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| Registros recuperados : 19 | |
| 5. |  | MATOS, C. S.; DUCROQUET, J. H. J. Efeitos da cianamida hidrogenada na quebra de dormencia de pessegueiro (Prunus persica Batsch), cv. rubidoux, na Regiao do Alto Vale do Rio do Peixe, SC Revista Brasileira de Fruticultura, v.14, n.2, p.175-178, Cruz das Almas, 1992| Biblioteca(s): Embrapa Mandioca e Fruticultura. |
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| 10. | | MATOS, C. S.; DUCROQUET, J. P. H. J.; PETRI, J. L.; MONDIN, V. P. Coral tardio, uma nova cultivar de pessegueiro, para a regiao do Alto Vale do Rio do Peixe - SC Revista Brasileira de Fruticultura, v.14, n.2, p.253-256, Cruz das Almas, 1992| Biblioteca(s): Embrapa Mandioca e Fruticultura. |
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| 16. | | LIMA, J. de F.; SANTOS, U. R. A. dos; CASTILHO, C. de L.; MATOS, C. S.; SILVA, D. R. da; MORAES, R. Parâmetros de qualidade de água no cultivo de tambaqui e alface em sistema de aquaponia. In: JORNADA CIENTÍFICA DA EMBRAPA AMAPÁ, 4., 2018, Macapá. Resumos... Macapá: Embrapa Amapá, 2019. p. 26. Editores técnicos: Adilson Lopes Lima e Ricardo Adaime.| Biblioteca(s): Embrapa Amapá. |
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| 17. | | SANTOS, U. R. A.; CASTILHO, C. de L.; MATOS, C. S.; SILVA, D. R. da; MORAES, R.; LIMA, J. de F. Policultivo de Colossoma macropomum e Macrobrachium amazonicum em sistema aquapônico de produção. In: JORNADA CIENTÍFICA DA EMBRAPA AMAPÁ, 4., 2018, Macapá. Resumos... Macapá: Embrapa Amapá, 2019. p. 14. Editores técnicos: Adilson Lopes Lima e Ricardo Adaime.| Biblioteca(s): Embrapa Amapá. |
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| 18. | | MACHADO, M. L.; FRANCO, G. C.; FIGUEIREDO, V. C.; VOLPATO, M. M. L.; SILVA, V. A.; SANTOS, S. A. dos; FERRAZ, G. A. S.; INÁCIO, F. D.; MATOS, C. S. M. de; ALVES, H. M. R. Estimativa de clorofila em folhas de cafeeiros por meio de câmera acoplada a aeronave remotamente pilotada. In: CONGRESSO BRASILEIRO DE PESQUISAS CAFEEIRAS, 47., 2023, Caxambú, MG. A tecnologia divulgando e o cafezal melhorando: trabalhos apresentados. Varginha: Fundação Procafé, 2023. p. 114| Biblioteca(s): Embrapa Café. |
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| 19. | | 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. Predicting coffee water potential from spectral reflectance indices with neural networks. Smart Agricultural Technology, v. 4, 100213, 2023. 6 p.| Biblioteca(s): Embrapa Café. |
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| Registros recuperados : 19 | |
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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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Circulação/Nível: |
B - 4 |
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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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Embrapa Café (CNPCa) |
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