Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1142552
Title: Analysis of pest incidence on apple trees validated by unsupervised machine learning algorithms.
Authors: SPERANZA, E. A.
GREGO, C. R.
GEBLER, L.
Affiliation: EDUARDO ANTONIO SPERANZA, CNPTIA; CELIA REGINA GREGO, CNPTIA; LUCIANO GEBLER, CNPUV.
Date Issued: 2022
Citation: Engenharia na Agricultura, v. 30, p. 63-74, 2022.
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.
NAL Thesaurus: Pest management
Geostatistics
Orchards
Apples
Keywords: Manejo de Pragas
Geoestatística
Análise geoestatística
Aprendizado de Máquina Não-Supervisionado
Pomares
Maçãs
Controle de pragas
Unsupervised Machine Learning
Language: Portugues
DOI: https://doi.org/10.13083/reveng.v30i1.12919
Type of Material: Artigo de periódico
Access: openAccess
Appears in Collections:Artigo em periódico indexado (CNPTIA)

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