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: Repositório Institucional da UFLA
Texto Completo: http://repositorio.ufla.br/jspui/handle/1/33694
Resumo: 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.
id UFLA_e7c7d67575947a3149e70e3995fca74f
oai_identifier_str oai:localhost:1/33694
network_acronym_str UFLA
network_name_str Repositório Institucional da UFLA
repository_id_str
spelling Use of classifier to determine coffee harvest time by detachment forceUso de classificador para determinação do momento de colheita do café pela força de desprendimentoCoffee cultivationCoffee - HarvestSupervised classifiersCoffee - MaturationCafeiculturaCafé - ColheitaClassificadores supervisionadosCafé - MaturaçãoCoffee 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.A qualidade do café é um aspecto imprescindível para o aumento do seu valor comercial e para que a cafeicultura brasileira continue com destaque no mercado mundial. O estádio de maturação dos frutos no momento da colheita é um dos fatores importantes que interfere na qualidade e no valor comercial do produto. Com a realização deste trabalho, objetivou-se desenvolver um classificador para determinação do momento de colheita do café pela força de desprendimento. A força de desprendimento dos frutos e o valor percentual do estádio de maturação foram mensurados durante a janela de colheita de 75 dias. As coletas foram realizadas quinzenalmente, resultando em cinco momentos distintos no período de colheita. Um classificador foi desenvolvido a partir de redes neurais para distinguir frutos verdes e cerejas nos momentos de colheita analisados. Os resultados demostraram que a primeira quinzena de junho foi o momento em que o classificador supervisionado apresentou a maior porcentagem de acerto na distinção de frutos verdes e cerejas, sendo este, o momento adequado para realização de uma colheita seletiva para as condições deste experimento.Departamento de Engenharia Agrícola - UFCG2019-04-23T13:04:45Z2019-04-23T13:04:45Z2018-05info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfBARROS, M. M. de et al. Use of classifier to determine coffee harvest time by detachment force. Revista Brasileira de Engenharia Agrícola e Ambiental, Campina Grande, v. 22, n. 5, p. 366-370, May 2018.http://repositorio.ufla.br/jspui/handle/1/33694Revista Brasileira de Engenharia Agrícola e Ambientalreponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessBarros, Murilo M. deSilva, Fábio M. daCosta, Anderson G.Ferraz, Gabriel A. e S.Silva, Flávio C. daeng2019-04-23T13:05:29Zoai:localhost:1/33694Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2019-04-23T13:05:29Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false
dc.title.none.fl_str_mv Use of classifier to determine coffee harvest time by detachment force
Uso de classificador para determinação do momento de colheita do café pela força de desprendimento
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
Coffee - Harvest
Supervised classifiers
Coffee - Maturation
Cafeicultura
Café - Colheita
Classificadores supervisionados
Café - Maturação
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
Coffee - Harvest
Supervised classifiers
Coffee - Maturation
Cafeicultura
Café - Colheita
Classificadores supervisionados
Café - Maturação
topic Coffee cultivation
Coffee - Harvest
Supervised classifiers
Coffee - Maturation
Cafeicultura
Café - Colheita
Classificadores supervisionados
Café - Maturação
description 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
2019-04-23T13:04:45Z
2019-04-23T13:04:45Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv BARROS, M. M. de et al. Use of classifier to determine coffee harvest time by detachment force. Revista Brasileira de Engenharia Agrícola e Ambiental, Campina Grande, v. 22, n. 5, p. 366-370, May 2018.
http://repositorio.ufla.br/jspui/handle/1/33694
identifier_str_mv BARROS, M. M. de et al. Use of classifier to determine coffee harvest time by detachment force. Revista Brasileira de Engenharia Agrícola e Ambiental, Campina Grande, v. 22, n. 5, p. 366-370, May 2018.
url http://repositorio.ufla.br/jspui/handle/1/33694
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
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
reponame:Repositório Institucional da UFLA
instname:Universidade Federal de Lavras (UFLA)
instacron:UFLA
instname_str Universidade Federal de Lavras (UFLA)
instacron_str UFLA
institution UFLA
reponame_str Repositório Institucional da UFLA
collection Repositório Institucional da UFLA
repository.name.fl_str_mv Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)
repository.mail.fl_str_mv nivaldo@ufla.br || repositorio.biblioteca@ufla.br
_version_ 1815439133931208704

Registros relacionados