Ontology type: schema:ScholarlyArticle
2019-12
AUTHORSYu Jiang, Changying Li, Fumiomi Takeda, Elizabeth A. Kramer, Hamid Ashrafi, Jamal Hunter
ABSTRACTSize and shape are important properties of shrub crops such as blueberries, and they can be particularly useful for evaluating bush architecture suited to mechanical harvesting. The overall goal of this study was to develop a 3D imaging approach to measure size-related traits and bush shape that are relevant to mechanical harvesting. 3D point clouds were acquired for 367 bushes from five genotype groups. Point cloud data were preprocessed to obtain clean bush points for characterizing bush architecture, including bush morphology (height, width, and volume), crown size, and shape descriptors (path curve λ and five shape indices). One-dimensional traits (height, width, and crown size) had high correlations (R 2 = 0.88-0.95) between proposed method and manual measurements, whereas bush volume showed relatively lower correlations (R 2 = 0.78-0.85). These correlations suggested that the present approach was accurate in measuring one-dimensional size traits and acceptable in estimating three-dimensional bush volume. Statistical results demonstrated that the five genotype groups were statistically different in crown size and bush shape. The differences matched with human evaluation regarding optimal bush architecture for mechanical harvesting. In particular, a visualization tool could be generated using crown size and path curve λ, which showed great potential of determining bush architecture suitable for mechanical harvesting quickly. Therefore, the processing pipeline of 3D point cloud data presented in this study is an effective tool for blueberry breeding programs (in particular for mechanical harvesting) and farm management. More... »
PAGES43
http://scigraph.springernature.com/pub.10.1038/s41438-019-0123-9
DOIhttp://dx.doi.org/10.1038/s41438-019-0123-9
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1113162109
PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/30962936
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/2008/0801",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Artificial Intelligence and Image Processing",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Information and Computing Sciences",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "University of Georgia",
"id": "https://www.grid.ac/institutes/grid.213876.9",
"name": [
"School of Electrical and Computer Engineering, College of Engineering, The University of Georgia, 30602, Athens, GA, USA"
],
"type": "Organization"
},
"familyName": "Jiang",
"givenName": "Yu",
"type": "Person"
},
{
"affiliation": {
"alternateName": "University of Georgia",
"id": "https://www.grid.ac/institutes/grid.213876.9",
"name": [
"School of Electrical and Computer Engineering, College of Engineering, The University of Georgia, 30602, Athens, GA, USA"
],
"type": "Organization"
},
"familyName": "Li",
"givenName": "Changying",
"type": "Person"
},
{
"affiliation": {
"alternateName": "Agricultural Research Service",
"id": "https://www.grid.ac/institutes/grid.463419.d",
"name": [
"Appalachian Fruit Research Station, United States Department of Agriculture-Agricultural Research Service, 25430, Kearneysville, WV, USA"
],
"type": "Organization"
},
"familyName": "Takeda",
"givenName": "Fumiomi",
"type": "Person"
},
{
"affiliation": {
"alternateName": "University of Georgia",
"id": "https://www.grid.ac/institutes/grid.213876.9",
"name": [
"Department of Agricultural and Applied Economics, College of Agricultural and Environmental Sciences, The University of Georgia, 30602, Athens, GA, USA"
],
"type": "Organization"
},
"familyName": "Kramer",
"givenName": "Elizabeth A.",
"type": "Person"
},
{
"affiliation": {
"alternateName": "North Carolina State University",
"id": "https://www.grid.ac/institutes/grid.40803.3f",
"name": [
"Department of Horticultural Science, North Carolina State University, 27695, Raleigh, NC, USA"
],
"type": "Organization"
},
"familyName": "Ashrafi",
"givenName": "Hamid",
"type": "Person"
},
{
"affiliation": {
"alternateName": "University of Georgia",
"id": "https://www.grid.ac/institutes/grid.213876.9",
"name": [
"Department of Entomology, College of Agricultural and Environmental Sciences, The University of Georgia, 30602, Athens, GA, USA"
],
"type": "Organization"
},
"familyName": "Hunter",
"givenName": "Jamal",
"type": "Person"
}
],
"citation": [
{
"id": "https://doi.org/10.3390/s141120078",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1002490891"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.postharvbio.2009.05.001",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1002895747"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11947-011-0556-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1003298165",
"https://doi.org/10.1007/s11947-011-0556-0"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.compag.2011.09.007",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1003819915"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.envsoft.2016.04.025",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1008114498"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.envsoft.2016.04.025",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1008114498"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.compag.2014.10.003",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1008734033"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bf02228852",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1011781346",
"https://doi.org/10.1007/bf02228852"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.agrformet.2009.04.008",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1014572748"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1080/15538362.2011.619348",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1015349807"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.compag.2013.12.001",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1018480744"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11263-007-0107-3",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1021569711",
"https://doi.org/10.1007/s11263-007-0107-3"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.tplants.2013.09.008",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1022724478"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1093/jxb/erw176",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1022848380"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.scienta.2013.10.028",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1025038764"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1006/cviu.1999.0832",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1025309926"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.3390/rs70404213",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1031551544"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/1471-2105-12-148",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1032029823",
"https://doi.org/10.1186/1471-2105-12-148"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/1471-2105-12-148",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1032029823",
"https://doi.org/10.1186/1471-2105-12-148"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11284-005-0090-5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1032735147",
"https://doi.org/10.1007/s11284-005-0090-5"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1007/s11284-005-0090-5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1032735147"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.biosystemseng.2014.01.008",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1033006364"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1371/journal.pone.0060522",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1034840135"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.3390/f7060127",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1037348384"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.pbi.2016.04.005",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1037898208"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.3791/1856",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1040461839"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1073/pnas.1613593114",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1043835805"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1071/fp16167",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1046702324"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.compag.2014.09.021",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1048297110"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.compag.2016.09.014",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1051612769"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.compag.2016.09.014",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1051612769"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.biosystemseng.2008.10.009",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1052961296"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tro.2012.2200990",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061785404"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.13031/2013.25435",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1064893414"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.13031/2013.5794",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1064905990"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.21273/hortsci11323-16",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1068967925"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.5376/pgt.2010.01.0001",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1072799089"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.3390/rs9020111",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1074238723"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.3389/fpls.2017.00373",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1084434325"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.3390/agronomy7020033",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1085133581"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.3389/fpls.2017.00915",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1085721351"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1080/00103624.2017.1299169",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1085774827"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.pbi.2017.05.006",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1090858916"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.pbi.2017.05.006",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1090858916"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.pbi.2017.05.006",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1090858916"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.3835/plantgenome2017.03.0026",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1092111415"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.scienta.2017.10.006",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1092225539"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.7717/peerj.4088",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1093136906"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.3390/rs9121250",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1093167841"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1201/9781482298000",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1109619223"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/s41438-018-0097-z",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1110346385",
"https://doi.org/10.1038/s41438-018-0097-z"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/s41438-018-0097-z",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1110346385",
"https://doi.org/10.1038/s41438-018-0097-z"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/s41438-018-0097-z",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1110346385",
"https://doi.org/10.1038/s41438-018-0097-z"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/s41438-018-0097-z",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1110346385",
"https://doi.org/10.1038/s41438-018-0097-z"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.21273/horttech.18.1.130",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1110655090"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.21273/horttech.23.4.430",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1110655787"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.21273/jashs.134.1.77",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1111867507"
],
"type": "CreativeWork"
}
],
"datePublished": "2019-12",
"datePublishedReg": "2019-12-01",
"description": "Size and shape are important properties of shrub crops such as blueberries, and they can be particularly useful for evaluating bush architecture suited to mechanical harvesting. The overall goal of this study was to develop a 3D imaging approach to measure size-related traits and bush shape that are relevant to mechanical harvesting. 3D point clouds were acquired for 367 bushes from five genotype groups. Point cloud data were preprocessed to obtain clean bush points for characterizing bush architecture, including bush morphology (height, width, and volume), crown size, and shape descriptors (path curve \u03bb and five shape indices). One-dimensional traits (height, width, and crown size) had high correlations (R 2\u2009=\u20090.88-0.95) between proposed method and manual measurements, whereas bush volume showed relatively lower correlations (R 2\u2009=\u20090.78-0.85). These correlations suggested that the present approach was accurate in measuring one-dimensional size traits and acceptable in estimating three-dimensional bush volume. Statistical results demonstrated that the five genotype groups were statistically different in crown size and bush shape. The differences matched with human evaluation regarding optimal bush architecture for mechanical harvesting. In particular, a visualization tool could be generated using crown size and path curve \u03bb, which showed great potential of determining bush architecture suitable for mechanical harvesting quickly. Therefore, the processing pipeline of 3D point cloud data presented in this study is an effective tool for blueberry breeding programs (in particular for mechanical harvesting) and farm management.",
"genre": "research_article",
"id": "sg:pub.10.1038/s41438-019-0123-9",
"inLanguage": [
"en"
],
"isAccessibleForFree": false,
"isFundedItemOf": [
{
"id": "sg:grant.3951614",
"type": "MonetaryGrant"
}
],
"isPartOf": [
{
"id": "sg:journal.1051681",
"issn": [
"2052-7276"
],
"name": "Horticulture Research",
"type": "Periodical"
},
{
"issueNumber": "1",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "6"
}
],
"name": "3D point cloud data to quantitatively characterize size and shape of shrub crops",
"pagination": "43",
"productId": [
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1038/s41438-019-0123-9"
]
},
{
"name": "readcube_id",
"type": "PropertyValue",
"value": [
"f61d11d83d5acda0e5b6757eab4a2e85e97047d6c3a55b42bf8f7b0d0d2347ff"
]
},
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1113162109"
]
},
{
"name": "nlm_unique_id",
"type": "PropertyValue",
"value": [
"101655540"
]
},
{
"name": "pubmed_id",
"type": "PropertyValue",
"value": [
"30962936"
]
}
],
"sameAs": [
"https://doi.org/10.1038/s41438-019-0123-9",
"https://app.dimensions.ai/details/publication/pub.1113162109"
],
"sdDataset": "articles",
"sdDatePublished": "2019-04-15T09:19",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000376_0000000376/records_56176_00000006.jsonl",
"type": "ScholarlyArticle",
"url": "https://www.nature.com/articles/s41438-019-0123-9"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
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.10.1038/s41438-019-0123-9'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/s41438-019-0123-9'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41438-019-0123-9'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41438-019-0123-9'
This table displays all metadata directly associated to this object as RDF triples.
260 TRIPLES
21 PREDICATES
78 URIs
21 LITERALS
9 BLANK NODES