Canopy defoliation by leaf-cutting ants in eucalyptus plantations inferred by unsupervised machine learning applied to remote sensing
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/s11119-022-09919-x http://hdl.handle.net/11449/241184 |
Resumo: | Defoliation 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. |
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Canopy defoliation by leaf-cutting ants in eucalyptus plantations inferred by unsupervised machine learning applied to remote sensingAttaForest entomologyForest protectionMachine learningDefoliation 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.Laboratório de Fitossanidade (FitLab) Instituto Federal de Mato Grosso IFMT, Mato GrossoSchool of Geography Archaeology and Environmental Studies University of WitwatersrandDepartamento de Pesquisa Florestal KLABIN S/A, Avenida Brasil 26Departamento de Produção Vegetal Faculdade de Ciências Agronômicas UNESP, Caixa Postal 237, São PauloDepartamento de Entomologia Universidade Federal de Lavras, Minas GeraisDepartamento de Entomologia/BIOAGRO Universidade Federal de Viçosa, Minas GeraisLaboratório de Fitossanidade (FitLab) Instituto Federal de Mato Grosso IFMT, P.O. Box 244, Mato GrossoDepartamento de Produção Vegetal Faculdade de Ciências Agronômicas UNESP, Caixa Postal 237, São PauloIFMTUniversity of WitwatersrandKLABIN S/AUniversidade Estadual Paulista (UNESP)Universidade Federal de Lavras (UFLA)Universidade Federal de Viçosa (UFV)Santos, Alexandre dosde Lima Santos, Isabel CarolinaCosta, Jeffersoney GarciaOumar, ZakariyyaaBueno, Mariane CamargoMota Filho, Tarcísio Marcos Macedo [UNESP]Zanetti, RonaldZanuncio, José Cola2023-03-01T20:50:47Z2023-03-01T20:50:47Z2022-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1007/s11119-022-09919-xPrecision Agriculture.1573-16181385-2256http://hdl.handle.net/11449/24118410.1007/s11119-022-09919-x2-s2.0-85132283650Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengPrecision Agricultureinfo:eu-repo/semantics/openAccess2024-04-30T15:55:49Zoai:repositorio.unesp.br:11449/241184Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-04-30T15:55:49Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Canopy defoliation by leaf-cutting ants in eucalyptus plantations inferred by unsupervised machine learning applied to remote sensing |
title |
Canopy defoliation by leaf-cutting ants in eucalyptus plantations inferred by unsupervised machine learning applied to remote sensing |
spellingShingle |
Canopy defoliation by leaf-cutting ants in eucalyptus plantations inferred by unsupervised machine learning applied to remote sensing Santos, Alexandre dos Atta Forest entomology Forest protection Machine learning |
title_short |
Canopy defoliation by leaf-cutting ants in eucalyptus plantations inferred by unsupervised machine learning applied to remote sensing |
title_full |
Canopy defoliation by leaf-cutting ants in eucalyptus plantations inferred by unsupervised machine learning applied to remote sensing |
title_fullStr |
Canopy defoliation by leaf-cutting ants in eucalyptus plantations inferred by unsupervised machine learning applied to remote sensing |
title_full_unstemmed |
Canopy defoliation by leaf-cutting ants in eucalyptus plantations inferred by unsupervised machine learning applied to remote sensing |
title_sort |
Canopy defoliation by leaf-cutting ants in eucalyptus plantations inferred by unsupervised machine learning applied to remote sensing |
author |
Santos, Alexandre dos |
author_facet |
Santos, Alexandre dos de Lima Santos, Isabel Carolina Costa, Jeffersoney Garcia Oumar, Zakariyyaa Bueno, Mariane Camargo Mota Filho, Tarcísio Marcos Macedo [UNESP] Zanetti, Ronald Zanuncio, José Cola |
author_role |
author |
author2 |
de Lima Santos, Isabel Carolina Costa, Jeffersoney Garcia Oumar, Zakariyyaa Bueno, Mariane Camargo Mota Filho, Tarcísio Marcos Macedo [UNESP] Zanetti, Ronald Zanuncio, José Cola |
author2_role |
author author author author author author author |
dc.contributor.none.fl_str_mv |
IFMT University of Witwatersrand KLABIN S/A Universidade Estadual Paulista (UNESP) Universidade Federal de Lavras (UFLA) Universidade Federal de Viçosa (UFV) |
dc.contributor.author.fl_str_mv |
Santos, Alexandre dos de Lima Santos, Isabel Carolina Costa, Jeffersoney Garcia Oumar, Zakariyyaa Bueno, Mariane Camargo Mota Filho, Tarcísio Marcos Macedo [UNESP] Zanetti, Ronald Zanuncio, José Cola |
dc.subject.por.fl_str_mv |
Atta Forest entomology Forest protection Machine learning |
topic |
Atta Forest entomology Forest protection Machine learning |
description |
Defoliation 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. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-01 2023-03-01T20:50:47Z 2023-03-01T20:50:47Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1007/s11119-022-09919-x Precision Agriculture. 1573-1618 1385-2256 http://hdl.handle.net/11449/241184 10.1007/s11119-022-09919-x 2-s2.0-85132283650 |
url |
http://dx.doi.org/10.1007/s11119-022-09919-x http://hdl.handle.net/11449/241184 |
identifier_str_mv |
Precision Agriculture. 1573-1618 1385-2256 10.1007/s11119-022-09919-x 2-s2.0-85132283650 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Precision Agriculture |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1803046180611948544 |