{"id":"https://openalex.org/W4210667194","doi":"https://doi.org/10.1109/tgrs.2022.3142288","title":"A Robust Deep Learning Approach for the Quantitative Characterization and Clustering of Peach Tree Crowns Based on UAV Images","display_name":"A Robust Deep Learning Approach for the Quantitative Characterization and Clustering of Peach Tree Crowns Based on UAV Images","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4210667194","doi":"https://doi.org/10.1109/tgrs.2022.3142288"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2022.3142288","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3142288","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-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/A5101602259","display_name":"Jun Hu","orcid":"https://orcid.org/0000-0002-4240-1430"},"institutions":[{"id":"https://openalex.org/I4210126939","display_name":"ZheJiang Academy of Agricultural Sciences","ror":"https://ror.org/02qbc3192","country_code":"CN","type":"nonprofit","lineage":["https://openalex.org/I4210126939"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jun Hu","raw_affiliation_strings":["Food Science Institute, Zhejiang Academy of Agricultural Sciences, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Food Science Institute, Zhejiang Academy of Agricultural Sciences, Hangzhou, China","institution_ids":["https://openalex.org/I4210126939"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100367505","display_name":"Yanfeng Zhang","orcid":"https://orcid.org/0009-0007-2486-6772"},"institutions":[{"id":"https://openalex.org/I4210110961","display_name":"Anhui Academy of Agricultural Sciences","ror":"https://ror.org/01pw5qp76","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210110961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanfeng Zhang","raw_affiliation_strings":["Institute of Soil and Fertilizer, Anhui Academy of Agricultural Sciences, Hefei, China"],"affiliations":[{"raw_affiliation_string":"Institute of Soil and Fertilizer, Anhui Academy of Agricultural Sciences, Hefei, China","institution_ids":["https://openalex.org/I4210110961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101724878","display_name":"Dandan Zhao","orcid":"https://orcid.org/0000-0002-0585-2742"},"institutions":[{"id":"https://openalex.org/I4210126939","display_name":"ZheJiang Academy of Agricultural Sciences","ror":"https://ror.org/02qbc3192","country_code":"CN","type":"nonprofit","lineage":["https://openalex.org/I4210126939"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dandan Zhao","raw_affiliation_strings":["Food Science Institute, Zhejiang Academy of Agricultural Sciences, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Food Science Institute, Zhejiang Academy of Agricultural Sciences, Hangzhou, China","institution_ids":["https://openalex.org/I4210126939"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020713851","display_name":"Guijun Yang","orcid":"https://orcid.org/0000-0002-6425-8321"},"institutions":[{"id":"https://openalex.org/I4210156423","display_name":"National Engineering Research Center for Information Technology in Agriculture","ror":"https://ror.org/04c3j3t84","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210156423"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guijun Yang","raw_affiliation_strings":["Information Technology Research Center and the Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Information Technology Research Center and the Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210156423"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073367660","display_name":"Feiyun Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feiyun Chen","raw_affiliation_strings":["School of Life Sciences, Anhui University, Hefei, China"],"affiliations":[{"raw_affiliation_string":"School of Life Sciences, Anhui University, Hefei, China","institution_ids":["https://openalex.org/I143868143"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081731800","display_name":"Chengquan Zhou","orcid":"https://orcid.org/0000-0001-7427-0888"},"institutions":[{"id":"https://openalex.org/I4210126939","display_name":"ZheJiang Academy of Agricultural Sciences","ror":"https://ror.org/02qbc3192","country_code":"CN","type":"nonprofit","lineage":["https://openalex.org/I4210126939"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chengquan Zhou","raw_affiliation_strings":["Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, Hangzhou, China","institution_ids":["https://openalex.org/I4210126939"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059336561","display_name":"Wenxuan Chen","orcid":"https://orcid.org/0000-0002-3803-2847"},"institutions":[{"id":"https://openalex.org/I4210126939","display_name":"ZheJiang Academy of Agricultural Sciences","ror":"https://ror.org/02qbc3192","country_code":"CN","type":"nonprofit","lineage":["https://openalex.org/I4210126939"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenxuan Chen","raw_affiliation_strings":["Food Science Institute, Zhejiang Academy of Agricultural Sciences, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Food Science Institute, Zhejiang Academy of Agricultural Sciences, Hangzhou, China","institution_ids":["https://openalex.org/I4210126939"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5101602259"],"corresponding_institution_ids":["https://openalex.org/I4210126939"],"apc_list":null,"apc_paid":null,"fwci":3.5513,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.91842195,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"60","issue":null,"first_page":"1","last_page":"13"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9958000183105469,"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":0.9958000183105469,"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/T11796","display_name":"Horticultural and Viticultural Research","score":0.9958000183105469,"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":0.9954000115394592,"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/point-cloud","display_name":"Point cloud","score":0.6581583619117737},{"id":"https://openalex.org/keywords/crown","display_name":"Crown (dentistry)","score":0.610167920589447},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5316522717475891},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4758855104446411},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.4748920500278473},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.4420991837978363},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4286476969718933},{"id":"https://openalex.org/keywords/volume","display_name":"Volume (thermodynamics)","score":0.42800015211105347},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4108617901802063},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3935338854789734}],"concepts":[{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.6581583619117737},{"id":"https://openalex.org/C2778400979","wikidata":"https://www.wikidata.org/wiki/Q143720","display_name":"Crown (dentistry)","level":2,"score":0.610167920589447},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5316522717475891},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4758855104446411},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.4748920500278473},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.4420991837978363},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4286476969718933},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.42800015211105347},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4108617901802063},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3935338854789734},{"id":"https://openalex.org/C199343813","wikidata":"https://www.wikidata.org/wiki/Q12128","display_name":"Dentistry","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2022.3142288","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3142288","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15","score":0.5400000214576721}],"awards":[{"id":"https://openalex.org/G2046306524","display_name":null,"funder_award_id":"6182011","funder_id":"https://openalex.org/F4320322919","funder_display_name":"Natural Science Foundation of Beijing Municipality"},{"id":"https://openalex.org/G2480972564","display_name":null,"funder_award_id":"31901662","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2670459227","display_name":null,"funder_award_id":"KJCX20170423","funder_id":"https://openalex.org/F4320324784","funder_display_name":"Beijing Academy of Agricultural and Forestry Sciences"},{"id":"https://openalex.org/G7979661840","display_name":null,"funder_award_id":"31901722","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8560393470","display_name":null,"funder_award_id":"32000283","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322919","display_name":"Natural Science Foundation of Beijing Municipality","ror":null},{"id":"https://openalex.org/F4320324784","display_name":"Beijing Academy of Agricultural and Forestry Sciences","ror":"https://ror.org/04trzn023"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W1964116066","https://openalex.org/W1966579280","https://openalex.org/W1972252554","https://openalex.org/W1994434338","https://openalex.org/W1994868963","https://openalex.org/W2009938987","https://openalex.org/W2027038745","https://openalex.org/W2045770636","https://openalex.org/W2054397552","https://openalex.org/W2063113990","https://openalex.org/W2081310370","https://openalex.org/W2123673090","https://openalex.org/W2128866545","https://openalex.org/W2144158466","https://openalex.org/W2145023731","https://openalex.org/W2188563027","https://openalex.org/W2290937242","https://openalex.org/W2466959383","https://openalex.org/W2473156356","https://openalex.org/W2560462200","https://openalex.org/W2565531507","https://openalex.org/W2768265868","https://openalex.org/W2769625730","https://openalex.org/W2790979755","https://openalex.org/W2796455819","https://openalex.org/W2800741685","https://openalex.org/W2800958576","https://openalex.org/W2810482441","https://openalex.org/W2883776037","https://openalex.org/W2901528449","https://openalex.org/W2902048518","https://openalex.org/W2920621226","https://openalex.org/W2921602766","https://openalex.org/W2930717782","https://openalex.org/W2947521956","https://openalex.org/W2954996726","https://openalex.org/W2958949438","https://openalex.org/W2997224422","https://openalex.org/W2998304066","https://openalex.org/W3042706118","https://openalex.org/W3096112403","https://openalex.org/W6678815747"],"related_works":["https://openalex.org/W1966682116","https://openalex.org/W3016928466","https://openalex.org/W2026089796","https://openalex.org/W4389574804","https://openalex.org/W4375867731","https://openalex.org/W2936725271","https://openalex.org/W1998232909","https://openalex.org/W2367081626","https://openalex.org/W2315757411","https://openalex.org/W1556261626"],"abstract_inverted_index":{"The":[0,289],"accurate":[1,47],"large-scale":[2],"measurement":[3,61],"of":[4,15,34,77,89,137,225,238,243,292],"peach":[5,79,91,286],"crowns":[6,81],"is":[7,62,112,282],"vital":[8],"in":[9,93,299],"horticultural":[10],"science":[11],"and":[12,26,46,53,75,114,148,182,191,203,221,252,262,271,302],"the":[13,32,43,73,97,100,131,135,138,165,187,192,216,222,241,259,275,279],"optimization":[14],"orchard":[16,92],"management.":[17],"Nowadays,":[18],"numerous":[19],"crown":[20,23,60,132,139,166,188,244],"parameters":[21],"(e.g.,":[22],"area,":[24],"height,":[25],"volume)":[27],"can":[28],"be":[29,269],"obtained":[30],"via":[31],"analysis":[33],"point":[35],"clouds":[36],"or":[37],"photographs.":[38],"Current":[39],"laser-based":[40],"sensors":[41],"provide":[42],"required":[44],"reliable":[45],"information;":[48],"however,":[49],"they":[50],"are":[51],"costly":[52],"time-consuming.":[54],"Therefore,":[55],"a":[56,70,90,108,149,249,257],"simpler":[57],"approach":[58],"for":[59,72,195,284],"required.":[63],"For":[64,240],"this":[65,67],"purpose,":[66],"study":[68],"presents":[69],"pipeline":[71],"monitoring":[74],"clustering":[76],"259":[78],"tree":[80,287],"based":[82,120],"on":[83,121],"unmanned":[84],"aerial":[85,102],"vehicle":[86],"(UAV)":[87],"images":[88],"Southeast":[94],"China.":[95],"Considering":[96],"limitation":[98],"that":[99,278],"original":[101],"image":[103],"dataset":[104],"contains":[105],"little":[106],"information,":[107],"data":[109],"augmentation":[110],"process":[111,147],"adopted,":[113],"an":[115,144,156,235],"efficient":[116],"deep":[117],"learning":[118],"architecture":[119],"conditional":[122],"generative":[123],"adversarial":[124],"networks":[125],"(cGANs)":[126],"was":[127,141,161],"designed":[128],"to":[129,163,185,268],"extract":[130],"area.":[133],"Then,":[134],"shape":[136],"area":[140],"clustered":[142],"using":[143,246],"edge":[145],"detection":[146,251],"<inline-formula>":[150,170,175],"<tex-math":[151,171,176],"notation=\"LaTeX\">$k$":[152],"</tex-math></inline-formula>-means":[153],"algorithm.":[154],"Finally,":[155],"ellipsoid":[157],"volume":[158],"method":[159,281],"(EVM)":[160],"applied":[162,297],"estimate":[164],"volume.":[167],"Five":[168],"indicators&#x2014;namely,":[169],"notation=\"LaTeX\">$Q_{\\mathrm":[172],"{seg}}$":[173],"</tex-math></inline-formula>,":[174,179],"notation=\"LaTeX\">$S_{\\mathrm":[177],"{r}}$":[178],"Precision,":[180],"Recall,":[181],"F-measure&#x2014;were":[183],"employed":[184],"evaluate":[186],"extraction":[189,227],"effects,":[190],"average":[193],"results":[194,276],"testing":[196],"samples":[197],"were":[198,266],"0.832,":[199],"0.847,":[200],"0.851,":[201],"0.828,":[202],"0.846,":[204],"respectively.":[205,273],"Compared":[206],"with":[207],"other":[208],"approaches&#x2014;namely,":[209],"fully":[210],"convolutional":[211],"network":[212],"(FCN),":[213],"U-Net,":[214],"SegNet21,":[215],"excess":[217],"green":[218],"index":[219,224],"(ExG),":[220],"color":[223],"vegetation":[226],"(CIVE)&#x2014;the":[228],"proposed":[229,280],"cGAN":[230],"model":[231],"performs":[232],"better,":[233],"achieving":[234],"accuracy":[236],"improvement":[237],"5&#x0025;&#x2013;25&#x0025;.":[239],"estimation":[242],"volume,":[245],"measurements":[247],"from":[248],"light":[250],"ranging":[253],"(LIDAR)":[254],"scanner":[255],"as":[256],"reference,":[258],"correlation":[260],"coefficient":[261],"relative-root-mean-square":[263],"error":[264],"(R-RMSE)":[265],"found":[267],"0.836&#x0025;":[270],"14.93&#x0025;,":[272],"Overall,":[274],"demonstrate":[277],"feasible":[283],"measuring":[285],"crowns.":[288],"wide":[290],"application":[291],"such":[293],"technology":[294],"would":[295],"facilitate":[296],"research":[298],"plant":[300],"phenotyping":[301],"precision":[303],"horticulture.":[304]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
