Analysis of pest incidence on apple trees validated by unsupervised machine learning algorithms.
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
Texto Completo: | http://www.alice.cnptia.embrapa.br/alice/handle/doc/1142552 https://doi.org/10.13083/reveng.v30i1.12919 |
Resumo: | ABSTRACT. Integrated pest control is a practice commonly used in apple orchards in southern Brazil. This type of management is an important tool to help improve quality and increase yields. This study aimed to identify areas with higher and lower incidence of aerial pests in a commercial apple orchard, regarding data collected from three different crops using georeferenced traps. Geostatistical analyses were performed, based on the modeling of semivariograms and spatial interpolation using the kriging method; and clustering, based on specific unsupervised machine learning algorithms for count data. The algorithms were selected from measures of stability, connectivity and homogeneity, seeking to identify areas with different incidence of pests that could help farmer decision making regarding insect population control using pesticides. The geostatistical analysis verified the presence of individual pest infestations in specific sites of the study area. Additionally, the analysis using machine learning allowed the identification of areas with incidence above the average for all analyzed pests, especially in the central area of the map. The process of evaluation described in this study can serve as an aid for risk analysis, promoting management benefits and reducing cost in the farms. |
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Analysis of pest incidence on apple trees validated by unsupervised machine learning algorithms.Manejo de PragasGeoestatísticaAnálise geoestatísticaAprendizado de Máquina Não-SupervisionadoPomaresMaçãsControle de pragasUnsupervised Machine LearningPest managementGeostatisticsOrchardsApplesABSTRACT. Integrated pest control is a practice commonly used in apple orchards in southern Brazil. This type of management is an important tool to help improve quality and increase yields. This study aimed to identify areas with higher and lower incidence of aerial pests in a commercial apple orchard, regarding data collected from three different crops using georeferenced traps. Geostatistical analyses were performed, based on the modeling of semivariograms and spatial interpolation using the kriging method; and clustering, based on specific unsupervised machine learning algorithms for count data. The algorithms were selected from measures of stability, connectivity and homogeneity, seeking to identify areas with different incidence of pests that could help farmer decision making regarding insect population control using pesticides. The geostatistical analysis verified the presence of individual pest infestations in specific sites of the study area. Additionally, the analysis using machine learning allowed the identification of areas with incidence above the average for all analyzed pests, especially in the central area of the map. The process of evaluation described in this study can serve as an aid for risk analysis, promoting management benefits and reducing cost in the farms.EDUARDO ANTONIO SPERANZA, CNPTIA; CELIA REGINA GREGO, CNPTIA; LUCIANO GEBLER, CNPUV.SPERANZA, E. A.GREGO, C. R.GEBLER, L.2022-05-02T14:12:25Z2022-05-02T14:12:25Z2022-05-022022info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleEngenharia na Agricultura, v. 30, p. 63-74, 2022.http://www.alice.cnptia.embrapa.br/alice/handle/doc/1142552https://doi.org/10.13083/reveng.v30i1.12919porinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPA2022-05-02T14:12:34Zoai:www.alice.cnptia.embrapa.br:doc/1142552Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestopendoar:21542022-05-02T14:12:34falseRepositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542022-05-02T14:12:34Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false |
dc.title.none.fl_str_mv |
Analysis of pest incidence on apple trees validated by unsupervised machine learning algorithms. |
title |
Analysis of pest incidence on apple trees validated by unsupervised machine learning algorithms. |
spellingShingle |
Analysis of pest incidence on apple trees validated by unsupervised machine learning algorithms. SPERANZA, E. A. Manejo de Pragas Geoestatística Análise geoestatística Aprendizado de Máquina Não-Supervisionado Pomares Maçãs Controle de pragas Unsupervised Machine Learning Pest management Geostatistics Orchards Apples |
title_short |
Analysis of pest incidence on apple trees validated by unsupervised machine learning algorithms. |
title_full |
Analysis of pest incidence on apple trees validated by unsupervised machine learning algorithms. |
title_fullStr |
Analysis of pest incidence on apple trees validated by unsupervised machine learning algorithms. |
title_full_unstemmed |
Analysis of pest incidence on apple trees validated by unsupervised machine learning algorithms. |
title_sort |
Analysis of pest incidence on apple trees validated by unsupervised machine learning algorithms. |
author |
SPERANZA, E. A. |
author_facet |
SPERANZA, E. A. GREGO, C. R. GEBLER, L. |
author_role |
author |
author2 |
GREGO, C. R. GEBLER, L. |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
EDUARDO ANTONIO SPERANZA, CNPTIA; CELIA REGINA GREGO, CNPTIA; LUCIANO GEBLER, CNPUV. |
dc.contributor.author.fl_str_mv |
SPERANZA, E. A. GREGO, C. R. GEBLER, L. |
dc.subject.por.fl_str_mv |
Manejo de Pragas Geoestatística Análise geoestatística Aprendizado de Máquina Não-Supervisionado Pomares Maçãs Controle de pragas Unsupervised Machine Learning Pest management Geostatistics Orchards Apples |
topic |
Manejo de Pragas Geoestatística Análise geoestatística Aprendizado de Máquina Não-Supervisionado Pomares Maçãs Controle de pragas Unsupervised Machine Learning Pest management Geostatistics Orchards Apples |
description |
ABSTRACT. Integrated pest control is a practice commonly used in apple orchards in southern Brazil. This type of management is an important tool to help improve quality and increase yields. This study aimed to identify areas with higher and lower incidence of aerial pests in a commercial apple orchard, regarding data collected from three different crops using georeferenced traps. Geostatistical analyses were performed, based on the modeling of semivariograms and spatial interpolation using the kriging method; and clustering, based on specific unsupervised machine learning algorithms for count data. The algorithms were selected from measures of stability, connectivity and homogeneity, seeking to identify areas with different incidence of pests that could help farmer decision making regarding insect population control using pesticides. The geostatistical analysis verified the presence of individual pest infestations in specific sites of the study area. Additionally, the analysis using machine learning allowed the identification of areas with incidence above the average for all analyzed pests, especially in the central area of the map. The process of evaluation described in this study can serve as an aid for risk analysis, promoting management benefits and reducing cost in the farms. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-05-02T14:12:25Z 2022-05-02T14:12:25Z 2022-05-02 2022 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
Engenharia na Agricultura, v. 30, p. 63-74, 2022. http://www.alice.cnptia.embrapa.br/alice/handle/doc/1142552 https://doi.org/10.13083/reveng.v30i1.12919 |
identifier_str_mv |
Engenharia na Agricultura, v. 30, p. 63-74, 2022. |
url |
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1142552 https://doi.org/10.13083/reveng.v30i1.12919 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa) instacron:EMBRAPA |
instname_str |
Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
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EMBRAPA |
institution |
EMBRAPA |
reponame_str |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
collection |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
repository.name.fl_str_mv |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
repository.mail.fl_str_mv |
cg-riaa@embrapa.br |
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