Canopy defoliation by leaf-cutting ants in eucalyptus plantations inferred by unsupervised machine learning applied to remote sensing

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.creatorSantos, Alexandre dos-
Autor(es): dc.creatorSantos, Isabel Carolina de Lima-
Autor(es): dc.creatorCosta, Jeffersoney Garcia-
Autor(es): dc.creatorOumar, Zakariyyaa-
Autor(es): dc.creatorBueno, Mariane Camargo-
Autor(es): dc.creatorMota Filho, Tarcísio Marcos Macedo-
Autor(es): dc.creatorZanetti, Ronald-
Autor(es): dc.creatorZanuncio, José Cola-
Data de aceite: dc.date.accessioned2026-02-09T11:43:23Z-
Data de disponibilização: dc.date.available2026-02-09T11:43:23Z-
Data de envio: dc.date.issued2022-10-31-
Data de envio: dc.date.issued2022-10-31-
Data de envio: dc.date.issued2021-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/55388-
Fonte completa do material: dc.identifierhttps://doi.org/10.1007/s11119-022-09919-x-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1146024-
Descrição: dc.descriptionDefoliation by leaf-cutting ants alters the physiological processes of plants, and this defoliation can be inferred from satellite imagery used to identify plant injuries. The aim of this study was to evaluate the spectral pattern of defoliation by leaf-cutting ants in eucalyptus plants on a pixel level using unsupervised machine learning techniques applied to remote sensing by satellites. The study was carried out in a eucalyptus plantation in the municipality of Telêmaco Borba, Paraná state, Brazil. The nests of leaf-cutting ants were located and georeferenced. Multispectral images were obtained from the Sentinel-2 (S-2) and planet scope (PS) satellites. The response variables were the RGB-NIR bands and four vegetation indices (VIs). The data obtained from these bands and vegetation indices was separated in an unsupervised method by the k-medoids clustering algorithm and input into a Random Forest (RF) model. The significance of the models was tested with permutational multivariate analysis of variance (PERMANOVA). The k-medoids algorithm classified the spectral response of the RGB-NIR and VIs bands into two main factors of variation in the tree canopy. The models selected were 1200 trees and 6 variables for the S2 satellite (accuracy = 97.74 ± 0.040%) and 900 trees and 5 variables for the PS (accuracy = 97.42 ± 0.026%). The unsupervised machine learning technique, applied to remote sensing, was effective to map defoliation caused by leaf-cutting ants, and this approach can be used in precision agriculture for pest management purposes.-
Idioma: dc.languageen-
Publicador: dc.publisherSpringer-
Direitos: dc.rightsrestrictAccess-
???dc.source???: dc.sourcePrecision Agriculture-
Palavras-chave: dc.subjectForest entomology-
Palavras-chave: dc.subjectForest protection-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectLeaf-cutting ants-
Palavras-chave: dc.subjectRemote sensing-
Palavras-chave: dc.subjectPest management-
Palavras-chave: dc.subjectEntomologia florestal-
Palavras-chave: dc.subjectProteção florestal-
Palavras-chave: dc.subjectAprendizado de máquina-
Palavras-chave: dc.subjectFormigas cortadeiras-
Palavras-chave: dc.subjectSensoriamento remoto-
Palavras-chave: dc.subjectManejo de pragas-
Título: dc.titleCanopy defoliation by leaf-cutting ants in eucalyptus plantations inferred by unsupervised machine learning applied to remote sensing-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

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