{"id":"https://openalex.org/W4386212494","doi":"https://doi.org/10.1109/siu59756.2023.10223805","title":"SEGMENTATION OF PLANT POINT CLOUDS FOR PHENOTYPING","display_name":"SEGMENTATION OF PLANT POINT CLOUDS FOR PHENOTYPING","publication_year":2023,"publication_date":"2023-07-05","ids":{"openalex":"https://openalex.org/W4386212494","doi":"https://doi.org/10.1109/siu59756.2023.10223805"},"language":"en","primary_location":{"id":"doi:10.1109/siu59756.2023.10223805","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/siu59756.2023.10223805","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 31st Signal Processing and Communications Applications Conference (SIU)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5104161461","display_name":"Ahmetcan Yavuz","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ahmetcan Yavuz","raw_affiliation_strings":["Bo&#x011F;azi&#x00E7;i &#x00DC;niversitesi,Elektrik-Elektronik M&#x00FC;hendisli&#x011F;i"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bo&#x011F;azi&#x00E7;i &#x00DC;niversitesi,Elektrik-Elektronik M&#x00FC;hendisli&#x011F;i","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092705710","display_name":"Berke Arda","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Berke Arda","raw_affiliation_strings":["Bo&#x011F;azi&#x00E7;i &#x00DC;niversitesi,Elektrik-Elektronik M&#x00FC;hendisli&#x011F;i"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bo&#x011F;azi&#x00E7;i &#x00DC;niversitesi,Elektrik-Elektronik M&#x00FC;hendisli&#x011F;i","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001721758","display_name":"Helin Duta\u011fac\u0131","orcid":"https://orcid.org/0000-0003-4528-9277"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Helin Dutagaci","raw_affiliation_strings":["Eski&#x015F;ehir Osmangazi &#x00DC;niversitesi,Elektrik-Elektronik M&#x00FC;hendisli&#x011F;i"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Eski&#x015F;ehir Osmangazi &#x00DC;niversitesi,Elektrik-Elektronik M&#x00FC;hendisli&#x011F;i","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018114635","display_name":"B\u00fclent Sankur","orcid":"https://orcid.org/0000-0001-5208-1533"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"B\u00fclent Sankur","raw_affiliation_strings":["Bo&#x011F;azi&#x00E7;i &#x00DC;niversitesi,Elektrik-Elektronik M&#x00FC;hendisli&#x011F;i"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bo&#x011F;azi&#x00E7;i &#x00DC;niversitesi,Elektrik-Elektronik M&#x00FC;hendisli&#x011F;i","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.15148501,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9955000281333923,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9955000281333923,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9941999912261963,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9922000169754028,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7808212041854858},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.764898419380188},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.7186990976333618},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.697093665599823},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.6390070915222168},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5201371312141418},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48761096596717834},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.47890105843544006},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4257924556732178},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3656243681907654},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3200039565563202}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7808212041854858},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.764898419380188},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.7186990976333618},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.697093665599823},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.6390070915222168},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5201371312141418},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48761096596717834},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.47890105843544006},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4257924556732178},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3656243681907654},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3200039565563202}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/siu59756.2023.10223805","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/siu59756.2023.10223805","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 31st Signal Processing and Communications Applications Conference (SIU)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3016928466","https://openalex.org/W2090763504","https://openalex.org/W4389574804","https://openalex.org/W2936725271","https://openalex.org/W3150655618","https://openalex.org/W3137866197","https://openalex.org/W2741749319","https://openalex.org/W2095030957","https://openalex.org/W2066827917","https://openalex.org/W2884201223"],"abstract_inverted_index":{"In":[0,30],"this":[1],"paper,":[2],"we":[3,34,82],"consider":[4,35],"semantic":[5,26],"segmentation":[6,27,130],"of":[7,20,28,74,136],"3D":[8],"plant":[9,105],"point":[10,54],"clouds.":[11],"We":[12,49,107,123],"propose":[13,50,108],"various":[14],"modifications":[15,127],"to":[16,51,71,87],"improve":[17],"the":[18,31,36,53,63,75,78,129,133,137],"performance":[19,131],"two":[21,57],"main":[22],"state-of-the-art":[23,92],"methods":[24],"for":[25,104,118,121],"plants.":[29],"first":[32,64],"method,":[33],"classical":[37],"machine":[38],"learning":[39,102],"approach":[40,86],"where":[41],"local":[42],"geometric":[43],"features":[44],"are":[45,69],"classified":[46],"via":[47],"SVM.":[48],"process":[52],"cloud":[55],"in":[56,62,77],"stages:":[58],"The":[59,90],"points":[60,114],"classified,":[61],"stage,":[65],"with":[66],"high":[67],"confidence":[68],"removed":[70],"enable":[72],"retraining":[73],"SVM":[76],"second":[79,91],"stage.":[80],"Furthermore,":[81],"apply":[83],"a":[84,98],"multi-resolution":[85],"feature":[88],"extraction.":[89],"method":[93],"is":[94,97],"PointNet++,":[95],"which":[96],"widely":[99],"used":[100],"deep":[101],"architecture":[103],"segmentation.":[106],"alternative":[109],"approaches,":[110],"based":[111],"on":[112,132],"critical":[113],"and":[115],"guide":[116],"points,":[117],"data":[119,139],"preprocessing":[120],"PointNet++.":[122],"observed":[124],"that":[125],"these":[126],"improved":[128],"tomato":[134],"models":[135],"Pheno4D":[138],"set.":[140]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
