Coffee classification according to its detachment force: a decision tree-based approach

Detalhes bibliográficos
Ano de defesa: 2023
Autor(a) principal: Meneses, Mariana Dias. [UNESP]
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: eng
Instituição de defesa: Universidade Estadual Paulista (Unesp)
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Machine Learning
Selective Mechanized Harvesting
Uneven Maturation
Coffee
Agricultura
Link de acesso: http://hdl.handle.net/11449/242933
Resumo: The world coffee consumption demands high-efficiency crop systems. Consumers appreciate flavor and aroma in this beverage, characteristics that are game-changing in coffee value. A key role to improve this production chain, mechanized harvesting fails in provide coffee fruits selectivity. It means that the industry receives fruits with astringent flavor or fermentation. Because coffee plant has uneven maturation, i.e., green, cherry, and dry fruits, and the harvester settings are generalist, the fruits are detached regardless their maturation stage. The use of Machine Learning techniques improves the traditional agriculture to a digital one, its use in mechanized harvesting enhances selectivity of the coffee fruits. Overall, the present study aimed to classify the coffee fruit detachment force using a Decision Tree Classifier. The experiment was conducted in two field in the Brazilian state of Minas Gerais. A dynamometer was used to measure the detachment force of 23 coffee cultivars. The cultivars were grouped using a cluster algorithm and a Decision Tree classified each group according to the detachment force. The Decision Tree obtained a mean Matthews Correlation Coefficient of 0.81, proving its efficiency in classify the detachment force. Therefore, we proved that Decision Tree can power the mechanized harvesting as a tool to more accurate decision-making settings
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spelling Coffee classification according to its detachment force: a decision tree-based approachClassificação do café de acordo com sua força de desprendimento: uma abordagem baseada em árvore de decisãoMachine LearningSelective Mechanized HarvestingUneven MaturationCoffeeAgriculturaThe world coffee consumption demands high-efficiency crop systems. Consumers appreciate flavor and aroma in this beverage, characteristics that are game-changing in coffee value. A key role to improve this production chain, mechanized harvesting fails in provide coffee fruits selectivity. It means that the industry receives fruits with astringent flavor or fermentation. Because coffee plant has uneven maturation, i.e., green, cherry, and dry fruits, and the harvester settings are generalist, the fruits are detached regardless their maturation stage. The use of Machine Learning techniques improves the traditional agriculture to a digital one, its use in mechanized harvesting enhances selectivity of the coffee fruits. Overall, the present study aimed to classify the coffee fruit detachment force using a Decision Tree Classifier. The experiment was conducted in two field in the Brazilian state of Minas Gerais. A dynamometer was used to measure the detachment force of 23 coffee cultivars. The cultivars were grouped using a cluster algorithm and a Decision Tree classified each group according to the detachment force. The Decision Tree obtained a mean Matthews Correlation Coefficient of 0.81, proving its efficiency in classify the detachment force. Therefore, we proved that Decision Tree can power the mechanized harvesting as a tool to more accurate decision-making settingsO consumo mundial de café exige alta eficiência no sistema da cultura. Consumidores apreciam o sabor e aroma da bebida, características que afetam o valor do café. Peça chave para a cadeia produtiva, a colheita mecanizada falha em promover a seletividade dos frutos. Entregando a indústria, frutos com sabor adstringente ou fermentados. Por causa da maturação irregular na planta do café e dos ajustes generalistas da colhedora, os frutos são derriçados independentemente da sua maturação. O uso de técnicas de Machine Learning melhora a agricultura tradicional para uma agricultura digital, o uso na colheita mecanizada aumenta a seletividade dos frutos colhidos. Visto isso, o presente estudo objetivou classificar a força de desprendimento de frutos do café usando um algoritmo de Árvore de Decisão Classificador. O experimento foi conduzido em duas áreas no estado de Minas Gerais, Brasil. Um dinamômetro foi usado para mensurar a força de desprendimento de 23 cultivares de café. As cultivares foram agrupadas usando um algoritmo de cluster e a Árvore de Decisão classificou cada um dos grupos de acordo com a força de desprendimento. A Árvore de Decisão obteve um coeficiente de correlação Matthews de 0,81, provando a eficiência em classificar a força de desprendimento. Portanto, foi provado que a Árvore de Decisão é uma ferramenta capaz de tornar a tomada de decisão mais acurada, melhorando a colheita mecanizada.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)88887641456/2021-00Universidade Estadual Paulista (Unesp)Silva, Rouverson Pereira daVale, Welington Gonzaga doRolim, Glauco de SouzaUniversidade Estadual Paulista (Unesp)Meneses, Mariana Dias. [UNESP]2023-04-11T20:23:16Z2023-04-11T20:23:16Z2023-02-28info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/11449/24293333004102071P2enginfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESP2025-10-22T05:12:14Zoai:repositorio.unesp.br:11449/242933Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestrepositoriounesp@unesp.bropendoar:29462025-10-22T05:12:14Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Coffee classification according to its detachment force: a decision tree-based approach
Classificação do café de acordo com sua força de desprendimento: uma abordagem baseada em árvore de decisão
title Coffee classification according to its detachment force: a decision tree-based approach
spellingShingle Coffee classification according to its detachment force: a decision tree-based approach
Meneses, Mariana Dias. [UNESP]
Machine Learning
Selective Mechanized Harvesting
Uneven Maturation
Coffee
Agricultura
title_short Coffee classification according to its detachment force: a decision tree-based approach
title_full Coffee classification according to its detachment force: a decision tree-based approach
title_fullStr Coffee classification according to its detachment force: a decision tree-based approach
title_full_unstemmed Coffee classification according to its detachment force: a decision tree-based approach
title_sort Coffee classification according to its detachment force: a decision tree-based approach
author Meneses, Mariana Dias. [UNESP]
author_facet Meneses, Mariana Dias. [UNESP]
author_role author
dc.contributor.none.fl_str_mv Silva, Rouverson Pereira da
Vale, Welington Gonzaga do
Rolim, Glauco de Souza
Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Meneses, Mariana Dias. [UNESP]
dc.subject.por.fl_str_mv Machine Learning
Selective Mechanized Harvesting
Uneven Maturation
Coffee
Agricultura
topic Machine Learning
Selective Mechanized Harvesting
Uneven Maturation
Coffee
Agricultura
description The world coffee consumption demands high-efficiency crop systems. Consumers appreciate flavor and aroma in this beverage, characteristics that are game-changing in coffee value. A key role to improve this production chain, mechanized harvesting fails in provide coffee fruits selectivity. It means that the industry receives fruits with astringent flavor or fermentation. Because coffee plant has uneven maturation, i.e., green, cherry, and dry fruits, and the harvester settings are generalist, the fruits are detached regardless their maturation stage. The use of Machine Learning techniques improves the traditional agriculture to a digital one, its use in mechanized harvesting enhances selectivity of the coffee fruits. Overall, the present study aimed to classify the coffee fruit detachment force using a Decision Tree Classifier. The experiment was conducted in two field in the Brazilian state of Minas Gerais. A dynamometer was used to measure the detachment force of 23 coffee cultivars. The cultivars were grouped using a cluster algorithm and a Decision Tree classified each group according to the detachment force. The Decision Tree obtained a mean Matthews Correlation Coefficient of 0.81, proving its efficiency in classify the detachment force. Therefore, we proved that Decision Tree can power the mechanized harvesting as a tool to more accurate decision-making settings
publishDate 2023
dc.date.none.fl_str_mv 2023-04-11T20:23:16Z
2023-04-11T20:23:16Z
2023-02-28
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/11449/242933
33004102071P2
url http://hdl.handle.net/11449/242933
identifier_str_mv 33004102071P2
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade Estadual Paulista (Unesp)
publisher.none.fl_str_mv Universidade Estadual Paulista (Unesp)
dc.source.none.fl_str_mv reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv repositoriounesp@unesp.br
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