Use of classifier to determine coffee harvest time by detachment force

Detalhes bibliográficos
Autor(a) principal: Barros,Murilo M. de
Data de Publicação: 2018
Outros Autores: Silva,Fábio M. da, Costa,Anderson G., Ferraz,Gabriel A. e S., Silva,Flávio C. da
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Revista Brasileira de Engenharia Agrícola e Ambiental (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662018000500366
Resumo: ABSTRACT Coffee quality is an essential aspect to increase its commercial value and for the Brazilian coffee business to remain prominent in the world market. Fruit maturity stage at harvest is an important factor that affects the quality and commercial value of the product. Therefore, the objective of this study was to develop a classifier using neural networks to distinguish green coffee fruits from mature coffee fruits, based on the detachment force. Fruit detachment force and the percentage value of the maturity stage were measured during a 75-day harvest window. Collections were carried out biweekly, resulting in five different moments within the harvest period. A classifier was developed using neural networks to distinguish green fruits from mature fruits in the harvest period analyzed. The results show that, in the first half of June, the supervised classified had the highest success percentage in differentiating green fruits from mature fruits, and this period was considered as ideal for a selective harvest under these experimental conditions.
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spelling Use of classifier to determine coffee harvest time by detachment forcecoffee cultivationmanagementmaturationsupervised classifiersABSTRACT Coffee quality is an essential aspect to increase its commercial value and for the Brazilian coffee business to remain prominent in the world market. Fruit maturity stage at harvest is an important factor that affects the quality and commercial value of the product. Therefore, the objective of this study was to develop a classifier using neural networks to distinguish green coffee fruits from mature coffee fruits, based on the detachment force. Fruit detachment force and the percentage value of the maturity stage were measured during a 75-day harvest window. Collections were carried out biweekly, resulting in five different moments within the harvest period. A classifier was developed using neural networks to distinguish green fruits from mature fruits in the harvest period analyzed. The results show that, in the first half of June, the supervised classified had the highest success percentage in differentiating green fruits from mature fruits, and this period was considered as ideal for a selective harvest under these experimental conditions.Departamento de Engenharia Agrícola - UFCG2018-05-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662018000500366Revista Brasileira de Engenharia Agrícola e Ambiental v.22 n.5 2018reponame:Revista Brasileira de Engenharia Agrícola e Ambiental (Online)instname:Universidade Federal de Campina Grande (UFCG)instacron:UFCG10.1590/1807-1929/agriambi.v22n5p366-370info:eu-repo/semantics/openAccessBarros,Murilo M. deSilva,Fábio M. daCosta,Anderson G.Ferraz,Gabriel A. e S.Silva,Flávio C. daeng2018-06-05T00:00:00Zoai:scielo:S1415-43662018000500366Revistahttp://www.scielo.br/rbeaaPUBhttps://old.scielo.br/oai/scielo-oai.php||agriambi@agriambi.com.br1807-19291415-4366opendoar:2018-06-05T00:00Revista Brasileira de Engenharia Agrícola e Ambiental (Online) - Universidade Federal de Campina Grande (UFCG)false
dc.title.none.fl_str_mv Use of classifier to determine coffee harvest time by detachment force
title Use of classifier to determine coffee harvest time by detachment force
spellingShingle Use of classifier to determine coffee harvest time by detachment force
Barros,Murilo M. de
coffee cultivation
management
maturation
supervised classifiers
title_short Use of classifier to determine coffee harvest time by detachment force
title_full Use of classifier to determine coffee harvest time by detachment force
title_fullStr Use of classifier to determine coffee harvest time by detachment force
title_full_unstemmed Use of classifier to determine coffee harvest time by detachment force
title_sort Use of classifier to determine coffee harvest time by detachment force
author Barros,Murilo M. de
author_facet Barros,Murilo M. de
Silva,Fábio M. da
Costa,Anderson G.
Ferraz,Gabriel A. e S.
Silva,Flávio C. da
author_role author
author2 Silva,Fábio M. da
Costa,Anderson G.
Ferraz,Gabriel A. e S.
Silva,Flávio C. da
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Barros,Murilo M. de
Silva,Fábio M. da
Costa,Anderson G.
Ferraz,Gabriel A. e S.
Silva,Flávio C. da
dc.subject.por.fl_str_mv coffee cultivation
management
maturation
supervised classifiers
topic coffee cultivation
management
maturation
supervised classifiers
description ABSTRACT Coffee quality is an essential aspect to increase its commercial value and for the Brazilian coffee business to remain prominent in the world market. Fruit maturity stage at harvest is an important factor that affects the quality and commercial value of the product. Therefore, the objective of this study was to develop a classifier using neural networks to distinguish green coffee fruits from mature coffee fruits, based on the detachment force. Fruit detachment force and the percentage value of the maturity stage were measured during a 75-day harvest window. Collections were carried out biweekly, resulting in five different moments within the harvest period. A classifier was developed using neural networks to distinguish green fruits from mature fruits in the harvest period analyzed. The results show that, in the first half of June, the supervised classified had the highest success percentage in differentiating green fruits from mature fruits, and this period was considered as ideal for a selective harvest under these experimental conditions.
publishDate 2018
dc.date.none.fl_str_mv 2018-05-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
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dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662018000500366
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dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/1807-1929/agriambi.v22n5p366-370
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
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dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Departamento de Engenharia Agrícola - UFCG
publisher.none.fl_str_mv Departamento de Engenharia Agrícola - UFCG
dc.source.none.fl_str_mv Revista Brasileira de Engenharia Agrícola e Ambiental v.22 n.5 2018
reponame:Revista Brasileira de Engenharia Agrícola e Ambiental (Online)
instname:Universidade Federal de Campina Grande (UFCG)
instacron:UFCG
instname_str Universidade Federal de Campina Grande (UFCG)
instacron_str UFCG
institution UFCG
reponame_str Revista Brasileira de Engenharia Agrícola e Ambiental (Online)
collection Revista Brasileira de Engenharia Agrícola e Ambiental (Online)
repository.name.fl_str_mv Revista Brasileira de Engenharia Agrícola e Ambiental (Online) - Universidade Federal de Campina Grande (UFCG)
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