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


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2022-06-15

AUTHORS

Alexandre dos Santos, Isabel Carolina de Lima Santos, Jeffersoney Garcia Costa, Zakariyyaa Oumar, Mariane Camargo Bueno, Tarcísio Marcos Macedo Mota Filho, Ronald Zanetti, José Cola Zanuncio

ABSTRACT

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. More... »

PAGES

1-17

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11119-022-09919-x

DOI

http://dx.doi.org/10.1007/s11119-022-09919-x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1148692755


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/07", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Agricultural and Veterinary Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0705", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Forestry Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Laborat\u00f3rio de Fitossanidade (FitLab), Instituto Federal de Mato Grosso, IFMT, P.O. Box 244, 78200- 000, Zip Code, C\u00e1ceres, Mato Grosso, Brasil", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Laborat\u00f3rio de Fitossanidade (FitLab), Instituto Federal de Mato Grosso, IFMT, 78201-380, C\u00e1ceres, Mato Grosso, Brazil", 
            "Laborat\u00f3rio de Fitossanidade (FitLab), Instituto Federal de Mato Grosso, IFMT, P.O. Box 244, 78200- 000, Zip Code, C\u00e1ceres, Mato Grosso, Brasil"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Santos", 
        "givenName": "Alexandre dos", 
        "id": "sg:person.07612226275.25", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07612226275.25"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Laborat\u00f3rio de Fitossanidade (FitLab), Instituto Federal de Mato Grosso, IFMT, 78201-380, C\u00e1ceres, Mato Grosso, Brazil", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Laborat\u00f3rio de Fitossanidade (FitLab), Instituto Federal de Mato Grosso, IFMT, 78201-380, C\u00e1ceres, Mato Grosso, Brazil"
          ], 
          "type": "Organization"
        }, 
        "familyName": "de Lima Santos", 
        "givenName": "Isabel Carolina", 
        "id": "sg:person.013543363117.56", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013543363117.56"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Laborat\u00f3rio de Fitossanidade (FitLab), Instituto Federal de Mato Grosso, IFMT, 78201-380, C\u00e1ceres, Mato Grosso, Brazil", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Laborat\u00f3rio de Fitossanidade (FitLab), Instituto Federal de Mato Grosso, IFMT, 78201-380, C\u00e1ceres, Mato Grosso, Brazil"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Costa", 
        "givenName": "Jeffersoney Garcia", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "School of Geography, Archaeology and Environmental Studies, University of Witwatersrand, Johannesburg, South Africa", 
          "id": "http://www.grid.ac/institutes/grid.11951.3d", 
          "name": [
            "School of Geography, Archaeology and Environmental Studies, University of Witwatersrand, Johannesburg, South Africa"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Oumar", 
        "givenName": "Zakariyyaa", 
        "id": "sg:person.010407606675.43", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010407606675.43"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Departamento de Pesquisa Florestal, KLABIN S/A, Avenida Brasil 26, 84275-000, Tel\u00eamaco Borba, Paran\u00e1, Brazil", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Departamento de Pesquisa Florestal, KLABIN S/A, Avenida Brasil 26, 84275-000, Tel\u00eamaco Borba, Paran\u00e1, Brazil"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Bueno", 
        "givenName": "Mariane Camargo", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Departamento de Produ\u00e7\u00e3o Vegetal, Faculdade de Ci\u00eancias Agron\u00f4micas, UNESP, Caixa Postal 237, 18603-970, Botucatu, S\u00e3o Paulo, Brazil", 
          "id": "http://www.grid.ac/institutes/grid.410543.7", 
          "name": [
            "Departamento de Produ\u00e7\u00e3o Vegetal, Faculdade de Ci\u00eancias Agron\u00f4micas, UNESP, Caixa Postal 237, 18603-970, Botucatu, S\u00e3o Paulo, Brazil"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Mota Filho", 
        "givenName": "Tarc\u00edsio Marcos Macedo", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Departamento de Entomologia, Universidade Federal de Lavras, 37200-900, Lavras, Minas Gerais, Brazil", 
          "id": "http://www.grid.ac/institutes/grid.411269.9", 
          "name": [
            "Departamento de Entomologia, Universidade Federal de Lavras, 37200-900, Lavras, Minas Gerais, Brazil"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zanetti", 
        "givenName": "Ronald", 
        "id": "sg:person.01353420233.48", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01353420233.48"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Departamento de Entomologia/BIOAGRO, Universidade Federal de Vi\u00e7osa, 36570-900, Vi\u00e7osa, Minas Gerais, Brazil", 
          "id": "http://www.grid.ac/institutes/grid.12799.34", 
          "name": [
            "Departamento de Entomologia/BIOAGRO, Universidade Federal de Vi\u00e7osa, 36570-900, Vi\u00e7osa, Minas Gerais, Brazil"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zanuncio", 
        "givenName": "Jos\u00e9 Cola", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/978-3-319-62416-7_21", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1090325232", 
          "https://doi.org/10.1007/978-3-319-62416-7_21"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1010933404324", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024739340", 
          "https://doi.org/10.1023/a:1010933404324"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11119-016-9495-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004654316", 
          "https://doi.org/10.1007/s11119-016-9495-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02224026", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001033192", 
          "https://doi.org/10.1007/bf02224026"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s13744-014-0267-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025912094", 
          "https://doi.org/10.1007/s13744-014-0267-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s13595-016-0548-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053242841", 
          "https://doi.org/10.1007/s13595-016-0548-3"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2022-06-15", 
    "datePublishedReg": "2022-06-15", 
    "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 Tel\u00eamaco Borba, Paran\u00e1 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\u2009=\u200997.74\u2009\u00b1\u20090.040%) and 900 trees and 5 variables for the PS (accuracy\u2009=\u200997.42\u2009\u00b1\u20090.026%). 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.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s11119-022-09919-x", 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1135929", 
        "issn": [
          "1385-2256", 
          "1573-1618"
        ], 
        "name": "Precision Agriculture", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }
    ], 
    "keywords": [
      "leaf-cutting ants", 
      "Eucalyptus plantations", 
      "vegetation index", 
      "pest management purposes", 
      "permutational multivariate analysis", 
      "eucalyptus plants", 
      "physiological processes", 
      "remote sensing", 
      "ants", 
      "plant injury", 
      "ants alters", 
      "defoliation", 
      "precision agriculture", 
      "Tel\u00eamaco Borba", 
      "tree canopy", 
      "Paran\u00e1 State", 
      "plants", 
      "Sentinel-2", 
      "plantations", 
      "forest model", 
      "random forest model", 
      "management purposes", 
      "trees", 
      "response variables", 
      "satellite imagery", 
      "nests", 
      "multispectral images", 
      "main factors", 
      "agriculture", 
      "canopy", 
      "Borba", 
      "alters", 
      "Brazil", 
      "index", 
      "RGB-NIR", 
      "input", 
      "variation", 
      "patterns", 
      "municipalities", 
      "response", 
      "variables", 
      "pixel level", 
      "imagery", 
      "levels", 
      "study", 
      "sensing", 
      "factors", 
      "variance", 
      "significance", 
      "analysis", 
      "spectral response", 
      "spectral patterns", 
      "process", 
      "band", 
      "data", 
      "model", 
      "satellite", 
      "aim", 
      "unsupervised machine", 
      "approach", 
      "state", 
      "technique", 
      "multivariate analysis", 
      "purpose", 
      "injury", 
      "PS", 
      "method", 
      "unsupervised method", 
      "k-medoids algorithm", 
      "machine", 
      "k-medoids", 
      "images", 
      "algorithm"
    ], 
    "name": "Canopy defoliation by leaf-cutting ants in eucalyptus plantations inferred by unsupervised machine learning applied to remote sensing", 
    "pagination": "1-17", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1148692755"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s11119-022-09919-x"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s11119-022-09919-x", 
      "https://app.dimensions.ai/details/publication/pub.1148692755"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-09-02T16:08", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220902/entities/gbq_results/article/article_945.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s11119-022-09919-x"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11119-022-09919-x'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11119-022-09919-x'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11119-022-09919-x'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11119-022-09919-x'


 

This table displays all metadata directly associated to this object as RDF triples.

209 TRIPLES      21 PREDICATES      101 URIs      87 LITERALS      4 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s11119-022-09919-x schema:about anzsrc-for:07
2 anzsrc-for:0705
3 schema:author N34162881dd954f998d24ee6c4c53acc0
4 schema:citation sg:pub.10.1007/978-3-319-62416-7_21
5 sg:pub.10.1007/bf02224026
6 sg:pub.10.1007/s11119-016-9495-0
7 sg:pub.10.1007/s13595-016-0548-3
8 sg:pub.10.1007/s13744-014-0267-0
9 sg:pub.10.1023/a:1010933404324
10 schema:datePublished 2022-06-15
11 schema:datePublishedReg 2022-06-15
12 schema: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.
13 schema:genre article
14 schema:isAccessibleForFree false
15 schema:isPartOf sg:journal.1135929
16 schema:keywords Borba
17 Brazil
18 Eucalyptus plantations
19 PS
20 Paraná State
21 RGB-NIR
22 Sentinel-2
23 Telêmaco Borba
24 agriculture
25 aim
26 algorithm
27 alters
28 analysis
29 ants
30 ants alters
31 approach
32 band
33 canopy
34 data
35 defoliation
36 eucalyptus plants
37 factors
38 forest model
39 imagery
40 images
41 index
42 injury
43 input
44 k-medoids
45 k-medoids algorithm
46 leaf-cutting ants
47 levels
48 machine
49 main factors
50 management purposes
51 method
52 model
53 multispectral images
54 multivariate analysis
55 municipalities
56 nests
57 patterns
58 permutational multivariate analysis
59 pest management purposes
60 physiological processes
61 pixel level
62 plant injury
63 plantations
64 plants
65 precision agriculture
66 process
67 purpose
68 random forest model
69 remote sensing
70 response
71 response variables
72 satellite
73 satellite imagery
74 sensing
75 significance
76 spectral patterns
77 spectral response
78 state
79 study
80 technique
81 tree canopy
82 trees
83 unsupervised machine
84 unsupervised method
85 variables
86 variance
87 variation
88 vegetation index
89 schema:name Canopy defoliation by leaf-cutting ants in eucalyptus plantations inferred by unsupervised machine learning applied to remote sensing
90 schema:pagination 1-17
91 schema:productId Na996e72157984cf98174de6de8916521
92 Ne62ba94130524a289258cd0326bd7143
93 schema:sameAs https://app.dimensions.ai/details/publication/pub.1148692755
94 https://doi.org/10.1007/s11119-022-09919-x
95 schema:sdDatePublished 2022-09-02T16:08
96 schema:sdLicense https://scigraph.springernature.com/explorer/license/
97 schema:sdPublisher Ne86c02a103734e0794dbe6caf26e7824
98 schema:url https://doi.org/10.1007/s11119-022-09919-x
99 sgo:license sg:explorer/license/
100 sgo:sdDataset articles
101 rdf:type schema:ScholarlyArticle
102 N062bb05393224dd29ac33363dd15842b rdf:first N23f5f773175748a8b27e3a45b9b54414
103 rdf:rest N8c870a7838564caf8bde48be613a441c
104 N23f5f773175748a8b27e3a45b9b54414 schema:affiliation grid-institutes:None
105 schema:familyName Costa
106 schema:givenName Jeffersoney Garcia
107 rdf:type schema:Person
108 N2683738436894ea98e9b5d9939d35fb4 rdf:first Nce6be45aac1040eda4deb19b13dc028a
109 rdf:rest N4c2afad0a2c647c486100c62663de0aa
110 N286f4db77c9b434c9a8a1b51e6a59d8b schema:affiliation grid-institutes:grid.12799.34
111 schema:familyName Zanuncio
112 schema:givenName José Cola
113 rdf:type schema:Person
114 N2f15f382098a4eddb919108e29201502 rdf:first N286f4db77c9b434c9a8a1b51e6a59d8b
115 rdf:rest rdf:nil
116 N34162881dd954f998d24ee6c4c53acc0 rdf:first sg:person.07612226275.25
117 rdf:rest Nd7f55527d0a7467bab19a7b9b63699bd
118 N432302b1016c4871b60706792abd1033 rdf:first N915034d7964844a5b259f64a0cc563cc
119 rdf:rest N2683738436894ea98e9b5d9939d35fb4
120 N4c2afad0a2c647c486100c62663de0aa rdf:first sg:person.01353420233.48
121 rdf:rest N2f15f382098a4eddb919108e29201502
122 N8c870a7838564caf8bde48be613a441c rdf:first sg:person.010407606675.43
123 rdf:rest N432302b1016c4871b60706792abd1033
124 N915034d7964844a5b259f64a0cc563cc schema:affiliation grid-institutes:None
125 schema:familyName Bueno
126 schema:givenName Mariane Camargo
127 rdf:type schema:Person
128 Na996e72157984cf98174de6de8916521 schema:name dimensions_id
129 schema:value pub.1148692755
130 rdf:type schema:PropertyValue
131 Nce6be45aac1040eda4deb19b13dc028a schema:affiliation grid-institutes:grid.410543.7
132 schema:familyName Mota Filho
133 schema:givenName Tarcísio Marcos Macedo
134 rdf:type schema:Person
135 Nd7f55527d0a7467bab19a7b9b63699bd rdf:first sg:person.013543363117.56
136 rdf:rest N062bb05393224dd29ac33363dd15842b
137 Ne62ba94130524a289258cd0326bd7143 schema:name doi
138 schema:value 10.1007/s11119-022-09919-x
139 rdf:type schema:PropertyValue
140 Ne86c02a103734e0794dbe6caf26e7824 schema:name Springer Nature - SN SciGraph project
141 rdf:type schema:Organization
142 anzsrc-for:07 schema:inDefinedTermSet anzsrc-for:
143 schema:name Agricultural and Veterinary Sciences
144 rdf:type schema:DefinedTerm
145 anzsrc-for:0705 schema:inDefinedTermSet anzsrc-for:
146 schema:name Forestry Sciences
147 rdf:type schema:DefinedTerm
148 sg:journal.1135929 schema:issn 1385-2256
149 1573-1618
150 schema:name Precision Agriculture
151 schema:publisher Springer Nature
152 rdf:type schema:Periodical
153 sg:person.010407606675.43 schema:affiliation grid-institutes:grid.11951.3d
154 schema:familyName Oumar
155 schema:givenName Zakariyyaa
156 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010407606675.43
157 rdf:type schema:Person
158 sg:person.01353420233.48 schema:affiliation grid-institutes:grid.411269.9
159 schema:familyName Zanetti
160 schema:givenName Ronald
161 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01353420233.48
162 rdf:type schema:Person
163 sg:person.013543363117.56 schema:affiliation grid-institutes:None
164 schema:familyName de Lima Santos
165 schema:givenName Isabel Carolina
166 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013543363117.56
167 rdf:type schema:Person
168 sg:person.07612226275.25 schema:affiliation grid-institutes:None
169 schema:familyName Santos
170 schema:givenName Alexandre dos
171 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07612226275.25
172 rdf:type schema:Person
173 sg:pub.10.1007/978-3-319-62416-7_21 schema:sameAs https://app.dimensions.ai/details/publication/pub.1090325232
174 https://doi.org/10.1007/978-3-319-62416-7_21
175 rdf:type schema:CreativeWork
176 sg:pub.10.1007/bf02224026 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001033192
177 https://doi.org/10.1007/bf02224026
178 rdf:type schema:CreativeWork
179 sg:pub.10.1007/s11119-016-9495-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004654316
180 https://doi.org/10.1007/s11119-016-9495-0
181 rdf:type schema:CreativeWork
182 sg:pub.10.1007/s13595-016-0548-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053242841
183 https://doi.org/10.1007/s13595-016-0548-3
184 rdf:type schema:CreativeWork
185 sg:pub.10.1007/s13744-014-0267-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025912094
186 https://doi.org/10.1007/s13744-014-0267-0
187 rdf:type schema:CreativeWork
188 sg:pub.10.1023/a:1010933404324 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024739340
189 https://doi.org/10.1023/a:1010933404324
190 rdf:type schema:CreativeWork
191 grid-institutes:None schema:alternateName Departamento de Pesquisa Florestal, KLABIN S/A, Avenida Brasil 26, 84275-000, Telêmaco Borba, Paraná, Brazil
192 Laboratório de Fitossanidade (FitLab), Instituto Federal de Mato Grosso, IFMT, 78201-380, Cáceres, Mato Grosso, Brazil
193 Laboratório de Fitossanidade (FitLab), Instituto Federal de Mato Grosso, IFMT, P.O. Box 244, 78200- 000, Zip Code, Cáceres, Mato Grosso, Brasil
194 schema:name Departamento de Pesquisa Florestal, KLABIN S/A, Avenida Brasil 26, 84275-000, Telêmaco Borba, Paraná, Brazil
195 Laboratório de Fitossanidade (FitLab), Instituto Federal de Mato Grosso, IFMT, 78201-380, Cáceres, Mato Grosso, Brazil
196 Laboratório de Fitossanidade (FitLab), Instituto Federal de Mato Grosso, IFMT, P.O. Box 244, 78200- 000, Zip Code, Cáceres, Mato Grosso, Brasil
197 rdf:type schema:Organization
198 grid-institutes:grid.11951.3d schema:alternateName School of Geography, Archaeology and Environmental Studies, University of Witwatersrand, Johannesburg, South Africa
199 schema:name School of Geography, Archaeology and Environmental Studies, University of Witwatersrand, Johannesburg, South Africa
200 rdf:type schema:Organization
201 grid-institutes:grid.12799.34 schema:alternateName Departamento de Entomologia/BIOAGRO, Universidade Federal de Viçosa, 36570-900, Viçosa, Minas Gerais, Brazil
202 schema:name Departamento de Entomologia/BIOAGRO, Universidade Federal de Viçosa, 36570-900, Viçosa, Minas Gerais, Brazil
203 rdf:type schema:Organization
204 grid-institutes:grid.410543.7 schema:alternateName Departamento de Produção Vegetal, Faculdade de Ciências Agronômicas, UNESP, Caixa Postal 237, 18603-970, Botucatu, São Paulo, Brazil
205 schema:name Departamento de Produção Vegetal, Faculdade de Ciências Agronômicas, UNESP, Caixa Postal 237, 18603-970, Botucatu, São Paulo, Brazil
206 rdf:type schema:Organization
207 grid-institutes:grid.411269.9 schema:alternateName Departamento de Entomologia, Universidade Federal de Lavras, 37200-900, Lavras, Minas Gerais, Brazil
208 schema:name Departamento de Entomologia, Universidade Federal de Lavras, 37200-900, Lavras, Minas Gerais, Brazil
209 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...