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Biblioteca(s): |
Embrapa Café. |
Data corrente: |
13/03/2023 |
Data da última atualização: |
13/03/2023 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
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. |
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. |
Título: |
Predicting coffee water potential from spectral reflectance indices with neural networks. |
Ano de publicação: |
2023 |
Fonte/Imprenta: |
Smart Agricultural Technology, v. 4, 100213, 2023. |
Páginas: |
6 p. |
DOI: |
https://doi.org/10.1016/j.atech.2023.100213 |
Idioma: |
Inglês |
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 |
Thesaurus Nal: |
Artificial intelligence; Coffea; Neural networks; Trees; Water potential. |
Categoria do assunto: |
-- |
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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Registro Completo
Biblioteca(s): |
Embrapa Amazônia Ocidental. |
Data corrente: |
09/05/2005 |
Data da última atualização: |
31/01/2019 |
Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
Autoria: |
LEHMANN, J.; CAMPOS, C. V.; MACEDO, J. L. V. de; GERMAN, L. |
Afiliação: |
JEFERSON LUIS VASCONCELOS DE MACEDO, CPAA. |
Título: |
Sequential P fractionation of relict anthropogenic dark earths of Amazonia. |
Ano de publicação: |
2004 |
Fonte/Imprenta: |
In: GLASER, B.; WOODS, W. I. (Ed.). Amazonian dark earths: explorations in space and time. Berlin: Springer, 2004. p. 113-123. |
Idioma: |
Inglês |
Conteúdo: |
This study compares different P pools of two anthropogenic dark earths with unfertilized and fertilized terra firme soils from Central Amazonia. One anthrosol was collected near Rio Preto da Eva, 60 km north of Manaus, Brazil. The socond anthrosol was obtained from the lower Rio Negro in the community of Marajá. |
Palavras-Chave: |
Brasil; Terra firme; Terra preta de índio. |
Thesagro: |
Fósforo. |
Thesaurus NAL: |
Amazonia. |
Categoria do assunto: |
-- |
Marc: |
LEADER 00969naa a2200217 a 4500 001 1675551 005 2019-01-31 008 2004 bl uuuu u00u1 u #d 100 1 $aLEHMANN, J. 245 $aSequential P fractionation of relict anthropogenic dark earths of Amazonia. 260 $c2004 520 $aThis study compares different P pools of two anthropogenic dark earths with unfertilized and fertilized terra firme soils from Central Amazonia. One anthrosol was collected near Rio Preto da Eva, 60 km north of Manaus, Brazil. The socond anthrosol was obtained from the lower Rio Negro in the community of Marajá. 650 $aAmazonia 650 $aFósforo 653 $aBrasil 653 $aTerra firme 653 $aTerra preta de índio 700 1 $aCAMPOS, C. V. 700 1 $aMACEDO, J. L. V. de 700 1 $aGERMAN, L. 773 $tIn: GLASER, B.; WOODS, W. I. (Ed.). Amazonian dark earths: explorations in space and time. Berlin: Springer, 2004. p. 113-123.
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