Analysis of pest incidence on apple trees validated by unsupervised machine learning algorithms.

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dc.contributorEDUARDO ANTONIO SPERANZA, CNPTIA; CELIA REGINA GREGO, CNPTIA; LUCIANO GEBLER, CNPUV.
dc.creatorSPERANZA, E. A.
dc.creatorGREGO, C. R.
dc.creatorGEBLER, L.
dc.date2022-05-02T14:12:25Z
dc.date2022-05-02T14:12:25Z
dc.date2022-05-02
dc.date2022
dc.date.accessioned2026-07-07T04:16:48Z
dc.descriptionABSTRACT. 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.
dc.identifierEngenharia na Agricultura, v. 30, p. 63-74, 2022.
dc.identifierhttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1142552
dc.identifierhttps://doi.org/10.13083/reveng.v30i1.12919
dc.identifier.urihttp://hdl.handle.net/123456789/456284
dc.languagepor
dc.rightsopenAccess
dc.subjectManejo de Pragas
dc.subjectGeoestatística
dc.subjectAnálise geoestatística
dc.subjectAprendizado de Máquina Não-Supervisionado
dc.subjectPomares
dc.subjectMaçãs
dc.subjectControle de pragas
dc.subjectUnsupervised Machine Learning
dc.subjectPest management
dc.subjectGeostatistics
dc.subjectOrchards
dc.subjectApples
dc.titleAnalysis of pest incidence on apple trees validated by unsupervised machine learning algorithms.
dc.typeArtigo de periódico

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