{"id":"https://openalex.org/W4415524711","doi":"https://doi.org/10.1109/mlsp62443.2025.11204237","title":"Deep Learning Based Polyculture Structural Phenotyping","display_name":"Deep Learning Based Polyculture Structural Phenotyping","publication_year":2025,"publication_date":"2025-08-31","ids":{"openalex":"https://openalex.org/W4415524711","doi":"https://doi.org/10.1109/mlsp62443.2025.11204237"},"language":null,"primary_location":{"id":"doi:10.1109/mlsp62443.2025.11204237","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp62443.2025.11204237","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 35th International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"},"type":"article","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/A5103075625","display_name":"Arif Y\u0131lmaz","orcid":"https://orcid.org/0000-0001-6619-1469"},"institutions":[{"id":"https://openalex.org/I4210105785","display_name":"Intelligent Systems Research (United States)","ror":"https://ror.org/01reevc91","country_code":"US","type":"company","lineage":["https://openalex.org/I4210105785"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Arif Y\u0131lmaz","raw_affiliation_strings":["Boǧaziçi University,Intelligent Systems Laboratory,Department of Electrical & Electronics Engineering,Istanbul,Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Boǧaziçi University,Intelligent Systems Laboratory,Department of Electrical & Electronics Engineering,Istanbul,Turkey","institution_ids":["https://openalex.org/I4210105785"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056675916","display_name":"Yavuzhan Erdem","orcid":null},"institutions":[{"id":"https://openalex.org/I4210105785","display_name":"Intelligent Systems Research (United States)","ror":"https://ror.org/01reevc91","country_code":"US","type":"company","lineage":["https://openalex.org/I4210105785"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yavuzhan Erdem","raw_affiliation_strings":["Boǧaziçi University,Intelligent Systems Laboratory,Department of Electrical & Electronics Engineering,Istanbul,Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Boǧaziçi University,Intelligent Systems Laboratory,Department of Electrical & Electronics Engineering,Istanbul,Turkey","institution_ids":["https://openalex.org/I4210105785"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081079933","display_name":"H. I\u015f\u0131l Bozma","orcid":"https://orcid.org/0000-0002-0903-2979"},"institutions":[{"id":"https://openalex.org/I4210105785","display_name":"Intelligent Systems Research (United States)","ror":"https://ror.org/01reevc91","country_code":"US","type":"company","lineage":["https://openalex.org/I4210105785"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"H. I\u015f\u0131l Bozma","raw_affiliation_strings":["Boǧaziçi University,Intelligent Systems Laboratory,Department of Electrical & Electronics Engineering,Istanbul,Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Boǧaziçi University,Intelligent Systems Laboratory,Department of Electrical & Electronics Engineering,Istanbul,Turkey","institution_ids":["https://openalex.org/I4210105785"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"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":0.32175723,"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":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14010","display_name":"Food Industry and Aquatic Biology","score":0.504800021648407,"subfield":{"id":"https://openalex.org/subfields/1106","display_name":"Food 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/T14010","display_name":"Food Industry and Aquatic Biology","score":0.504800021648407,"subfield":{"id":"https://openalex.org/subfields/1106","display_name":"Food 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/polyculture","display_name":"Polyculture","score":0.9362000226974487},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.4936999976634979},{"id":"https://openalex.org/keywords/crop-loss","display_name":"Crop loss","score":0.45840001106262207},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4514999985694885},{"id":"https://openalex.org/keywords/phenomics","display_name":"Phenomics","score":0.4429999887943268},{"id":"https://openalex.org/keywords/canopy","display_name":"Canopy","score":0.3813999891281128}],"concepts":[{"id":"https://openalex.org/C68701300","wikidata":"https://www.wikidata.org/wiki/Q2739133","display_name":"Polyculture","level":4,"score":0.9362000226974487},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6007000207901001},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5440999865531921},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.4936999976634979},{"id":"https://openalex.org/C2992336426","wikidata":"https://www.wikidata.org/wiki/Q5187949","display_name":"Crop loss","level":3,"score":0.45840001106262207},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4514999985694885},{"id":"https://openalex.org/C98108635","wikidata":"https://www.wikidata.org/wiki/Q6497275","display_name":"Phenomics","level":5,"score":0.4429999887943268},{"id":"https://openalex.org/C101000010","wikidata":"https://www.wikidata.org/wiki/Q5033434","display_name":"Canopy","level":2,"score":0.3813999891281128},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.38109999895095825},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3513999879360199},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.27149999141693115},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2639000117778778},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.26170000433921814},{"id":"https://openalex.org/C59519942","wikidata":"https://www.wikidata.org/wiki/Q650665","display_name":"Drone","level":2,"score":0.2513999938964844},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mlsp62443.2025.11204237","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp62443.2025.11204237","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 35th International Workshop on Machine Learning for Signal Processing (MLSP)","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":22,"referenced_works":["https://openalex.org/W1998686312","https://openalex.org/W2541344361","https://openalex.org/W2620819300","https://openalex.org/W2940522640","https://openalex.org/W2948594986","https://openalex.org/W2960779155","https://openalex.org/W2968907940","https://openalex.org/W2976292222","https://openalex.org/W3046517933","https://openalex.org/W3093134863","https://openalex.org/W3134010067","https://openalex.org/W3134390481","https://openalex.org/W3195047309","https://openalex.org/W3215736044","https://openalex.org/W4283574546","https://openalex.org/W4318817220","https://openalex.org/W4322774198","https://openalex.org/W4383097431","https://openalex.org/W4385824676","https://openalex.org/W4387301011","https://openalex.org/W4390874575","https://openalex.org/W4400062053"],"related_works":[],"abstract_inverted_index":{"Structural":[0],"phenotyping":[1],"in":[2,81,100],"polyculture":[3,51,91,103],"farming":[4],"is":[5],"challenging":[6],"due":[7],"to":[8],"the":[9,95],"morphological":[10],"diversity":[11],"across":[12,74],"plant":[13,43],"species.":[14],"We":[15,85],"propose":[16],"a":[17,38,49,88,101],"novel":[18],"end-to-end":[19],"pipeline":[20,72],"for":[21,42,64],"autonomous":[22],"estimation":[23],"of":[24],"four":[25],"structural":[26,66],"traits-length,":[27],"width,":[28],"height,":[29],"and":[30,45,93],"canopy":[31],"area-from":[32],"RGB-D":[33,52,90],"data.":[34],"The":[35],"system":[36],"employs":[37],"deep":[39],"learning":[40],"model":[41],"detection":[44],"segmentation,":[46],"trained":[47],"on":[48],"custom-collected":[50],"dataset":[53,92],"spanning":[54],"two":[55],"months.":[56],"Segmented":[57],"data":[58],"are":[59],"converted":[60],"into":[61],"point":[62],"clouds":[63],"per-plant":[65],"analysis.":[67],"Unlike":[68],"prior":[69],"methods,":[70],"our":[71],"generalizes":[73],"multiple":[75],"species,":[76],"enabling":[77],"accurate":[78],"phenotype":[79],"extraction":[80],"diverse":[82],"crop":[83],"beds.":[84],"also":[86],"introduce":[87],"new":[89],"validate":[94],"pipeline's":[96],"performance":[97],"through":[98],"experiments":[99],"real-world":[102],"garden,":[104],"demonstrating":[105],"robust,":[106],"species-agnostic":[107],"phenotyping.":[108]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-24T00:00:00"}