Identification of patterns for increasing production with decision trees in sugarcane mill data
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Scientia Agrícola (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162019001400281 |
Resumo: | ABSTRACT: Sugarcane mills in Brazil collect a vast amount of data relating to production on an annual basis. The analysis of this type of database is complex, especially when factors relating to varieties, climate, detailed management techniques, and edaphic conditions are taken into account. The aim of this paper was to perform a decision tree analysis of a detailed database from a production unit and to evaluate the actionable patterns found in terms of their usefulness for increasing production. The decision tree revealed interpretable patterns relating to sugarcane yield (R2 = 0.617), certain of which were actionable and had been previously studied and reported in the literature. Based on two actionable patterns relating to soil chemistry, intervention which will increase production by almost 2 % were suitable for recommendation. The method was successful in reproducing the knowledge of experts of the factors which influence sugarcane yield, and the decision trees can support the decision-making process in the context of production and the formulation of hypotheses for specific experiments. |
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Scientia Agrícola (Online) |
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|
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Identification of patterns for increasing production with decision trees in sugarcane mill datadata miningyield variabilityregression treeknowledge discoveryABSTRACT: Sugarcane mills in Brazil collect a vast amount of data relating to production on an annual basis. The analysis of this type of database is complex, especially when factors relating to varieties, climate, detailed management techniques, and edaphic conditions are taken into account. The aim of this paper was to perform a decision tree analysis of a detailed database from a production unit and to evaluate the actionable patterns found in terms of their usefulness for increasing production. The decision tree revealed interpretable patterns relating to sugarcane yield (R2 = 0.617), certain of which were actionable and had been previously studied and reported in the literature. Based on two actionable patterns relating to soil chemistry, intervention which will increase production by almost 2 % were suitable for recommendation. The method was successful in reproducing the knowledge of experts of the factors which influence sugarcane yield, and the decision trees can support the decision-making process in the context of production and the formulation of hypotheses for specific experiments.Escola Superior de Agricultura "Luiz de Queiroz"2019-07-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162019001400281Scientia Agricola v.76 n.4 2019reponame:Scientia Agrícola (Online)instname:Universidade de São Paulo (USP)instacron:USP10.1590/1678-992x-2017-0239info:eu-repo/semantics/openAccessPeloia,Paulo RodriguesBocca,Felipe FerreiraRodrigues,Luiz Henrique Antuneseng2019-03-18T00:00:00Zoai:scielo:S0103-90162019001400281Revistahttp://revistas.usp.br/sa/indexPUBhttps://old.scielo.br/oai/scielo-oai.phpscientia@usp.br||alleoni@usp.br1678-992X0103-9016opendoar:2019-03-18T00:00Scientia Agrícola (Online) - Universidade de São Paulo (USP)false |
dc.title.none.fl_str_mv |
Identification of patterns for increasing production with decision trees in sugarcane mill data |
title |
Identification of patterns for increasing production with decision trees in sugarcane mill data |
spellingShingle |
Identification of patterns for increasing production with decision trees in sugarcane mill data Peloia,Paulo Rodrigues data mining yield variability regression tree knowledge discovery |
title_short |
Identification of patterns for increasing production with decision trees in sugarcane mill data |
title_full |
Identification of patterns for increasing production with decision trees in sugarcane mill data |
title_fullStr |
Identification of patterns for increasing production with decision trees in sugarcane mill data |
title_full_unstemmed |
Identification of patterns for increasing production with decision trees in sugarcane mill data |
title_sort |
Identification of patterns for increasing production with decision trees in sugarcane mill data |
author |
Peloia,Paulo Rodrigues |
author_facet |
Peloia,Paulo Rodrigues Bocca,Felipe Ferreira Rodrigues,Luiz Henrique Antunes |
author_role |
author |
author2 |
Bocca,Felipe Ferreira Rodrigues,Luiz Henrique Antunes |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Peloia,Paulo Rodrigues Bocca,Felipe Ferreira Rodrigues,Luiz Henrique Antunes |
dc.subject.por.fl_str_mv |
data mining yield variability regression tree knowledge discovery |
topic |
data mining yield variability regression tree knowledge discovery |
description |
ABSTRACT: Sugarcane mills in Brazil collect a vast amount of data relating to production on an annual basis. The analysis of this type of database is complex, especially when factors relating to varieties, climate, detailed management techniques, and edaphic conditions are taken into account. The aim of this paper was to perform a decision tree analysis of a detailed database from a production unit and to evaluate the actionable patterns found in terms of their usefulness for increasing production. The decision tree revealed interpretable patterns relating to sugarcane yield (R2 = 0.617), certain of which were actionable and had been previously studied and reported in the literature. Based on two actionable patterns relating to soil chemistry, intervention which will increase production by almost 2 % were suitable for recommendation. The method was successful in reproducing the knowledge of experts of the factors which influence sugarcane yield, and the decision trees can support the decision-making process in the context of production and the formulation of hypotheses for specific experiments. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-07-01 |
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=S0103-90162019001400281 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162019001400281 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1678-992x-2017-0239 |
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 |
Escola Superior de Agricultura "Luiz de Queiroz" |
publisher.none.fl_str_mv |
Escola Superior de Agricultura "Luiz de Queiroz" |
dc.source.none.fl_str_mv |
Scientia Agricola v.76 n.4 2019 reponame:Scientia Agrícola (Online) instname:Universidade de São Paulo (USP) instacron:USP |
instname_str |
Universidade de São Paulo (USP) |
instacron_str |
USP |
institution |
USP |
reponame_str |
Scientia Agrícola (Online) |
collection |
Scientia Agrícola (Online) |
repository.name.fl_str_mv |
Scientia Agrícola (Online) - Universidade de São Paulo (USP) |
repository.mail.fl_str_mv |
scientia@usp.br||alleoni@usp.br |
_version_ |
1748936465117609984 |