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Registro Completo |
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
01/10/2001 |
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Data da última atualização: |
01/10/2001 |
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Autoria: |
HARRIS, C.; McLAUGHLIN, W.; BROWN, G.; BECKER, D. R. |
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Título: |
Rural communities in the inland Northwest: an asessment of small rural communities in the interior and upper Columbia River basins. |
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Ano de publicação: |
2000 |
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Fonte/Imprenta: |
Portland: USDA. Forest Service. Pacific Northwest Research Station, 2000. |
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Páginas: |
120 p. |
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Série: |
(USDA. For. Serv. Gen. Tech. Rep. PNW-GTR-477). |
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Idioma: |
Inglês |
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Palavras-Chave: |
Comunidade florestal. |
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Thesagro: |
Comunidade Rural. |
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Thesaurus Nal: |
forest communities; rural communities. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 00699nam a2200205 a 4500
001 1297634
005 2001-10-01
008 2000 bl uuuu 00u1 u #d
100 1 $aHARRIS, C.
245 $aRural communities in the inland Northwest$ban asessment of small rural communities in the interior and upper Columbia River basins.
260 $aPortland: USDA. Forest Service. Pacific Northwest Research Station$c2000
300 $a120 p.
490 $a(USDA. For. Serv. Gen. Tech. Rep. PNW-GTR-477).
650 $aforest communities
650 $arural communities
650 $aComunidade Rural
653 $aComunidade florestal
700 1 $aMcLAUGHLIN, W.
700 1 $aBROWN, G.
700 1 $aBECKER, D. R.
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Registro original: |
Embrapa Florestas (CNPF) |
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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
08/04/2022 |
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Data da última atualização: |
08/04/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
BARBEDO, J. G. A. |
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Afiliação: |
JAYME GARCIA ARNAL BARBEDO, CNPTIA. |
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Título: |
Data fusion in agriculture: resolving ambiguities and closing data gaps. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Sensors, v. 22, n. 6, p. 1-20, 2022. |
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DOI: |
https://doi.org/10.3390/s22062285 |
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Idioma: |
Inglês |
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Notas: |
Article number: 2285. |
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Conteúdo: |
Abstract. Acquiring useful data from agricultural areas has always been somewhat of a challenge, as these are often expansive, remote, and vulnerable to weather events. Despite these challenges, as technologies evolve and prices drop, a surge of new data are being collected. Although a wealth of data are being collected at different scales (i.e., proximal, aerial, satellite, ancillary data), this has been geographically unequal, causing certain areas to be virtually devoid of useful data to help face their specific challenges. However, even in areas with available resources and good infrastructure, data and knowledge gaps are still prevalent, because agricultural environments are mostly uncontrolled and there are vast numbers of factors that need to be taken into account and properly measured for a full characterization of a given area. As a result, data from a single sensor type are frequently unable to provide unambiguous answers, even with very effective algorithms, and even if the problem at hand is well defined and limited in scope. Fusing the information contained in different sensors and in data from different types is one possible solution that has been explored for some decades. The idea behind data fusion involves exploring complementarities and synergies of different kinds of data in order to extract more reliable and useful information about the areas being analyzed. While some success has been achieved, there are still many challenges that prevent a more widespread adoption of this type of approach. This is particularly true for the highly complex environments found in agricultural areas. In this article, we provide a comprehensive overview on the data fusion applied to agricultural problems; we present the main successes, highlight the main challenges that remain, and suggest possible directions for future research. MenosAbstract. Acquiring useful data from agricultural areas has always been somewhat of a challenge, as these are often expansive, remote, and vulnerable to weather events. Despite these challenges, as technologies evolve and prices drop, a surge of new data are being collected. Although a wealth of data are being collected at different scales (i.e., proximal, aerial, satellite, ancillary data), this has been geographically unequal, causing certain areas to be virtually devoid of useful data to help face their specific challenges. However, even in areas with available resources and good infrastructure, data and knowledge gaps are still prevalent, because agricultural environments are mostly uncontrolled and there are vast numbers of factors that need to be taken into account and properly measured for a full characterization of a given area. As a result, data from a single sensor type are frequently unable to provide unambiguous answers, even with very effective algorithms, and even if the problem at hand is well defined and limited in scope. Fusing the information contained in different sensors and in data from different types is one possible solution that has been explored for some decades. The idea behind data fusion involves exploring complementarities and synergies of different kinds of data in order to extract more reliable and useful information about the areas being analyzed. While some success has been achieved, there are still many challenges that prevent a more widespr... Mostrar Tudo |
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Palavras-Chave: |
Data fusion; Fusão de dados; Inteligência artificial; Sensores; Sensors; Variabilidade. |
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Thesagro: |
Agricultura de Precisão. |
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Thesaurus NAL: |
Artificial intelligence; Precision agriculture; Variability. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1142040/1/AP-Data-Fusion-in-Agriculture-2022.pdf
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Marc: |
LEADER 02640naa a2200265 a 4500
001 2142040
005 2022-04-08
008 2022 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.3390/s22062285$2DOI
100 1 $aBARBEDO, J. G. A.
245 $aData fusion in agriculture$bresolving ambiguities and closing data gaps.$h[electronic resource]
260 $c2022
500 $aArticle number: 2285.
520 $aAbstract. Acquiring useful data from agricultural areas has always been somewhat of a challenge, as these are often expansive, remote, and vulnerable to weather events. Despite these challenges, as technologies evolve and prices drop, a surge of new data are being collected. Although a wealth of data are being collected at different scales (i.e., proximal, aerial, satellite, ancillary data), this has been geographically unequal, causing certain areas to be virtually devoid of useful data to help face their specific challenges. However, even in areas with available resources and good infrastructure, data and knowledge gaps are still prevalent, because agricultural environments are mostly uncontrolled and there are vast numbers of factors that need to be taken into account and properly measured for a full characterization of a given area. As a result, data from a single sensor type are frequently unable to provide unambiguous answers, even with very effective algorithms, and even if the problem at hand is well defined and limited in scope. Fusing the information contained in different sensors and in data from different types is one possible solution that has been explored for some decades. The idea behind data fusion involves exploring complementarities and synergies of different kinds of data in order to extract more reliable and useful information about the areas being analyzed. While some success has been achieved, there are still many challenges that prevent a more widespread adoption of this type of approach. This is particularly true for the highly complex environments found in agricultural areas. In this article, we provide a comprehensive overview on the data fusion applied to agricultural problems; we present the main successes, highlight the main challenges that remain, and suggest possible directions for future research.
650 $aArtificial intelligence
650 $aPrecision agriculture
650 $aVariability
650 $aAgricultura de Precisão
653 $aData fusion
653 $aFusão de dados
653 $aInteligência artificial
653 $aSensores
653 $aSensors
653 $aVariabilidade
773 $tSensors$gv. 22, n. 6, p. 1-20, 2022.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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