Potential of using tatistical quality control in agriculture 4.0

Detalhes bibliográficos
Autor(a) principal: Silva,Rouverson Pereira da
Data de Publicação: 2020
Outros Autores: Santos,Adão Felipe dos, Oliveira,Bruno Rocca de, Souza,Jarlyson Brunno Costa, Oliveira,Danilo Tedesco de, Carneiro,Franciele Morlin
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Revista ciência agronômica (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902020000500414
Resumo: ABSTRACT Agriculture 4.0 involves the incorporation of information and communication technologies into machines, equipment, and sensors for use in agricultural production systems. It aims to ease decision-making in agricultural processes. Statistical Quality Control (SQC) is a statistical method with several techniques and tools used to analyze the variability. These tools can be used to provide important information for decision making, including for mechanized agricultural operations. This paper aimed to characterize the worldwide scientific literature on Statistical Process Control use in mechanized agricultural processes, demonstrating its potential to be incorporated into Agriculture 4.0. Our research involved a bibliometric survey on Scopus and Academic Google databases. The analyzed studies allowed us to infer that SQC tools may improve understanding of mechanized operations and be used in Agriculture 4.0. Such features can also streamline and enhance decision making, converting big data into useful information.
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spelling Potential of using tatistical quality control in agriculture 4.0Statistical process controlDigital agricultureQuality toolsQuality indicatorsABSTRACT Agriculture 4.0 involves the incorporation of information and communication technologies into machines, equipment, and sensors for use in agricultural production systems. It aims to ease decision-making in agricultural processes. Statistical Quality Control (SQC) is a statistical method with several techniques and tools used to analyze the variability. These tools can be used to provide important information for decision making, including for mechanized agricultural operations. This paper aimed to characterize the worldwide scientific literature on Statistical Process Control use in mechanized agricultural processes, demonstrating its potential to be incorporated into Agriculture 4.0. Our research involved a bibliometric survey on Scopus and Academic Google databases. The analyzed studies allowed us to infer that SQC tools may improve understanding of mechanized operations and be used in Agriculture 4.0. Such features can also streamline and enhance decision making, converting big data into useful information.Universidade Federal do Ceará2020-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902020000500414Revista Ciência Agronômica v.51 n.spe 2020reponame:Revista ciência agronômica (Online)instname:Universidade Federal do Ceará (UFC)instacron:UFC10.5935/1806-6690.20200105info:eu-repo/semantics/openAccessSilva,Rouverson Pereira daSantos,Adão Felipe dosOliveira,Bruno Rocca deSouza,Jarlyson Brunno CostaOliveira,Danilo Tedesco deCarneiro,Franciele Morlineng2021-08-17T00:00:00Zoai:scielo:S1806-66902020000500414Revistahttp://www.ccarevista.ufc.br/PUBhttps://old.scielo.br/oai/scielo-oai.php||alekdutra@ufc.br|| ccarev@ufc.br1806-66900045-6888opendoar:2021-08-17T00:00Revista ciência agronômica (Online) - Universidade Federal do Ceará (UFC)false
dc.title.none.fl_str_mv Potential of using tatistical quality control in agriculture 4.0
title Potential of using tatistical quality control in agriculture 4.0
spellingShingle Potential of using tatistical quality control in agriculture 4.0
Silva,Rouverson Pereira da
Statistical process control
Digital agriculture
Quality tools
Quality indicators
title_short Potential of using tatistical quality control in agriculture 4.0
title_full Potential of using tatistical quality control in agriculture 4.0
title_fullStr Potential of using tatistical quality control in agriculture 4.0
title_full_unstemmed Potential of using tatistical quality control in agriculture 4.0
title_sort Potential of using tatistical quality control in agriculture 4.0
author Silva,Rouverson Pereira da
author_facet Silva,Rouverson Pereira da
Santos,Adão Felipe dos
Oliveira,Bruno Rocca de
Souza,Jarlyson Brunno Costa
Oliveira,Danilo Tedesco de
Carneiro,Franciele Morlin
author_role author
author2 Santos,Adão Felipe dos
Oliveira,Bruno Rocca de
Souza,Jarlyson Brunno Costa
Oliveira,Danilo Tedesco de
Carneiro,Franciele Morlin
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Silva,Rouverson Pereira da
Santos,Adão Felipe dos
Oliveira,Bruno Rocca de
Souza,Jarlyson Brunno Costa
Oliveira,Danilo Tedesco de
Carneiro,Franciele Morlin
dc.subject.por.fl_str_mv Statistical process control
Digital agriculture
Quality tools
Quality indicators
topic Statistical process control
Digital agriculture
Quality tools
Quality indicators
description ABSTRACT Agriculture 4.0 involves the incorporation of information and communication technologies into machines, equipment, and sensors for use in agricultural production systems. It aims to ease decision-making in agricultural processes. Statistical Quality Control (SQC) is a statistical method with several techniques and tools used to analyze the variability. These tools can be used to provide important information for decision making, including for mechanized agricultural operations. This paper aimed to characterize the worldwide scientific literature on Statistical Process Control use in mechanized agricultural processes, demonstrating its potential to be incorporated into Agriculture 4.0. Our research involved a bibliometric survey on Scopus and Academic Google databases. The analyzed studies allowed us to infer that SQC tools may improve understanding of mechanized operations and be used in Agriculture 4.0. Such features can also streamline and enhance decision making, converting big data into useful information.
publishDate 2020
dc.date.none.fl_str_mv 2020-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902020000500414
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902020000500414
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.5935/1806-6690.20200105
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Universidade Federal do Ceará
publisher.none.fl_str_mv Universidade Federal do Ceará
dc.source.none.fl_str_mv Revista Ciência Agronômica v.51 n.spe 2020
reponame:Revista ciência agronômica (Online)
instname:Universidade Federal do Ceará (UFC)
instacron:UFC
instname_str Universidade Federal do Ceará (UFC)
instacron_str UFC
institution UFC
reponame_str Revista ciência agronômica (Online)
collection Revista ciência agronômica (Online)
repository.name.fl_str_mv Revista ciência agronômica (Online) - Universidade Federal do Ceará (UFC)
repository.mail.fl_str_mv ||alekdutra@ufc.br|| ccarev@ufc.br
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