{"id":"https://openalex.org/W2758256354","doi":"https://doi.org/10.1109/icip.2017.8297010","title":"Plant leaf segmentation for estimating phenotypic traits","display_name":"Plant leaf segmentation for estimating phenotypic traits","publication_year":2017,"publication_date":"2017-09-01","ids":{"openalex":"https://openalex.org/W2758256354","doi":"https://doi.org/10.1109/icip.2017.8297010","mag":"2758256354"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2017.8297010","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2017.8297010","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Image Processing (ICIP)","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/A5100321260","display_name":"Yuhao Chen","orcid":"https://orcid.org/0000-0001-6094-0545"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuhao Chen","raw_affiliation_strings":["Video and Image Processing Laboratory (VIPER), Purdue University, West Lafayette, Indiana, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Video and Image Processing Laboratory (VIPER), Purdue University, West Lafayette, Indiana, USA","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046343572","display_name":"Javier Ribera","orcid":"https://orcid.org/0000-0002-0161-1263"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Javier Ribera","raw_affiliation_strings":["Video and Image Processing Laboratory (VIPER), Purdue University, West Lafayette, Indiana, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Video and Image Processing Laboratory (VIPER), Purdue University, West Lafayette, Indiana, USA","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023791481","display_name":"Christopher R. Boomsma","orcid":null},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Christopher Boomsma","raw_affiliation_strings":["Department of Agronomy, Purdue University, West Lafayette, Indiana, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Agronomy, Purdue University, West Lafayette, Indiana, USA","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089688702","display_name":"Edward J. Delp","orcid":"https://orcid.org/0000-0002-2909-7323"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Edward J. Delp","raw_affiliation_strings":["Video and Image Processing Laboratory (VIPER), Purdue University, West Lafayette, Indiana, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Video and Image Processing Laboratory (VIPER), Purdue University, West Lafayette, Indiana, USA","institution_ids":["https://openalex.org/I219193219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I219193219"],"apc_list":null,"apc_paid":null,"fwci":6.227,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.96364574,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"3884","last_page":"3888"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14365","display_name":"Leaf Properties and Growth Measurement","score":0.9995999932289124,"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"}},"topics":[{"id":"https://openalex.org/T14365","display_name":"Leaf Properties and Growth Measurement","score":0.9995999932289124,"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":0.9987999796867371,"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"}},{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9975000023841858,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/sorghum","display_name":"Sorghum","score":0.6934149265289307},{"id":"https://openalex.org/keywords/leaf-area-index","display_name":"Leaf area index","score":0.5979472398757935},{"id":"https://openalex.org/keywords/crop","display_name":"Crop","score":0.595257043838501},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5514345765113831},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.5186861753463745},{"id":"https://openalex.org/keywords/phenotypic-trait","display_name":"Phenotypic trait","score":0.5127090215682983},{"id":"https://openalex.org/keywords/sorghum-bicolor","display_name":"Sorghum bicolor","score":0.47118523716926575},{"id":"https://openalex.org/keywords/agronomy","display_name":"Agronomy","score":0.3746201992034912},{"id":"https://openalex.org/keywords/phenotype","display_name":"Phenotype","score":0.34429335594177246},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3223528563976288},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.21236440539360046},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.19191297888755798}],"concepts":[{"id":"https://openalex.org/C2778157034","wikidata":"https://www.wikidata.org/wiki/Q12111","display_name":"Sorghum","level":2,"score":0.6934149265289307},{"id":"https://openalex.org/C25989453","wikidata":"https://www.wikidata.org/wiki/Q446746","display_name":"Leaf area index","level":2,"score":0.5979472398757935},{"id":"https://openalex.org/C137580998","wikidata":"https://www.wikidata.org/wiki/Q235352","display_name":"Crop","level":2,"score":0.595257043838501},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5514345765113831},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.5186861753463745},{"id":"https://openalex.org/C130073038","wikidata":"https://www.wikidata.org/wiki/Q1211967","display_name":"Phenotypic trait","level":4,"score":0.5127090215682983},{"id":"https://openalex.org/C2994122767","wikidata":"https://www.wikidata.org/wiki/Q332062","display_name":"Sorghum bicolor","level":3,"score":0.47118523716926575},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.3746201992034912},{"id":"https://openalex.org/C127716648","wikidata":"https://www.wikidata.org/wiki/Q104053","display_name":"Phenotype","level":3,"score":0.34429335594177246},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3223528563976288},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.21236440539360046},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.19191297888755798},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2017.8297010","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2017.8297010","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W294735983","https://openalex.org/W1928746976","https://openalex.org/W2057890665","https://openalex.org/W2065746463","https://openalex.org/W2067877300","https://openalex.org/W2069693699","https://openalex.org/W2069797029","https://openalex.org/W2072611758","https://openalex.org/W2073754472","https://openalex.org/W2086330580","https://openalex.org/W2102432803","https://openalex.org/W2133059825","https://openalex.org/W2158698691","https://openalex.org/W2161558103","https://openalex.org/W2168036630","https://openalex.org/W2216610202","https://openalex.org/W2342430100","https://openalex.org/W2482453724","https://openalex.org/W2511273853","https://openalex.org/W2811469471","https://openalex.org/W6610638243","https://openalex.org/W6688714797","https://openalex.org/W6753378672"],"related_works":["https://openalex.org/W2800847438","https://openalex.org/W2242197468","https://openalex.org/W2786248754","https://openalex.org/W2888769584","https://openalex.org/W2324600427","https://openalex.org/W4243235142","https://openalex.org/W2549659255","https://openalex.org/W2805804661","https://openalex.org/W2405489085","https://openalex.org/W3131124312"],"abstract_inverted_index":{"In":[0,58],"this":[1,111],"paper":[2],"we":[3],"propose":[4],"a":[5,45,56,59,104],"method":[6],"to":[7,66,82],"segment":[8,83],"individual":[9,71],"leaves":[10,62,85],"of":[11,23,27,47,55,98,117],"crop":[12,31],"plants":[13],"from":[14],"Unmanned":[15],"Aerial":[16],"Vehicle":[17],"(UAV)":[18],"imagery":[19],"for":[20,49],"the":[21,28,84,90,94],"purposes":[22],"deriving":[24],"phenotypic":[25,60,119],"properties":[26],"plant.":[29,57],"The":[30,96],"plant":[32,91],"used":[33,65],"in":[34,86],"our":[35],"study":[36],"is":[37,44,81,101],"sorghum":[38],"[Sorghum":[39],"bicolor":[40],"(L.)":[41],"Moench].":[42],"Phenotyping":[43],"set":[46],"methodologies":[48],"analyzing":[50],"and":[51,74],"obtaining":[52],"characteristic":[53],"traits":[54,68],"study,":[61],"are":[63],"often":[64],"estimate":[67],"such":[69],"as":[70,93],"leaf":[72,100,118],"area":[73],"Leaf":[75],"Area":[76],"Index":[77],"(LAI).":[78],"Our":[79],"approach":[80,112],"polar":[87],"coordinates":[88],"using":[89],"center":[92],"origin.":[95],"shape":[97,105],"each":[99],"estimated":[102],"by":[103],"model.":[106],"Experimental":[107],"results":[108],"indicate":[109],"that":[110],"can":[113],"provide":[114],"good":[115],"estimates":[116],"properties.":[120]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
