Evaluation of rapeseed flowering dynamics for different genotypes with UAV platform and machine learning algorithm View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2022-06-15

AUTHORS

Ziwen Xie, Song Chen, Guizhen Gao, Hao Li, Xiaoming Wu, Lei Meng, Yuntao Ma

ABSTRACT

Rapeseed (Brassica napus L.) is an important oil-bearing cash crop. Effective identification of the rapeseed flowering date is important for yield estimation and disease control. Traditional field measurements of rapeseed flowering are time-consuming, labour-intensive and strongly subjective. In this study, red, green and blue (RGB) images of rapeseed flowering derived from unmanned aerial vehicles (UAVs) were acquired with a total of seventeen available orthomosaic images, covering the whole flowering period for 299 rapeseed varieties. Five different machine learning methods were employed to identify and to extract the flowering areas in each plot. The results suggested that the accuracy of flowering area extraction by the decision tree-based segmentation model (DTSM) was higher than that of naive Bayes, K-nearest neighbours (KNN), random forest (RF) and support vector machine (SVM) in all varieties and flowering dates, with R2 = 0.97 and root mean square error (RMSE) = 0.051 pixels/pixels. Data on the proportion of flowering area and its dynamics showed differences in the time and duration of each flowering date among varieties. All varieties were classified into four clusters based on k-means clustering analysis. There were significant differences in eight phenotypic parameters among the four clusters, especially in the time of maximum flowering ratio and the time entering the early and medium flowering dates. The results from this study could provide a basis for rapeseed breeding based on flowering dynamics. More... »

PAGES

1688-1706

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11119-022-09904-4

DOI

http://dx.doi.org/10.1007/s11119-022-09904-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1148692754


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/2008/0703", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Crop and Pasture Production", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "College of Land Science and Technology, China Agricultural University, 100193, Beijing, China", 
          "id": "http://www.grid.ac/institutes/grid.22935.3f", 
          "name": [
            "College of Land Science and Technology, China Agricultural University, 100193, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Xie", 
        "givenName": "Ziwen", 
        "id": "sg:person.012147075775.64", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012147075775.64"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Institute of Industrial crops, Jiangsu Academy of Agricultural Sciences, 210014, Nanjing, China", 
          "id": "http://www.grid.ac/institutes/grid.454840.9", 
          "name": [
            "Institute of Industrial crops, Jiangsu Academy of Agricultural Sciences, 210014, Nanjing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chen", 
        "givenName": "Song", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, 430062, Wuhan, China", 
          "id": "http://www.grid.ac/institutes/grid.464406.4", 
          "name": [
            "Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, 430062, Wuhan, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Gao", 
        "givenName": "Guizhen", 
        "id": "sg:person.01200061106.54", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01200061106.54"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, 430062, Wuhan, China", 
          "id": "http://www.grid.ac/institutes/grid.464406.4", 
          "name": [
            "Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, 430062, Wuhan, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Li", 
        "givenName": "Hao", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, 430062, Wuhan, China", 
          "id": "http://www.grid.ac/institutes/grid.464406.4", 
          "name": [
            "Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, 430062, Wuhan, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wu", 
        "givenName": "Xiaoming", 
        "id": "sg:person.01021360406.37", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01021360406.37"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Geography, Environment, and Tourism, Western Michigan University, 49008, Kalamazoo, MI, USA", 
          "id": "http://www.grid.ac/institutes/grid.268187.2", 
          "name": [
            "Department of Geography, Environment, and Tourism, Western Michigan University, 49008, Kalamazoo, MI, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Meng", 
        "givenName": "Lei", 
        "id": "sg:person.012403705535.56", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012403705535.56"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "College of Land Science and Technology, China Agricultural University, 100193, Beijing, China", 
          "id": "http://www.grid.ac/institutes/grid.22935.3f", 
          "name": [
            "College of Land Science and Technology, China Agricultural University, 100193, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ma", 
        "givenName": "Yuntao", 
        "id": "sg:person.0616601741.62", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0616601741.62"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1186/s12911-021-01403-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1135370553", 
          "https://doi.org/10.1186/s12911-021-01403-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00039133", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035813223", 
          "https://doi.org/10.1007/bf00039133"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-29807-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035530996", 
          "https://doi.org/10.1007/978-3-642-29807-3"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2022-06-15", 
    "datePublishedReg": "2022-06-15", 
    "description": "Rapeseed (Brassica napus L.) is an important oil-bearing cash crop. Effective identification of the rapeseed flowering date is important for yield estimation and disease control. Traditional field measurements of rapeseed flowering are time-consuming, labour-intensive and strongly subjective. In this study, red, green and blue (RGB) images of rapeseed flowering derived from unmanned aerial vehicles (UAVs) were acquired with a total of seventeen available orthomosaic images, covering the whole flowering period for 299 rapeseed varieties. Five different machine learning methods were employed to identify and to extract the flowering areas in each plot. The results suggested that the accuracy of flowering area extraction by the decision tree-based segmentation model (DTSM) was higher than that of naive Bayes, K-nearest neighbours (KNN), random forest (RF) and support vector machine (SVM) in all varieties and flowering dates, with R2\u2009=\u20090.97 and root mean square error (RMSE)\u2009=\u20090.051 pixels/pixels. Data on the proportion of flowering area and its dynamics showed differences in the time and duration of each flowering date among varieties. All varieties were classified into four clusters based on k-means clustering analysis. There were significant differences in eight phenotypic parameters among the four clusters, especially in the time of maximum flowering ratio and the time entering the early and medium flowering dates. The results from this study could provide a basis for rapeseed breeding based on flowering dynamics.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s11119-022-09904-4", 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1135929", 
        "issn": [
          "1385-2256", 
          "1573-1618"
        ], 
        "name": "Precision Agriculture", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "5", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "23"
      }
    ], 
    "keywords": [
      "unmanned aerial vehicles", 
      "random forest", 
      "root mean square error", 
      "Naive Bayes", 
      "different machine", 
      "segmentation model", 
      "vector machine", 
      "k-means", 
      "aerial vehicles", 
      "UAV platform", 
      "orthomosaic images", 
      "area extraction", 
      "blue (RGB) images", 
      "mean square error", 
      "machine", 
      "nearest neighbours", 
      "traditional field measurements", 
      "effective identification", 
      "yield estimation", 
      "images", 
      "square error", 
      "Bayes", 
      "algorithm", 
      "pixels", 
      "platform", 
      "accuracy", 
      "vehicles", 
      "clusters", 
      "extraction", 
      "variety", 
      "error", 
      "time", 
      "neighbours", 
      "estimation", 
      "model", 
      "data", 
      "results", 
      "method", 
      "evaluation", 
      "area", 
      "identification", 
      "dynamics", 
      "forest", 
      "parameters", 
      "control", 
      "basis", 
      "date", 
      "analysis", 
      "R2", 
      "phenotypic parameters", 
      "study", 
      "field measurements", 
      "measurements", 
      "rapeseed breeding", 
      "ratio", 
      "differences", 
      "plots", 
      "crops", 
      "duration", 
      "cash crops", 
      "period", 
      "rapeseed varieties", 
      "flowering period", 
      "flowering dates", 
      "flowering dynamics", 
      "disease control", 
      "different genotypes", 
      "rapeseed", 
      "flowering", 
      "total", 
      "proportion", 
      "breeding", 
      "genotypes", 
      "significant differences", 
      "whole flowering period"
    ], 
    "name": "Evaluation of rapeseed flowering dynamics for different genotypes with UAV platform and machine learning algorithm", 
    "pagination": "1688-1706", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1148692754"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s11119-022-09904-4"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s11119-022-09904-4", 
      "https://app.dimensions.ai/details/publication/pub.1148692754"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-09-02T16:07", 
    "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_930.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s11119-022-09904-4"
  }
]
 

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/s11119-022-09904-4'

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/s11119-022-09904-4'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11119-022-09904-4'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11119-022-09904-4'


 

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

193 TRIPLES      21 PREDICATES      102 URIs      91 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s11119-022-09904-4 schema:about anzsrc-for:07
2 anzsrc-for:0703
3 schema:author Ncfc89b74564748488adfcec6092ca959
4 schema:citation sg:pub.10.1007/978-3-642-29807-3
5 sg:pub.10.1007/bf00039133
6 sg:pub.10.1186/s12911-021-01403-2
7 schema:datePublished 2022-06-15
8 schema:datePublishedReg 2022-06-15
9 schema:description Rapeseed (Brassica napus L.) is an important oil-bearing cash crop. Effective identification of the rapeseed flowering date is important for yield estimation and disease control. Traditional field measurements of rapeseed flowering are time-consuming, labour-intensive and strongly subjective. In this study, red, green and blue (RGB) images of rapeseed flowering derived from unmanned aerial vehicles (UAVs) were acquired with a total of seventeen available orthomosaic images, covering the whole flowering period for 299 rapeseed varieties. Five different machine learning methods were employed to identify and to extract the flowering areas in each plot. The results suggested that the accuracy of flowering area extraction by the decision tree-based segmentation model (DTSM) was higher than that of naive Bayes, K-nearest neighbours (KNN), random forest (RF) and support vector machine (SVM) in all varieties and flowering dates, with R2 = 0.97 and root mean square error (RMSE) = 0.051 pixels/pixels. Data on the proportion of flowering area and its dynamics showed differences in the time and duration of each flowering date among varieties. All varieties were classified into four clusters based on k-means clustering analysis. There were significant differences in eight phenotypic parameters among the four clusters, especially in the time of maximum flowering ratio and the time entering the early and medium flowering dates. The results from this study could provide a basis for rapeseed breeding based on flowering dynamics.
10 schema:genre article
11 schema:isAccessibleForFree false
12 schema:isPartOf N20d6051eff394ea0ab97b6258e53e34a
13 N53369c2117794e4680bc192fdf38f69b
14 sg:journal.1135929
15 schema:keywords Bayes
16 Naive Bayes
17 R2
18 UAV platform
19 accuracy
20 aerial vehicles
21 algorithm
22 analysis
23 area
24 area extraction
25 basis
26 blue (RGB) images
27 breeding
28 cash crops
29 clusters
30 control
31 crops
32 data
33 date
34 differences
35 different genotypes
36 different machine
37 disease control
38 duration
39 dynamics
40 effective identification
41 error
42 estimation
43 evaluation
44 extraction
45 field measurements
46 flowering
47 flowering dates
48 flowering dynamics
49 flowering period
50 forest
51 genotypes
52 identification
53 images
54 k-means
55 machine
56 mean square error
57 measurements
58 method
59 model
60 nearest neighbours
61 neighbours
62 orthomosaic images
63 parameters
64 period
65 phenotypic parameters
66 pixels
67 platform
68 plots
69 proportion
70 random forest
71 rapeseed
72 rapeseed breeding
73 rapeseed varieties
74 ratio
75 results
76 root mean square error
77 segmentation model
78 significant differences
79 square error
80 study
81 time
82 total
83 traditional field measurements
84 unmanned aerial vehicles
85 variety
86 vector machine
87 vehicles
88 whole flowering period
89 yield estimation
90 schema:name Evaluation of rapeseed flowering dynamics for different genotypes with UAV platform and machine learning algorithm
91 schema:pagination 1688-1706
92 schema:productId N3f00d336868944efad65adfad81507e6
93 Ne65f48645ca64fa9b82b8a2a23b269a8
94 schema:sameAs https://app.dimensions.ai/details/publication/pub.1148692754
95 https://doi.org/10.1007/s11119-022-09904-4
96 schema:sdDatePublished 2022-09-02T16:07
97 schema:sdLicense https://scigraph.springernature.com/explorer/license/
98 schema:sdPublisher N45752c8f84fe40ff8cce1a3a98e94490
99 schema:url https://doi.org/10.1007/s11119-022-09904-4
100 sgo:license sg:explorer/license/
101 sgo:sdDataset articles
102 rdf:type schema:ScholarlyArticle
103 N20d6051eff394ea0ab97b6258e53e34a schema:volumeNumber 23
104 rdf:type schema:PublicationVolume
105 N3f00d336868944efad65adfad81507e6 schema:name dimensions_id
106 schema:value pub.1148692754
107 rdf:type schema:PropertyValue
108 N45752c8f84fe40ff8cce1a3a98e94490 schema:name Springer Nature - SN SciGraph project
109 rdf:type schema:Organization
110 N53369c2117794e4680bc192fdf38f69b schema:issueNumber 5
111 rdf:type schema:PublicationIssue
112 N8336774a53ab477f82c233738fd7433e schema:affiliation grid-institutes:grid.464406.4
113 schema:familyName Li
114 schema:givenName Hao
115 rdf:type schema:Person
116 N98054bb8e1b14b539e40fea8d72e82ec schema:affiliation grid-institutes:grid.454840.9
117 schema:familyName Chen
118 schema:givenName Song
119 rdf:type schema:Person
120 Naf3155d5f5774c0b9a5bca19a7542d0a rdf:first sg:person.01200061106.54
121 rdf:rest Nbcd8bf60cdc74b9291b3017e5ef1ac1a
122 Nbcd8bf60cdc74b9291b3017e5ef1ac1a rdf:first N8336774a53ab477f82c233738fd7433e
123 rdf:rest Nfff31e88a6e545ac958cfc9b4719074e
124 Ncfc89b74564748488adfcec6092ca959 rdf:first sg:person.012147075775.64
125 rdf:rest Nea191858f0cf4474bcb2a3cd91eb57e8
126 Ne0b9ebe1dd9248f48be2e26b6f001832 rdf:first sg:person.0616601741.62
127 rdf:rest rdf:nil
128 Ne65f48645ca64fa9b82b8a2a23b269a8 schema:name doi
129 schema:value 10.1007/s11119-022-09904-4
130 rdf:type schema:PropertyValue
131 Ne8ce3da232aa471faa8124800e840008 rdf:first sg:person.012403705535.56
132 rdf:rest Ne0b9ebe1dd9248f48be2e26b6f001832
133 Nea191858f0cf4474bcb2a3cd91eb57e8 rdf:first N98054bb8e1b14b539e40fea8d72e82ec
134 rdf:rest Naf3155d5f5774c0b9a5bca19a7542d0a
135 Nfff31e88a6e545ac958cfc9b4719074e rdf:first sg:person.01021360406.37
136 rdf:rest Ne8ce3da232aa471faa8124800e840008
137 anzsrc-for:07 schema:inDefinedTermSet anzsrc-for:
138 schema:name Agricultural and Veterinary Sciences
139 rdf:type schema:DefinedTerm
140 anzsrc-for:0703 schema:inDefinedTermSet anzsrc-for:
141 schema:name Crop and Pasture Production
142 rdf:type schema:DefinedTerm
143 sg:journal.1135929 schema:issn 1385-2256
144 1573-1618
145 schema:name Precision Agriculture
146 schema:publisher Springer Nature
147 rdf:type schema:Periodical
148 sg:person.01021360406.37 schema:affiliation grid-institutes:grid.464406.4
149 schema:familyName Wu
150 schema:givenName Xiaoming
151 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01021360406.37
152 rdf:type schema:Person
153 sg:person.01200061106.54 schema:affiliation grid-institutes:grid.464406.4
154 schema:familyName Gao
155 schema:givenName Guizhen
156 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01200061106.54
157 rdf:type schema:Person
158 sg:person.012147075775.64 schema:affiliation grid-institutes:grid.22935.3f
159 schema:familyName Xie
160 schema:givenName Ziwen
161 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012147075775.64
162 rdf:type schema:Person
163 sg:person.012403705535.56 schema:affiliation grid-institutes:grid.268187.2
164 schema:familyName Meng
165 schema:givenName Lei
166 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012403705535.56
167 rdf:type schema:Person
168 sg:person.0616601741.62 schema:affiliation grid-institutes:grid.22935.3f
169 schema:familyName Ma
170 schema:givenName Yuntao
171 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0616601741.62
172 rdf:type schema:Person
173 sg:pub.10.1007/978-3-642-29807-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035530996
174 https://doi.org/10.1007/978-3-642-29807-3
175 rdf:type schema:CreativeWork
176 sg:pub.10.1007/bf00039133 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035813223
177 https://doi.org/10.1007/bf00039133
178 rdf:type schema:CreativeWork
179 sg:pub.10.1186/s12911-021-01403-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1135370553
180 https://doi.org/10.1186/s12911-021-01403-2
181 rdf:type schema:CreativeWork
182 grid-institutes:grid.22935.3f schema:alternateName College of Land Science and Technology, China Agricultural University, 100193, Beijing, China
183 schema:name College of Land Science and Technology, China Agricultural University, 100193, Beijing, China
184 rdf:type schema:Organization
185 grid-institutes:grid.268187.2 schema:alternateName Department of Geography, Environment, and Tourism, Western Michigan University, 49008, Kalamazoo, MI, USA
186 schema:name Department of Geography, Environment, and Tourism, Western Michigan University, 49008, Kalamazoo, MI, USA
187 rdf:type schema:Organization
188 grid-institutes:grid.454840.9 schema:alternateName Institute of Industrial crops, Jiangsu Academy of Agricultural Sciences, 210014, Nanjing, China
189 schema:name Institute of Industrial crops, Jiangsu Academy of Agricultural Sciences, 210014, Nanjing, China
190 rdf:type schema:Organization
191 grid-institutes:grid.464406.4 schema:alternateName Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, 430062, Wuhan, China
192 schema:name Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, 430062, Wuhan, China
193 rdf:type schema:Organization
 




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


...