| |
|
|
|
Registro Completo |
|
Biblioteca(s): |
Embrapa Meio-Norte; Embrapa Soja; Embrapa Trigo; Embrapa Unidades Centrais. |
|
Data corrente: |
24/07/1992 |
|
Data da última atualização: |
24/05/2011 |
|
Autoria: |
LANTMANN, A. F.; CAMPO, R. J.; SFREDO, G. J.; BORKERT, C. M. |
|
Afiliação: |
EMBRAPA-CNPSo. Londrina, PR. |
|
Título: |
Micronutrientes para a cultura da soja no Estado do Parana: zinco e molibdenio. |
|
Ano de publicação: |
1985 |
|
Fonte/Imprenta: |
Londrina: EMBRAPA-CNPSo, 1985. |
|
Páginas: |
8p. |
|
Série: |
(EMBRAPA-CNPSo. Comunicado Tecnico, 34). |
|
Idioma: |
Português |
|
Palavras-Chave: |
América do Sul; Analysis; Brasil; Calcareous; Correção; EMBRAPA; Fertilização; Fertilization; Limy; Micronutrient; Micronutriente; Micronutriente do solo; Micronutrientes; Paraná; Soybean; Sulphur. |
|
Thesagro: |
Acidez; Análise; Calcário; Enxofre; Fósforo; Molibdênio; Nitrogênio; Potássio; Produtividade; Soja; Solo; Zinco. |
|
Thesaurus Nal: |
acidity; Brazil; molybdenum; nitrogen; phosphorus; potassium; soil; South America; zinc. |
|
Categoria do assunto: |
-- |
|
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/35344/1/1985-Comunicado-Tecnico.n.34.Micronutrientes-18x26-OK.pdf
|
|
Marc: |
LEADER 01430nam a2200601 a 4500
001 1445462
005 2011-05-24
008 1985 bl uuuu u0uu1 u #d
100 1 $aLANTMANN, A. F.
245 $aMicronutrientes para a cultura da soja no Estado do Parana$bzinco e molibdenio.
260 $aLondrina: EMBRAPA-CNPSo$c1985
300 $a8p.
490 $a(EMBRAPA-CNPSo. Comunicado Tecnico, 34).
650 $aacidity
650 $aBrazil
650 $amolybdenum
650 $anitrogen
650 $aphosphorus
650 $apotassium
650 $asoil
650 $aSouth America
650 $azinc
650 $aAcidez
650 $aAnálise
650 $aCalcário
650 $aEnxofre
650 $aFósforo
650 $aMolibdênio
650 $aNitrogênio
650 $aPotássio
650 $aProdutividade
650 $aSoja
650 $aSolo
650 $aZinco
653 $aAmérica do Sul
653 $aAnalysis
653 $aBrasil
653 $aCalcareous
653 $aCorreção
653 $aEMBRAPA
653 $aFertilização
653 $aFertilization
653 $aLimy
653 $aMicronutrient
653 $aMicronutriente
653 $aMicronutriente do solo
653 $aMicronutrientes
653 $aParaná
653 $aSoybean
653 $aSulphur
700 1 $aCAMPO, R. J.
700 1 $aSFREDO, G. J.
700 1 $aBORKERT, C. M.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Soja (CNPSO) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
|
Registro Completo
|
Biblioteca(s): |
Embrapa Café. |
|
Data corrente: |
13/03/2023 |
|
Data da última atualização: |
13/03/2023 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Circulação/Nível: |
B - 4 |
|
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
|
|
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.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Café (CNPCa) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
| Nenhum registro encontrado para a expressão de busca informada. |
|
|
