07%. We concluded that the leaf reflectance indices may be properly used to build accurate models for estimating coffee water potential. The indices PRI, NDVI, CRI1 and SIPI were the most relevant ones for estimating and classifying the coffee water potential.
650 $aArtificial intelligence
650 $aCoffea
650 $aNeural networks
650 $aTrees
650 $aWater potential
700 1 $aPIERANGELI, E. V.
700 1 $aSANTOS, M. O.
700 1 $aSILVEIRA, H. R. O.
700 1 $aMATOS, C. S. M. de
700 1 $aPEREIRA, A. B.
700 1 $aALVES, H. M. R.
700 1 $aVOLPATO, M. M. L.
700 1 $aSILVA, V. A.
700 1 $aFERREIRA, D. D.
773 $tSmart Agricultural Technology$gv. 4, 100213, 2023.
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Registro original: |
Embrapa Café (CNPCa) |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Arroz e Feijão. Para informações adicionais entre em contato com cnpaf.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
15/09/2011 |
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Data da última atualização: |
20/09/2011 |
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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: |
FONSECA, F. A. da; SOARES JÚNIOR, M. S.; CALIARI, M.; BASSINELLO, P. Z.; EIFERT, E. da C.; GARCIA, D. M. |
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Afiliação: |
FLÁVIA ARAÚJO DA FONSECA, SENAI-GO; MANOEL SOARES SOARES JÚNIOR, UFG; MÁRCIO CALIARI, UFG; PRISCILA ZACZUK BASSINELLO, CNPAF; EDUARDO DA COSTA EIFERT, CNPAF; DIVA MENDONÇA GARCIA, INSTITUTO FEDERAL DE EDUCAÇÃO TECNOLÓGICA DE GOIÁS, URUTAÍ. |
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Título: |
Changes occurring during the parboiling of upland rice and in the maceration water at different temperatures and soaking times. |
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Ano de publicação: |
2011 |
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Fonte/Imprenta: |
International Journal of Food Science & Technology, v. 46, p. 1912-1920, 2011. |
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DOI: |
10.1111/j.1365-2621.2011.02701.x |
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Idioma: |
Inglês |
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Conteúdo: |
The objective of this work was to investigate the influence of temperature and soaking time on the quality of grains from two upland rice cultivars. Response surface methodology and a central compound rotational design were used. The data obtained for the cultivars BRS Primavera and BRS Sertaneja varied, respectively, between 27.7-55.0% and 26.0-51.7% for the Husk-Splitting Index; between 0.8-5.0% and 0.0-4.0% for the incidence of ?banana? grains; between 0.0-2.0% and 0.0-1.2% for non-gelatinised grains; and between 0.2-0.7% and 0.2-0.8% for soluble solids in the maceration water. Because BRS Primavera grains had a narrower shape, they absorbed the water faster and consequently presented a greater amount of physical defects, although the losses to the water were smaller than BRS Sertaneja. |
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Thesagro: |
Arroz; Maceração; Oryza sativa; Parboilização; Tecnologia de alimento. |
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Categoria do assunto: |
Q Alimentos e Nutrição Humana |
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Marc: |
LEADER 01600naa a2200253 a 4500
001 1900639
005 2011-09-20
008 2011 bl uuuu u00u1 u #d
024 7 $a10.1111/j.1365-2621.2011.02701.x$2DOI
100 1 $aFONSECA, F. A. da
245 $aChanges occurring during the parboiling of upland rice and in the maceration water at different temperatures and soaking times.
260 $c2011
520 $aThe objective of this work was to investigate the influence of temperature and soaking time on the quality of grains from two upland rice cultivars. Response surface methodology and a central compound rotational design were used. The data obtained for the cultivars BRS Primavera and BRS Sertaneja varied, respectively, between 27.7-55.0% and 26.0-51.7% for the Husk-Splitting Index; between 0.8-5.0% and 0.0-4.0% for the incidence of ?banana? grains; between 0.0-2.0% and 0.0-1.2% for non-gelatinised grains; and between 0.2-0.7% and 0.2-0.8% for soluble solids in the maceration water. Because BRS Primavera grains had a narrower shape, they absorbed the water faster and consequently presented a greater amount of physical defects, although the losses to the water were smaller than BRS Sertaneja.
650 $aArroz
650 $aMaceração
650 $aOryza sativa
650 $aParboilização
650 $aTecnologia de alimento
700 1 $aSOARES JÚNIOR, M. S.
700 1 $aCALIARI, M.
700 1 $aBASSINELLO, P. Z.
700 1 $aEIFERT, E. da C.
700 1 $aGARCIA, D. M.
773 $tInternational Journal of Food Science & Technology$gv. 46, p. 1912-1920, 2011.
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Embrapa Arroz e Feijão (CNPAF) |
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