Use of classifier to determine coffee harvest time by detachment force
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , |
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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Revista Brasileira de Engenharia Agrícola e Ambiental (Online) |
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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 |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662018000500366 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662018000500366 |
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 |
eu_rights_str_mv |
openAccess |
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) |
repository.mail.fl_str_mv |
||agriambi@agriambi.com.br |
_version_ |
1750297686036185088 |