Potential of using tatistical quality control in agriculture 4.0
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , |
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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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 |
_version_ |
1750297490208325632 |