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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital; Embrapa Uva e Vinho. |
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Data corrente: |
02/05/2022 |
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Data da última atualização: |
02/05/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
SPERANZA, E. A.; GREGO, C. R.; GEBLER, L. |
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Afiliação: |
EDUARDO ANTONIO SPERANZA, CNPTIA; CELIA REGINA GREGO, CNPTIA; LUCIANO GEBLER, CNPUV. |
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Título: |
Analysis of pest incidence on apple trees validated by unsupervised machine learning algorithms. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Engenharia na Agricultura, v. 30, p. 63-74, 2022. |
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DOI: |
https://doi.org/10.13083/reveng.v30i1.12919 |
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Idioma: |
Português |
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Conteúdo: |
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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Palavras-Chave: |
Análise geoestatística; Aprendizado de Máquina Não-Supervisionado; Controle de pragas; Geoestatística; Maçãs; Manejo de Pragas; Pomares; Unsupervised Machine Learning. |
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Thesaurus Nal: |
Apples; Geostatistics; Orchards; Pest management. |
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Categoria do assunto: |
--
F Plantas e Produtos de Origem Vegetal |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1142552/1/AP-Analysis-pest-incidence-2022.pdf
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Marc: |
LEADER 02221naa a2200301 a 4500
001 2142552
005 2022-05-02
008 2022 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.13083/reveng.v30i1.12919$2DOI
100 1 $aSPERANZA, E. A.
245 $aAnalysis of pest incidence on apple trees validated by unsupervised machine learning algorithms.$h[electronic resource]
260 $c2022
520 $aABSTRACT. 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.
650 $aApples
650 $aGeostatistics
650 $aOrchards
650 $aPest management
653 $aAnálise geoestatística
653 $aAprendizado de Máquina Não-Supervisionado
653 $aControle de pragas
653 $aGeoestatística
653 $aMaçãs
653 $aManejo de Pragas
653 $aPomares
653 $aUnsupervised Machine Learning
700 1 $aGREGO, C. R.
700 1 $aGEBLER, L.
773 $tEngenharia na Agricultura$gv. 30, p. 63-74, 2022.
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Embrapa Agricultura Digital (CNPTIA) |
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