{"id":"https://openalex.org/W7105648740","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227395","title":"GPFEnet: a lightweight grid parallel feature extraction net for 3D segmentation in agriculture","display_name":"GPFEnet: a lightweight grid parallel feature extraction net for 3D segmentation in agriculture","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W7105648740","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227395"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11227395","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227395","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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":null,"display_name":"Wenjing Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I89652312","display_name":"Northwest A&F University","ror":"https://ror.org/0051rme32","country_code":"CN","type":"education","lineage":["https://openalex.org/I89652312"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wenjing Zhang","raw_affiliation_strings":["Northwest A&F University,College of Information Engineering,Yangling,China"],"affiliations":[{"raw_affiliation_string":"Northwest A&F University,College of Information Engineering,Yangling,China","institution_ids":["https://openalex.org/I89652312"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Qinghe Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I89652312","display_name":"Northwest A&F University","ror":"https://ror.org/0051rme32","country_code":"CN","type":"education","lineage":["https://openalex.org/I89652312"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qinghe Liu","raw_affiliation_strings":["Northwest A&F University,College of Information Engineering,Yangling,China"],"affiliations":[{"raw_affiliation_string":"Northwest A&F University,College of Information Engineering,Yangling,China","institution_ids":["https://openalex.org/I89652312"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Huijun Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I89652312","display_name":"Northwest A&F University","ror":"https://ror.org/0051rme32","country_code":"CN","type":"education","lineage":["https://openalex.org/I89652312"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huijun Yang","raw_affiliation_strings":["Northwest A&F University,College of Information Engineering,Yangling,China"],"affiliations":[{"raw_affiliation_string":"Northwest A&F University,College of Information Engineering,Yangling,China","institution_ids":["https://openalex.org/I89652312"]}]},{"author_position":"last","author":{"id":null,"display_name":"Yufeng Fan","orcid":null},"institutions":[{"id":"https://openalex.org/I89652312","display_name":"Northwest A&F University","ror":"https://ror.org/0051rme32","country_code":"CN","type":"education","lineage":["https://openalex.org/I89652312"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yufeng Fan","raw_affiliation_strings":["Northwest A&F University,College of Information Engineering,Yangling,China"],"affiliations":[{"raw_affiliation_string":"Northwest A&F University,College of Information Engineering,Yangling,China","institution_ids":["https://openalex.org/I89652312"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I89652312"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.70243802,"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":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.5031999945640564,"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/T10616","display_name":"Smart Agriculture and AI","score":0.5031999945640564,"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.07280000299215317,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.0681999996304512,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.7874000072479248},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6525999903678894},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6061000227928162},{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.582099974155426},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.579800009727478},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5389999747276306},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5350000262260437}],"concepts":[{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.7874000072479248},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7559999823570251},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6525999903678894},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6061000227928162},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.582099974155426},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.579800009727478},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5655999779701233},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5389999747276306},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5350000262260437},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4189000129699707},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40799999237060547},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.3847000002861023},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38370001316070557},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.3799000084400177},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.37880000472068787},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3260999917984009},{"id":"https://openalex.org/C120217122","wikidata":"https://www.wikidata.org/wiki/Q740083","display_name":"Precision agriculture","level":3,"score":0.3005000054836273},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2953999936580658},{"id":"https://openalex.org/C2781395549","wikidata":"https://www.wikidata.org/wiki/Q4680762","display_name":"Adaptive sampling","level":3,"score":0.29510000348091125}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11227395","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227395","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/2","score":0.687504231929779,"display_name":"Zero hunger"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320337504","display_name":"Research and Development","ror":"https://ror.org/027s68j25"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W2015731569","https://openalex.org/W2099594591","https://openalex.org/W2555618208","https://openalex.org/W2769277501","https://openalex.org/W2798777114","https://openalex.org/W2960986959","https://openalex.org/W2963057320","https://openalex.org/W2963094037","https://openalex.org/W2963182550","https://openalex.org/W2968296999","https://openalex.org/W3034317268","https://openalex.org/W3034664537","https://openalex.org/W3034986117","https://openalex.org/W3035056458","https://openalex.org/W3152041191","https://openalex.org/W3199574295","https://openalex.org/W3200060357","https://openalex.org/W3204232356","https://openalex.org/W4206268807","https://openalex.org/W4210264399","https://openalex.org/W4399163139"],"related_works":[],"abstract_inverted_index":{"The":[0],"real-time":[1],"and":[2,29,49,87],"accurate":[3],"3D":[4],"segmentation":[5],"of":[6,59,67,109,201,208,232],"fruits":[7],"in":[8,73,112,123,230,239],"agricultural":[9,74],"field":[10,18,75,125,177],"is":[11,185],"crucial":[12],"for":[13,151],"precision":[14],"agriculture.":[15],"In":[16],"complex":[17,124],"scenarios,":[19],"point":[20,30,37,68,114,120,169,221],"clouds":[21,69],"generated":[22],"by":[23,118],"depth":[24],"cameras":[25],"often":[26],"have":[27],"gaps":[28],"dispersion":[31],"problems,":[32],"using":[33],"the":[34,57,64,107,155,181,206,214],"traditional":[35],"farthest":[36],"sampling":[38,65,136,143,147],"algorithm":[39,102,137,184],"(FPS)":[40],"makes":[41],"it":[42],"difficult":[43],"to":[44,105],"uniformly":[45],"capture":[46],"key":[47,202],"features,":[48],"FPS":[50],"requires":[51],"extensive":[52],"sorting":[53],"operations,":[54],"which":[55,141,164],"reduces":[56],"speed":[58,231],"feature":[60,79,92,158,194,233],"extraction.":[61],"To":[62,204],"optimize":[63,142],"uniformity":[66,144],"with":[70],"multiple":[71],"holes":[72],"scenes":[76],"while":[77,145,196,235],"enhancing":[78],"extraction":[80,93,159,195,234],"efficiency,":[81],"this":[82,129],"paper":[83],"proposes":[84],"a":[85,132,227],"lightweight":[86],"efficient":[88],"density-based":[89,133],"grid":[90,99,134,156],"parallel":[91,100,152,157],"network":[94],"GPFEnet.":[95],"First,":[96],"an":[97],"adaptive":[98],"grouping":[101],"was":[103,139,162,216],"designed":[104],"address":[106],"issue":[108],"low":[110],"efficiency":[111,148],"capturing":[113],"cloud":[115,121,170,222],"features":[116],"caused":[117],"irregular":[119],"data":[122],"scenarios.":[126],"Based":[127],"on":[128,176,210],"fundamental":[130],"algorithm,":[131],"random":[135],"GDSample":[138,183],"proposed,":[140],"improving":[146],"through":[149],"support":[150],"computation.":[153],"Finally,":[154],"subnetwork":[160],"GPFEnet":[161,209,215],"developed":[163],"can":[165],"be":[166],"embedded":[167],"into":[168,218],"deep":[171,223],"learning":[172,224],"networks.":[173],"Testing":[174],"results":[175],"datasets":[178],"show":[179],"that":[180],"proposed":[182],"approximately":[186],"35":[187],"times":[188],"faster":[189],"than":[190],"FPS,":[191],"significantly":[192],"accelerating":[193],"ensuring":[197],"more":[198],"even":[199],"coverage":[200],"features.":[203],"confirm":[205],"superiority":[207],"multi-hole":[211],"fruit":[212,240],"datasets,":[213],"integrated":[217],"various":[219],"cutting-edge":[220],"networks,":[225],"achieved":[226],"significant":[228],"enhancement":[229],"maintaining":[236],"similar":[237],"accuracy":[238],"segmentation.":[241]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-11-14T00:00:00"}