|
|
Registros recuperados : 1 | |
Registros recuperados : 1 | |
|
|
Registro Completo
Biblioteca(s): |
Embrapa Agricultura Digital; Embrapa Uva e Vinho. |
Data corrente: |
02/05/2022 |
Data da última atualização: |
02/05/2022 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
B - 3 |
Autoria: |
SPERANZA, E. A.; GREGO, C. R.; GEBLER, L. |
Afiliação: |
EDUARDO ANTONIO SPERANZA, CNPTIA; CELIA REGINA GREGO, CNPTIA; LUCIANO GEBLER, CNPUV. |
Título: |
Analysis of pest incidence on apple trees validated by unsupervised machine learning algorithms. |
Ano de publicação: |
2022 |
Fonte/Imprenta: |
Engenharia na Agricultura, v. 30, p. 63-74, 2022. |
DOI: |
https://doi.org/10.13083/reveng.v30i1.12919 |
Idioma: |
Português |
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. |
Palavras-Chave: |
Análise geoestatística; Aprendizado de Máquina Não-Supervisionado; Controle de pragas; Geoestatística; Maçãs; Manejo de Pragas; Pomares; Unsupervised Machine Learning. |
Thesaurus NAL: |
Apples; Geostatistics; Orchards; Pest management. |
Categoria do assunto: |
-- F Plantas e Produtos de Origem Vegetal |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1142552/1/AP-Analysis-pest-incidence-2022.pdf
|
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.
Download
Esconder MarcMostrar Marc Completo |
Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
Nenhum registro encontrado para a expressão de busca informada. |
|
|