Artificial intelligence applications in the agriculture 4.0

Detalhes bibliográficos
Autor(a) principal: Megeto,Guilherme Augusto Silva
Data de Publicação: 2020
Outros Autores: Silva,Atilla Graciano da, Bulgarelli,Rodrigo Fernandes, Bublitz,Carlos Fabiel, Valente,Augusto Cavalcante, Costa,Daniel Augusto Guerra da
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=S1806-66902020000500405
Resumo: ABSTRACT The usage of digital data is one of the main characteristics of the Agriculture 4.0 era. Different devices and sensors may be used to capture a variety of types of data that enable the development of applications of computer vision, acoustic events, and data processing. These applications are useful for monitoring, understanding, and predicting many attributes of agricultural chain production with the objective of assisting farmers in the decision-making process. In a scenario of increasing obligation for sustainable usage of natural resources and an increase in production rates to assure a food security situation in the world, there is a high demand for improvements at any stage of agricultural processes. This paper aims to contribute to further research on artificial intelligence in the agricultural context, listing sample practical AI scenarios, including those that the Eldorado Research Institute has contributed. Throughout this paper, different applications of AI are discussed, highlighting some characteristics, advantages, disadvantages, and results to provide an overview of the different technologies that can be applied in agriculture. Furthermore, we presented the main challenges of popularizing the use of AI-based systems, some possible approaches to reduce the difficulties, and a view of the next most promising technologies in conjunction with AI.
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spelling Artificial intelligence applications in the agriculture 4.0AlgorithmNeural networkComputer visionAcoustic event detectionData processingABSTRACT The usage of digital data is one of the main characteristics of the Agriculture 4.0 era. Different devices and sensors may be used to capture a variety of types of data that enable the development of applications of computer vision, acoustic events, and data processing. These applications are useful for monitoring, understanding, and predicting many attributes of agricultural chain production with the objective of assisting farmers in the decision-making process. In a scenario of increasing obligation for sustainable usage of natural resources and an increase in production rates to assure a food security situation in the world, there is a high demand for improvements at any stage of agricultural processes. This paper aims to contribute to further research on artificial intelligence in the agricultural context, listing sample practical AI scenarios, including those that the Eldorado Research Institute has contributed. Throughout this paper, different applications of AI are discussed, highlighting some characteristics, advantages, disadvantages, and results to provide an overview of the different technologies that can be applied in agriculture. Furthermore, we presented the main challenges of popularizing the use of AI-based systems, some possible approaches to reduce the difficulties, and a view of the next most promising technologies in conjunction with AI.Universidade Federal do Ceará2020-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902020000500405Revista Ciência Agronômica v.51 n.spe 2020reponame:Revista ciência agronômica (Online)instname:Universidade Federal do Ceará (UFC)instacron:UFC10.5935/1806-6690.20200084info:eu-repo/semantics/openAccessMegeto,Guilherme Augusto SilvaSilva,Atilla Graciano daBulgarelli,Rodrigo FernandesBublitz,Carlos FabielValente,Augusto CavalcanteCosta,Daniel Augusto Guerra daeng2021-08-17T00:00:00Zoai:scielo:S1806-66902020000500405Revistahttp://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 Artificial intelligence applications in the agriculture 4.0
title Artificial intelligence applications in the agriculture 4.0
spellingShingle Artificial intelligence applications in the agriculture 4.0
Megeto,Guilherme Augusto Silva
Algorithm
Neural network
Computer vision
Acoustic event detection
Data processing
title_short Artificial intelligence applications in the agriculture 4.0
title_full Artificial intelligence applications in the agriculture 4.0
title_fullStr Artificial intelligence applications in the agriculture 4.0
title_full_unstemmed Artificial intelligence applications in the agriculture 4.0
title_sort Artificial intelligence applications in the agriculture 4.0
author Megeto,Guilherme Augusto Silva
author_facet Megeto,Guilherme Augusto Silva
Silva,Atilla Graciano da
Bulgarelli,Rodrigo Fernandes
Bublitz,Carlos Fabiel
Valente,Augusto Cavalcante
Costa,Daniel Augusto Guerra da
author_role author
author2 Silva,Atilla Graciano da
Bulgarelli,Rodrigo Fernandes
Bublitz,Carlos Fabiel
Valente,Augusto Cavalcante
Costa,Daniel Augusto Guerra da
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Megeto,Guilherme Augusto Silva
Silva,Atilla Graciano da
Bulgarelli,Rodrigo Fernandes
Bublitz,Carlos Fabiel
Valente,Augusto Cavalcante
Costa,Daniel Augusto Guerra da
dc.subject.por.fl_str_mv Algorithm
Neural network
Computer vision
Acoustic event detection
Data processing
topic Algorithm
Neural network
Computer vision
Acoustic event detection
Data processing
description ABSTRACT The usage of digital data is one of the main characteristics of the Agriculture 4.0 era. Different devices and sensors may be used to capture a variety of types of data that enable the development of applications of computer vision, acoustic events, and data processing. These applications are useful for monitoring, understanding, and predicting many attributes of agricultural chain production with the objective of assisting farmers in the decision-making process. In a scenario of increasing obligation for sustainable usage of natural resources and an increase in production rates to assure a food security situation in the world, there is a high demand for improvements at any stage of agricultural processes. This paper aims to contribute to further research on artificial intelligence in the agricultural context, listing sample practical AI scenarios, including those that the Eldorado Research Institute has contributed. Throughout this paper, different applications of AI are discussed, highlighting some characteristics, advantages, disadvantages, and results to provide an overview of the different technologies that can be applied in agriculture. Furthermore, we presented the main challenges of popularizing the use of AI-based systems, some possible approaches to reduce the difficulties, and a view of the next most promising technologies in conjunction with AI.
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=S1806-66902020000500405
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902020000500405
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.5935/1806-6690.20200084
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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