| |
|
|
|
Registro Completo |
|
Biblioteca(s): |
Embrapa Trigo. |
|
Data corrente: |
09/09/1992 |
|
Data da última atualização: |
14/12/2011 |
|
Autoria: |
OLIVEIRA, O. F. de. |
|
Afiliação: |
Instituto Agronômico de Campinas, SP. |
|
Título: |
Criterio adotado pelo Instituto Agronômico de Campinas para recomendação de adubação em trigo baseado em dados experimentais de campo e na análise de solo. |
|
Ano de publicação: |
0 |
|
Fonte/Imprenta: |
[Campinas: IAC, 19--]. |
|
Páginas: |
2 p. |
|
Idioma: |
Português |
|
Palavras-Chave: |
Fertilização. |
|
Categoria do assunto: |
-- |
|
Marc: |
LEADER 00429nam a2200121 a 4500
001 1845155
005 2011-12-14
008 bl uuuu u0uu1 u #d
100 1 $aOLIVEIRA, O. F. de
245 $aCriterio adotado pelo Instituto Agronômico de Campinas para recomendação de adubação em trigo baseado em dados experimentais de campo e na análise de solo.
260 $a[Campinas: IAC, 19--].$c0
300 $a2 p.
653 $aFertilização
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Trigo (CNPT) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
|
 | Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
Registro Completo
|
Biblioteca(s): |
Embrapa Agricultura Digital. |
|
Data corrente: |
19/12/2013 |
|
Data da última atualização: |
08/01/2020 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Circulação/Nível: |
A - 1 |
|
Autoria: |
ROMANI, L. A. S.; AVILA, A. M. H. de; CHINO, D. Y. T.; ZULLO JÚNIOR, J.; CHBEIR, R.; TRAINA JÚNIOR, C.; TRAINA, A. J. M. |
|
Afiliação: |
LUCIANA ALVIM SANTOS ROMANI, CNPTIA; ANA MARIA H. DE AVILA, Cepagri/Unicamp; DANIEL Y. T. CHINO, ICMC/USP; JURANDIR ZULLO JÚNIOR, Cepagri/Unicamp; RICHARD CHBEIR, University of Bourgogne; CAETANO TRAINA JÚNIOR, ICMC/USP; AGMA J. M. TRAINA, ICMC/USP. |
|
Título: |
A new time series mining approach applied to multitemporal remote sensing imagery. |
|
Ano de publicação: |
2013 |
|
Fonte/Imprenta: |
IEEE transactions on geoscience and remote sensing, New York, v. 51, n. 1, p. 140-150, Jan. 2013. |
|
Idioma: |
Inglês |
|
Conteúdo: |
Abstract-In this paper, we present a novel unsupervised algorithm, called CLimate and rEmote sensing Association patteRns Miner, for mining association patterns on heterogeneous time series from climate and remote sensing data integrated in a remote sensing information system developed to improve the monitoring of sugar cane fields. The system, called RemoteAgri, consists of a large database of climate data and low-resolution remote sensing images, an image preprocessing module, a time series extraction module, and time series mining methods. The preprocessing module was projected to perform accurate geometric correction, what is a requirement particularly for land and agriculture applications of satellite images. The time series extraction is accomplished through a graphical interface that allows easy interaction and high flexibility to users. The time series mining method transforms series to symbolic representation in order to identify patterns in a multitemporal satellite images and associate them with patterns in other series within a temporal sliding window. The validation process was achieved with agroclimatic data and NOAA-AVHRR images of sugar cane fields. Results show a correlation between agroclimatic time series and vegetation index images. Rules generated by our new algorithm show the association patterns in different periods of time in each time series, pointing to a time delay between the occurrences of patterns in the series analyzed, corroborating what specialists usually forecast without having the burden of dealing with many data charts. MenosAbstract-In this paper, we present a novel unsupervised algorithm, called CLimate and rEmote sensing Association patteRns Miner, for mining association patterns on heterogeneous time series from climate and remote sensing data integrated in a remote sensing information system developed to improve the monitoring of sugar cane fields. The system, called RemoteAgri, consists of a large database of climate data and low-resolution remote sensing images, an image preprocessing module, a time series extraction module, and time series mining methods. The preprocessing module was projected to perform accurate geometric correction, what is a requirement particularly for land and agriculture applications of satellite images. The time series extraction is accomplished through a graphical interface that allows easy interaction and high flexibility to users. The time series mining method transforms series to symbolic representation in order to identify patterns in a multitemporal satellite images and associate them with patterns in other series within a temporal sliding window. The validation process was achieved with agroclimatic data and NOAA-AVHRR images of sugar cane fields. Results show a correlation between agroclimatic time series and vegetation index images. Rules generated by our new algorithm show the association patterns in different periods of time in each time series, pointing to a time delay between the occurrences of patterns in the series analyzed, corroborating what speci... Mostrar Tudo |
|
Palavras-Chave: |
Association rules; Imagens NOAA-AVHRR; Regras de associação; Séries temporais. |
|
Thesagro: |
Sensoriamento Remoto. |
|
Thesaurus NAL: |
Remote sensing; Time series analysis. |
|
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
|
Marc: |
LEADER 02454naa a2200277 a 4500
001 1974345
005 2020-01-08
008 2013 bl uuuu u00u1 u #d
100 1 $aROMANI, L. A. S.
245 $aA new time series mining approach applied to multitemporal remote sensing imagery.$h[electronic resource]
260 $c2013
520 $aAbstract-In this paper, we present a novel unsupervised algorithm, called CLimate and rEmote sensing Association patteRns Miner, for mining association patterns on heterogeneous time series from climate and remote sensing data integrated in a remote sensing information system developed to improve the monitoring of sugar cane fields. The system, called RemoteAgri, consists of a large database of climate data and low-resolution remote sensing images, an image preprocessing module, a time series extraction module, and time series mining methods. The preprocessing module was projected to perform accurate geometric correction, what is a requirement particularly for land and agriculture applications of satellite images. The time series extraction is accomplished through a graphical interface that allows easy interaction and high flexibility to users. The time series mining method transforms series to symbolic representation in order to identify patterns in a multitemporal satellite images and associate them with patterns in other series within a temporal sliding window. The validation process was achieved with agroclimatic data and NOAA-AVHRR images of sugar cane fields. Results show a correlation between agroclimatic time series and vegetation index images. Rules generated by our new algorithm show the association patterns in different periods of time in each time series, pointing to a time delay between the occurrences of patterns in the series analyzed, corroborating what specialists usually forecast without having the burden of dealing with many data charts.
650 $aRemote sensing
650 $aTime series analysis
650 $aSensoriamento Remoto
653 $aAssociation rules
653 $aImagens NOAA-AVHRR
653 $aRegras de associação
653 $aSéries temporais
700 1 $aAVILA, A. M. H. de
700 1 $aCHINO, D. Y. T.
700 1 $aZULLO JÚNIOR, J.
700 1 $aCHBEIR, R.
700 1 $aTRAINA JÚNIOR, C.
700 1 $aTRAINA, A. J. M.
773 $tIEEE transactions on geoscience and remote sensing, New York$gv. 51, n. 1, p. 140-150, Jan. 2013.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
| Nenhum registro encontrado para a expressão de busca informada. |
|
|
