Matching the best viewing angle in depth cameras for biomass estimation based on poplar seedling geometry.
Identifieur interne : 001C64 ( Main/Curation ); précédent : 001C63; suivant : 001C65Matching the best viewing angle in depth cameras for biomass estimation based on poplar seedling geometry.
Auteurs : Dionisio Andújar [Espagne] ; César Fernández-Quintanilla [Espagne] ; José Dorado [Espagne]Source :
- Sensors (Basel, Switzerland) [ 1424-8220 ] ; 2015.
Descripteurs français
- KwdFr :
- Agriculture (MeSH), Biomasse (MeSH), Enregistrement sur magnétoscope (instrumentation), Enregistrement sur magnétoscope (méthodes), Feuilles de plante (physiologie), Plant (physiologie), Populus (physiologie), Traitement d'image par ordinateur (instrumentation), Traitement d'image par ordinateur (méthodes).
- MESH :
- méthodes : Enregistrement sur magnétoscope, Traitement d'image par ordinateur.
- physiologie : Feuilles de plante, Plant, Populus.
- instrumentation : Agriculture, Biomasse, Enregistrement sur magnétoscope, Traitement d'image par ordinateur.
English descriptors
- KwdEn :
- MESH :
- instrumentation : Image Processing, Computer-Assisted, Video Recording.
- methods : Image Processing, Computer-Assisted, Video Recording.
- physiology : Plant Leaves, Populus, Seedlings.
- Agriculture, Biomass.
Abstract
In energy crops for biomass production a proper plant structure is important to optimize wood yields. A precise crop characterization in early stages may contribute to the choice of proper cropping techniques. This study assesses the potential of the Microsoft Kinect for Windows v.1 sensor to determine the best viewing angle of the sensor to estimate the plant biomass based on poplar seedling geometry. Kinect Fusion algorithms were used to generate a 3D point cloud from the depth video stream. The sensor was mounted in different positions facing the tree in order to obtain depth (RGB-D) images from different angles. Individuals of two different ages, e.g., one month and one year old, were scanned. Four different viewing angles were compared: top view (0°), 45° downwards view, front view (90°) and ground upwards view (-45°). The ground-truth used to validate the sensor readings consisted of a destructive sampling in which the height, leaf area and biomass (dry weight basis) were measured in each individual plant. The depth image models agreed well with 45°, 90° and -45° measurements in one-year poplar trees. Good correlations (0.88 to 0.92) between dry biomass and the area measured with the Kinect were found. In addition, plant height was accurately estimated with a few centimeters error. The comparison between different viewing angles revealed that top views showed poorer results due to the fact the top leaves occluded the rest of the tree. However, the other views led to good results. Conversely, small poplars showed better correlations with actual parameters from the top view (0°). Therefore, although the Microsoft Kinect for Windows v.1 sensor provides good opportunities for biomass estimation, the viewing angle must be chosen taking into account the developmental stage of the crop and the desired parameters. The results of this study indicate that Kinect is a promising tool for a rapid canopy characterization, i.e., for estimating crop biomass production, with several important advantages: low cost, low power needs and a high frame rate (frames per second) when dynamic measurements are required.
DOI: 10.3390/s150612999
PubMed: 26053748
PubMed Central: PMC4507630
Links toward previous steps (curation, corpus...)
- to stream Main, to step Corpus: Pour aller vers cette notice dans l'étape Curation :001C64
Links to Exploration step
pubmed:26053748Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Matching the best viewing angle in depth cameras for biomass estimation based on poplar seedling geometry.</title><author><name sortKey="Andujar, Dionisio" sort="Andujar, Dionisio" uniqKey="Andujar D" first="Dionisio" last="Andújar">Dionisio Andújar</name><affiliation wicri:level="1"><nlm:affiliation>Institute of Agricultural Sciences, CSIC, Madrid 28006, Spain. dionisioandujar@hotmail.com.</nlm:affiliation><country xml:lang="fr">Espagne</country><wicri:regionArea>Institute of Agricultural Sciences, CSIC, Madrid 28006</wicri:regionArea></affiliation></author><author><name sortKey="Fernandez Quintanilla, Cesar" sort="Fernandez Quintanilla, Cesar" uniqKey="Fernandez Quintanilla C" first="César" last="Fernández-Quintanilla">César Fernández-Quintanilla</name><affiliation wicri:level="1"><nlm:affiliation>Institute of Agricultural Sciences, CSIC, Madrid 28006, Spain. cesar@csic.es.</nlm:affiliation><country xml:lang="fr">Espagne</country><wicri:regionArea>Institute of Agricultural Sciences, CSIC, Madrid 28006</wicri:regionArea></affiliation></author><author><name sortKey="Dorado, Jose" sort="Dorado, Jose" uniqKey="Dorado J" first="José" last="Dorado">José Dorado</name><affiliation wicri:level="1"><nlm:affiliation>Institute of Agricultural Sciences, CSIC, Madrid 28006, Spain. jose.dorado@csic.es.</nlm:affiliation><country xml:lang="fr">Espagne</country><wicri:regionArea>Institute of Agricultural Sciences, CSIC, Madrid 28006</wicri:regionArea></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2015">2015</date><idno type="RBID">pubmed:26053748</idno><idno type="pmid">26053748</idno><idno type="doi">10.3390/s150612999</idno><idno type="pmc">PMC4507630</idno><idno type="wicri:Area/Main/Corpus">001C64</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">001C64</idno><idno type="wicri:Area/Main/Curation">001C64</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Curation">001C64</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Matching the best viewing angle in depth cameras for biomass estimation based on poplar seedling geometry.</title><author><name sortKey="Andujar, Dionisio" sort="Andujar, Dionisio" uniqKey="Andujar D" first="Dionisio" last="Andújar">Dionisio Andújar</name><affiliation wicri:level="1"><nlm:affiliation>Institute of Agricultural Sciences, CSIC, Madrid 28006, Spain. dionisioandujar@hotmail.com.</nlm:affiliation><country xml:lang="fr">Espagne</country><wicri:regionArea>Institute of Agricultural Sciences, CSIC, Madrid 28006</wicri:regionArea></affiliation></author><author><name sortKey="Fernandez Quintanilla, Cesar" sort="Fernandez Quintanilla, Cesar" uniqKey="Fernandez Quintanilla C" first="César" last="Fernández-Quintanilla">César Fernández-Quintanilla</name><affiliation wicri:level="1"><nlm:affiliation>Institute of Agricultural Sciences, CSIC, Madrid 28006, Spain. cesar@csic.es.</nlm:affiliation><country xml:lang="fr">Espagne</country><wicri:regionArea>Institute of Agricultural Sciences, CSIC, Madrid 28006</wicri:regionArea></affiliation></author><author><name sortKey="Dorado, Jose" sort="Dorado, Jose" uniqKey="Dorado J" first="José" last="Dorado">José Dorado</name><affiliation wicri:level="1"><nlm:affiliation>Institute of Agricultural Sciences, CSIC, Madrid 28006, Spain. jose.dorado@csic.es.</nlm:affiliation><country xml:lang="fr">Espagne</country><wicri:regionArea>Institute of Agricultural Sciences, CSIC, Madrid 28006</wicri:regionArea></affiliation></author></analytic><series><title level="j">Sensors (Basel, Switzerland)</title><idno type="eISSN">1424-8220</idno><imprint><date when="2015" type="published">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Agriculture (MeSH)</term><term>Biomass (MeSH)</term><term>Image Processing, Computer-Assisted (instrumentation)</term><term>Image Processing, Computer-Assisted (methods)</term><term>Plant Leaves (physiology)</term><term>Populus (physiology)</term><term>Seedlings (physiology)</term><term>Video Recording (instrumentation)</term><term>Video Recording (methods)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Agriculture (MeSH)</term><term>Biomasse (MeSH)</term><term>Enregistrement sur magnétoscope (instrumentation)</term><term>Enregistrement sur magnétoscope (méthodes)</term><term>Feuilles de plante (physiologie)</term><term>Plant (physiologie)</term><term>Populus (physiologie)</term><term>Traitement d'image par ordinateur (instrumentation)</term><term>Traitement d'image par ordinateur (méthodes)</term></keywords><keywords scheme="MESH" qualifier="instrumentation" xml:lang="en"><term>Image Processing, Computer-Assisted</term><term>Video Recording</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Image Processing, Computer-Assisted</term><term>Video Recording</term></keywords><keywords scheme="MESH" qualifier="méthodes" xml:lang="fr"><term>Enregistrement sur magnétoscope</term><term>Traitement d'image par ordinateur</term></keywords><keywords scheme="MESH" qualifier="physiologie" xml:lang="fr"><term>Feuilles de plante</term><term>Plant</term><term>Populus</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Plant Leaves</term><term>Populus</term><term>Seedlings</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Agriculture</term><term>Biomass</term></keywords><keywords scheme="MESH" qualifier="instrumentation" xml:lang="fr"><term>Agriculture</term><term>Biomasse</term><term>Enregistrement sur magnétoscope</term><term>Traitement d'image par ordinateur</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">In energy crops for biomass production a proper plant structure is important to optimize wood yields. A precise crop characterization in early stages may contribute to the choice of proper cropping techniques. This study assesses the potential of the Microsoft Kinect for Windows v.1 sensor to determine the best viewing angle of the sensor to estimate the plant biomass based on poplar seedling geometry. Kinect Fusion algorithms were used to generate a 3D point cloud from the depth video stream. The sensor was mounted in different positions facing the tree in order to obtain depth (RGB-D) images from different angles. Individuals of two different ages, e.g., one month and one year old, were scanned. Four different viewing angles were compared: top view (0°), 45° downwards view, front view (90°) and ground upwards view (-45°). The ground-truth used to validate the sensor readings consisted of a destructive sampling in which the height, leaf area and biomass (dry weight basis) were measured in each individual plant. The depth image models agreed well with 45°, 90° and -45° measurements in one-year poplar trees. Good correlations (0.88 to 0.92) between dry biomass and the area measured with the Kinect were found. In addition, plant height was accurately estimated with a few centimeters error. The comparison between different viewing angles revealed that top views showed poorer results due to the fact the top leaves occluded the rest of the tree. However, the other views led to good results. Conversely, small poplars showed better correlations with actual parameters from the top view (0°). Therefore, although the Microsoft Kinect for Windows v.1 sensor provides good opportunities for biomass estimation, the viewing angle must be chosen taking into account the developmental stage of the crop and the desired parameters. The results of this study indicate that Kinect is a promising tool for a rapid canopy characterization, i.e., for estimating crop biomass production, with several important advantages: low cost, low power needs and a high frame rate (frames per second) when dynamic measurements are required. </div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">26053748</PMID><DateCompleted><Year>2015</Year><Month>11</Month><Day>30</Day></DateCompleted><DateRevised><Year>2018</Year><Month>11</Month><Day>13</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1424-8220</ISSN><JournalIssue CitedMedium="Internet"><Volume>15</Volume><Issue>6</Issue><PubDate><Year>2015</Year><Month>Jun</Month><Day>04</Day></PubDate></JournalIssue><Title>Sensors (Basel, Switzerland)</Title><ISOAbbreviation>Sensors (Basel)</ISOAbbreviation></Journal><ArticleTitle>Matching the best viewing angle in depth cameras for biomass estimation based on poplar seedling geometry.</ArticleTitle><Pagination><MedlinePgn>12999-3011</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.3390/s150612999</ELocationID><Abstract><AbstractText>In energy crops for biomass production a proper plant structure is important to optimize wood yields. A precise crop characterization in early stages may contribute to the choice of proper cropping techniques. This study assesses the potential of the Microsoft Kinect for Windows v.1 sensor to determine the best viewing angle of the sensor to estimate the plant biomass based on poplar seedling geometry. Kinect Fusion algorithms were used to generate a 3D point cloud from the depth video stream. The sensor was mounted in different positions facing the tree in order to obtain depth (RGB-D) images from different angles. Individuals of two different ages, e.g., one month and one year old, were scanned. Four different viewing angles were compared: top view (0°), 45° downwards view, front view (90°) and ground upwards view (-45°). The ground-truth used to validate the sensor readings consisted of a destructive sampling in which the height, leaf area and biomass (dry weight basis) were measured in each individual plant. The depth image models agreed well with 45°, 90° and -45° measurements in one-year poplar trees. Good correlations (0.88 to 0.92) between dry biomass and the area measured with the Kinect were found. In addition, plant height was accurately estimated with a few centimeters error. The comparison between different viewing angles revealed that top views showed poorer results due to the fact the top leaves occluded the rest of the tree. However, the other views led to good results. Conversely, small poplars showed better correlations with actual parameters from the top view (0°). Therefore, although the Microsoft Kinect for Windows v.1 sensor provides good opportunities for biomass estimation, the viewing angle must be chosen taking into account the developmental stage of the crop and the desired parameters. The results of this study indicate that Kinect is a promising tool for a rapid canopy characterization, i.e., for estimating crop biomass production, with several important advantages: low cost, low power needs and a high frame rate (frames per second) when dynamic measurements are required. </AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Andújar</LastName><ForeName>Dionisio</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>Institute of Agricultural Sciences, CSIC, Madrid 28006, Spain. dionisioandujar@hotmail.com.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Fernández-Quintanilla</LastName><ForeName>César</ForeName><Initials>C</Initials><AffiliationInfo><Affiliation>Institute of Agricultural Sciences, CSIC, Madrid 28006, Spain. cesar@csic.es.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Dorado</LastName><ForeName>José</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Institute of Agricultural Sciences, CSIC, Madrid 28006, Spain. jose.dorado@csic.es.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2015</Year><Month>06</Month><Day>04</Day></ArticleDate></Article><MedlineJournalInfo><Country>Switzerland</Country><MedlineTA>Sensors (Basel)</MedlineTA><NlmUniqueID>101204366</NlmUniqueID><ISSNLinking>1424-8220</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000383" MajorTopicYN="N">Agriculture</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D018533" MajorTopicYN="Y">Biomass</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007091" MajorTopicYN="N">Image Processing, Computer-Assisted</DescriptorName><QualifierName UI="Q000295" MajorTopicYN="N">instrumentation</QualifierName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D018515" MajorTopicYN="N">Plant Leaves</DescriptorName><QualifierName UI="Q000502" MajorTopicYN="N">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D032107" MajorTopicYN="N">Populus</DescriptorName><QualifierName UI="Q000502" MajorTopicYN="Y">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D036226" MajorTopicYN="N">Seedlings</DescriptorName><QualifierName UI="Q000502" MajorTopicYN="Y">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D014741" MajorTopicYN="N">Video Recording</DescriptorName><QualifierName UI="Q000295" MajorTopicYN="N">instrumentation</QualifierName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Kinect</Keyword><Keyword MajorTopicYN="N">angle of view</Keyword><Keyword MajorTopicYN="N">biomass assessment</Keyword><Keyword MajorTopicYN="N">energy crops</Keyword><Keyword MajorTopicYN="N">plant structure characterization</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2015</Year><Month>02</Month><Day>09</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2015</Year><Month>05</Month><Day>29</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2015</Year><Month>05</Month><Day>29</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2015</Year><Month>6</Month><Day>9</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2015</Year><Month>6</Month><Day>9</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2015</Year><Month>12</Month><Day>15</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">26053748</ArticleId><ArticleId IdType="pii">s150612999</ArticleId><ArticleId IdType="doi">10.3390/s150612999</ArticleId><ArticleId IdType="pmc">PMC4507630</ArticleId></ArticleIdList><ReferenceList><Reference><Citation>Sensors (Basel). 2012;12(12):16988-7006</Citation><ArticleIdList><ArticleId IdType="pubmed">23235443</ArticleId></ArticleIdList></Reference><Reference><Citation>J Exp Bot. 2012 Mar;63(5):1799-808</Citation><ArticleIdList><ArticleId IdType="pubmed">22213810</ArticleId></ArticleIdList></Reference><Reference><Citation>Environ Manage. 2008 Jun;41(6):949-58</Citation><ArticleIdList><ArticleId IdType="pubmed">18288519</ArticleId></ArticleIdList></Reference><Reference><Citation>Sensors (Basel). 2011;11(3):2304-18</Citation><ArticleIdList><ArticleId IdType="pubmed">22163740</ArticleId></ArticleIdList></Reference><Reference><Citation>Trends Plant Sci. 2013 Aug;18(8):428-39</Citation><ArticleIdList><ArticleId IdType="pubmed">23706697</ArticleId></ArticleIdList></Reference><Reference><Citation>Sensors (Basel). 2014 Feb 14;14(2):3001-18</Citation><ArticleIdList><ArticleId IdType="pubmed">24534920</ArticleId></ArticleIdList></Reference><Reference><Citation>Annu Rev Plant Biol. 2013;64:267-91</Citation><ArticleIdList><ArticleId IdType="pubmed">23451789</ArticleId></ArticleIdList></Reference><Reference><Citation>Sensors (Basel). 2013;13(12):16216-33</Citation><ArticleIdList><ArticleId IdType="pubmed">24287538</ArticleId></ArticleIdList></Reference><Reference><Citation>Trends Plant Sci. 2003 Jan;8(1):2-6</Citation><ArticleIdList><ArticleId IdType="pubmed">12523992</ArticleId></ArticleIdList></Reference><Reference><Citation>Sensors (Basel). 2011;11(3):2459-77</Citation><ArticleIdList><ArticleId IdType="pubmed">22163749</ArticleId></ArticleIdList></Reference><Reference><Citation>Sensors (Basel). 2011;11(2):2177-94</Citation><ArticleIdList><ArticleId IdType="pubmed">22319405</ArticleId></ArticleIdList></Reference><Reference><Citation>Sensors (Basel). 2013;13(11):14662-75</Citation><ArticleIdList><ArticleId IdType="pubmed">24172283</ArticleId></ArticleIdList></Reference><Reference><Citation>Sensors (Basel). 2012;12(2):1437-54</Citation><ArticleIdList><ArticleId IdType="pubmed">22438718</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Bois/explor/PoplarV1/Data/Main/Curation
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 001C64 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Main/Curation/biblio.hfd -nk 001C64 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Bois
|area= PoplarV1
|flux= Main
|étape= Curation
|type= RBID
|clé= pubmed:26053748
|texte= Matching the best viewing angle in depth cameras for biomass estimation based on poplar seedling geometry.
}}
Pour générer des pages wiki
HfdIndexSelect -h $EXPLOR_AREA/Data/Main/Curation/RBID.i -Sk "pubmed:26053748" \
| HfdSelect -Kh $EXPLOR_AREA/Data/Main/Curation/biblio.hfd \
| NlmPubMed2Wicri -a PoplarV1
| This area was generated with Dilib version V0.6.37. |  |