{"id":"https://openalex.org/W3116690419","doi":"https://doi.org/10.1109/access.2020.3048374","title":"End-to-End Automatic Berry Counting for Table Grape Thinning","display_name":"End-to-End Automatic Berry Counting for Table Grape Thinning","publication_year":2020,"publication_date":"2020-12-31","ids":{"openalex":"https://openalex.org/W3116690419","doi":"https://doi.org/10.1109/access.2020.3048374","mag":"3116690419"},"language":"en","primary_location":{"id":"doi:10.1109/access.2020.3048374","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3048374","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09311500.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09311500.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5019078379","display_name":"Prawit Buayai","orcid":"https://orcid.org/0000-0002-0873-9569"},"institutions":[{"id":"https://openalex.org/I66906201","display_name":"University of Yamanashi","ror":"https://ror.org/059x21724","country_code":"JP","type":"education","lineage":["https://openalex.org/I66906201"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Prawit Buayai","raw_affiliation_strings":["Faculty of Engineering, University of Yamanashi, Kofu, Japan"],"raw_orcid":"https://orcid.org/0000-0002-0873-9569","affiliations":[{"raw_affiliation_string":"Faculty of Engineering, University of Yamanashi, Kofu, Japan","institution_ids":["https://openalex.org/I66906201"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074127349","display_name":"Kanda Runapongsa Saikaew","orcid":"https://orcid.org/0000-0003-1949-7048"},"institutions":[{"id":"https://openalex.org/I179193067","display_name":"Khon Kaen University","ror":"https://ror.org/03cq4gr50","country_code":"TH","type":"education","lineage":["https://openalex.org/I179193067"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Kanda Runapongsa Saikaew","raw_affiliation_strings":["Faculty of Engineering, Khon Kaen University, Khon Kaen, Thailand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Engineering, Khon Kaen University, Khon Kaen, Thailand","institution_ids":["https://openalex.org/I179193067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043255121","display_name":"Xiaoyang Mao","orcid":"https://orcid.org/0000-0001-9531-3197"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]},{"id":"https://openalex.org/I66906201","display_name":"University of Yamanashi","ror":"https://ror.org/059x21724","country_code":"JP","type":"education","lineage":["https://openalex.org/I66906201"]}],"countries":["CN","JP"],"is_corresponding":false,"raw_author_name":"Xiaoyang Mao","raw_affiliation_strings":["Faculty of Engineering, University of Yamanashi, Kofu, Japan","School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-9531-3197","affiliations":[{"raw_affiliation_string":"Faculty of Engineering, University of Yamanashi, Kofu, Japan","institution_ids":["https://openalex.org/I66906201"]},{"raw_affiliation_string":"School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":4.0517,"has_fulltext":true,"cited_by_count":42,"citation_normalized_percentile":{"value":0.94004996,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"9","issue":null,"first_page":"4829","last_page":"4842"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9753000140190125,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9753000140190125,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"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/T11439","display_name":"Video Analysis and Summarization","score":0.9369000196456909,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9319000244140625,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/thinning","display_name":"Thinning","score":0.8535341024398804},{"id":"https://openalex.org/keywords/berry","display_name":"Berry","score":0.7744730710983276},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.719235360622406},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.552290678024292},{"id":"https://openalex.org/keywords/table-grape","display_name":"Table grape","score":0.5431416034698486},{"id":"https://openalex.org/keywords/table","display_name":"Table (database)","score":0.5156334638595581},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5038730502128601},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.45915308594703674},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.320826917886734},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.22793978452682495},{"id":"https://openalex.org/keywords/horticulture","display_name":"Horticulture","score":0.1604101061820984}],"concepts":[{"id":"https://openalex.org/C2781353100","wikidata":"https://www.wikidata.org/wiki/Q1266974","display_name":"Thinning","level":2,"score":0.8535341024398804},{"id":"https://openalex.org/C2776034682","wikidata":"https://www.wikidata.org/wiki/Q13184","display_name":"Berry","level":2,"score":0.7744730710983276},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.719235360622406},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.552290678024292},{"id":"https://openalex.org/C2777523825","wikidata":"https://www.wikidata.org/wiki/Q361073","display_name":"Table grape","level":3,"score":0.5431416034698486},{"id":"https://openalex.org/C45235069","wikidata":"https://www.wikidata.org/wiki/Q278425","display_name":"Table (database)","level":2,"score":0.5156334638595581},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5038730502128601},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.45915308594703674},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.320826917886734},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.22793978452682495},{"id":"https://openalex.org/C144027150","wikidata":"https://www.wikidata.org/wiki/Q48803","display_name":"Horticulture","level":1,"score":0.1604101061820984},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2020.3048374","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3048374","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09311500.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:24bbc67a8c714a38a894c144d479ebab","is_oa":true,"landing_page_url":"https://doaj.org/article/24bbc67a8c714a38a894c144d479ebab","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 9, Pp 4829-4842 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2020.3048374","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3048374","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09311500.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3116690419.pdf","grobid_xml":"https://content.openalex.org/works/W3116690419.grobid-xml"},"referenced_works_count":47,"referenced_works":["https://openalex.org/W63402996","https://openalex.org/W114517082","https://openalex.org/W1503398984","https://openalex.org/W1508960934","https://openalex.org/W1536680647","https://openalex.org/W1678356000","https://openalex.org/W1892082890","https://openalex.org/W1972593456","https://openalex.org/W1984319709","https://openalex.org/W2023726356","https://openalex.org/W2027604671","https://openalex.org/W2063197142","https://openalex.org/W2123820573","https://openalex.org/W2125215748","https://openalex.org/W2217315150","https://openalex.org/W2468835460","https://openalex.org/W2501369945","https://openalex.org/W2536455790","https://openalex.org/W2564790728","https://openalex.org/W2588938825","https://openalex.org/W2604559464","https://openalex.org/W2611227133","https://openalex.org/W2622263826","https://openalex.org/W2738588019","https://openalex.org/W2773985483","https://openalex.org/W2793092779","https://openalex.org/W2807964252","https://openalex.org/W2884745705","https://openalex.org/W2911433502","https://openalex.org/W2911964244","https://openalex.org/W2916798096","https://openalex.org/W2963150697","https://openalex.org/W2963351448","https://openalex.org/W2963353662","https://openalex.org/W2963433607","https://openalex.org/W2963849369","https://openalex.org/W2967789058","https://openalex.org/W2968851387","https://openalex.org/W2973497687","https://openalex.org/W2989676862","https://openalex.org/W3004977026","https://openalex.org/W4211037340","https://openalex.org/W4212863985","https://openalex.org/W4251916190","https://openalex.org/W6739622702","https://openalex.org/W6768439253","https://openalex.org/W7001826656"],"related_works":["https://openalex.org/W2179817500","https://openalex.org/W4318778296","https://openalex.org/W1889402392","https://openalex.org/W2171014258","https://openalex.org/W4367360509","https://openalex.org/W3175840934","https://openalex.org/W1965605486","https://openalex.org/W2808636988","https://openalex.org/W2119652015","https://openalex.org/W4313588674"],"abstract_inverted_index":{"Bunch":[0],"shape":[1,34],"and":[2,11,35,109,146,219],"berry":[3],"size":[4],"indicate":[5],"the":[6,22,54,63,72,88,91,129,160,186,196,217,222],"quality":[7],"of":[8,21,56,74,90,162,175,188,221],"table":[9],"grapes":[10],"crucially":[12],"affect":[13],"their":[14,179],"market":[15],"value.":[16],"Berry":[17],"thinning":[18,64,92,130],"is":[19,52,132,182],"one":[20],"most":[23],"important":[24],"tasks":[25],"in":[26,50,58,76,164,190],"grape":[27,48],"cultivation":[28],"to":[29,36,61,86,215],"achieve":[30],"an":[31],"ideal":[32],"bunch":[33,60,79,167,192],"make":[37],"sufficient":[38],"space":[39],"for":[40,69,158,184],"individual":[41],"berries.":[42],"A":[43],"successful":[44],"practice":[45],"by":[46,84],"skilled":[47],"farmers":[49,85],"Japan":[51],"using":[53,195,205],"number":[55,73,161,187],"berries":[57,75,163,189,197],"a":[59,67,77,97,104,117,123,135,151,165,169,173,191,200],"guide":[62],"process;":[65],"hence,":[66],"technique":[68,101,127],"automatically":[70],"counting":[71],"working":[78,166],"has":[80],"been":[81,213],"long":[82],"desired":[83],"improve":[87],"efficiency":[89],"task.":[93],"This":[94],"research":[95],"presents":[96],"novel":[98,124],"end-to-end":[99],"berry-counting":[100],"based":[102],"on":[103,199],"deep":[105],"neural":[106],"network":[107],"(DNN),":[108],"its":[110],"contributions":[111],"are":[112],"as":[113],"follows.":[114],"First,":[115],"because":[116],"DNN":[118],"requires":[119],"massive":[120],"training":[121],"data,":[122],"data":[125,206],"augmentation":[126],"simulating":[128],"process":[131,211],"proposed.":[133],"Second,":[134],"new":[136],"location-sensitive":[137],"object":[138],"detection":[139],"model":[140,155],"that":[141],"integrates":[142],"explicit":[143],"location":[144],"information":[145],"supplementary":[147],"classification":[148],"loss":[149],"into":[150],"state-of-the-art":[152],"instance":[153],"segmentation":[154],"was":[156],"proposed":[157,223],"detecting":[159],"with":[168,178],"high":[170],"accuracy.":[171],"Third,":[172],"set":[174],"features,":[176],"together":[177],"extraction":[180],"algorithms,":[181],"designed":[183],"predicting":[185],"(3D":[193],"counting)":[194],"detected":[198],"single":[201],"2D":[202],"image.":[203],"Experiments":[204],"collected":[207],"from":[208],"farmers'":[209],"grape-thinning":[210],"have":[212],"conducted":[214],"validate":[216],"accuracy":[218],"effectiveness":[220],"methods.":[224]},"counts_by_year":[{"year":2026,"cited_by_count":8},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":5}],"updated_date":"2026-08-11T07:18:39.950985","created_date":"2025-10-10T00:00:00"}