{"id":"https://openalex.org/W4226194683","doi":"https://doi.org/10.1145/3505711.3505720","title":"Efficient Fruit and Vegetable Classification and Counting for Retail Applications Using Deep Learning","display_name":"Efficient Fruit and Vegetable Classification and Counting for Retail Applications Using Deep Learning","publication_year":2021,"publication_date":"2021-11-20","ids":{"openalex":"https://openalex.org/W4226194683","doi":"https://doi.org/10.1145/3505711.3505720"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://doi.org/10.1145/3505711.3505720","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3505711.3505720","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true},"type":"article","type_crossref":"proceedings-article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3505711.3505720","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5013211552","display_name":"Kirill Bogomasov","orcid":"https://orcid.org/0000-0002-8121-4427"},"institutions":[{"id":"https://openalex.org/I44260953","display_name":"Heinrich Heine University D\u00fcsseldorf","ror":"https://ror.org/024z2rq82","country_code":"DE","type":"funder","lineage":["https://openalex.org/I44260953"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Kirill Bogomasov","raw_affiliation_strings":["Heinrich Heine University, HHU, Germany"],"affiliations":[{"raw_affiliation_string":"Heinrich Heine University, HHU, Germany","institution_ids":["https://openalex.org/I44260953"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037038868","display_name":"Stefan Conrad","orcid":"https://orcid.org/0000-0003-2788-3854"},"institutions":[{"id":"https://openalex.org/I44260953","display_name":"Heinrich Heine University D\u00fcsseldorf","ror":"https://ror.org/024z2rq82","country_code":"DE","type":"funder","lineage":["https://openalex.org/I44260953"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Stefan Conrad","raw_affiliation_strings":["Heinrich Heine University, HHU, Germany"],"affiliations":[{"raw_affiliation_string":"Heinrich Heine University, HHU, Germany","institution_ids":["https://openalex.org/I44260953"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.258,"has_fulltext":true,"fulltext_origin":"pdf","cited_by_count":2,"citation_normalized_percentile":{"value":0.523246,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":66,"max":71},"biblio":{"volume":null,"issue":null,"first_page":"65","last_page":"71"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9978,"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":0.9978,"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/T14319","display_name":"Currency Recognition and Detection","score":0.99,"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/T12697","display_name":"Water Quality Monitoring Technologies","score":0.9883,"subfield":{"id":"https://openalex.org/subfields/2312","display_name":"Water Science and Technology"},"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/contextual-image-classification","display_name":"Contextual image classification","score":0.4530338},{"id":"https://openalex.org/keywords/identification","display_name":"Identification","score":0.4530156},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.41293052}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.773953},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5696925},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.56324446},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.54253876},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5145023},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.50611585},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.48995438},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.48924303},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.47785446},{"id":"https://openalex.org/C2781018962","wikidata":"https://www.wikidata.org/wiki/Q5164884","display_name":"Container (type theory)","level":2,"score":0.46937943},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4530338},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.4530156},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.42202118},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.41293052},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40685463},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3590092},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.24059528},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11284432},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.09983027},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.091827214},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","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/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","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":1,"locations":[{"is_oa":true,"landing_page_url":"https://doi.org/10.1145/3505711.3505720","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3505711.3505720","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://doi.org/10.1145/3505711.3505720","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3505711.3505720","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true},"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":25,"referenced_works":["https://openalex.org/W1496832333","https://openalex.org/W1574062176","https://openalex.org/W2164445672","https://openalex.org/W2211996548","https://openalex.org/W2463631526","https://openalex.org/W2501369945","https://openalex.org/W2512006179","https://openalex.org/W2611194840","https://openalex.org/W2611227133","https://openalex.org/W2737762472","https://openalex.org/W2892570125","https://openalex.org/W2962921175","https://openalex.org/W2963163009","https://openalex.org/W2963351448","https://openalex.org/W2963499661","https://openalex.org/W2963523428","https://openalex.org/W2963686699","https://openalex.org/W2964444661","https://openalex.org/W2977084122","https://openalex.org/W2978559043","https://openalex.org/W3004672782","https://openalex.org/W3006217902","https://openalex.org/W3034971973","https://openalex.org/W3111114947","https://openalex.org/W4389138872"],"related_works":["https://openalex.org/W4387272257","https://openalex.org/W4200301313","https://openalex.org/W2974221847","https://openalex.org/W2775844720","https://openalex.org/W2367301169","https://openalex.org/W2352134912","https://openalex.org/W2074219825","https://openalex.org/W2055230095","https://openalex.org/W2048054615","https://openalex.org/W2001079144"],"abstract_inverted_index":{"The":[0,70,148,167],"process":[1],"of":[2,7,65,100,127,174,181,195],"manual":[3],"classification":[4,107,132,146],"and":[5,9,35,109,133,144,155,159,177],"counting":[6,140,172],"fruits":[8],"vegetables,":[10],"from":[11],"the":[12,14,19,26,29,48,96,102,111],"moment":[13],"customer":[15],"places":[16],"items":[17],"on":[18,28,75,142,201],"conveyor":[20],"belt":[21],"to":[22,43],"their":[23],"weighing":[24],"by":[25,90],"cashier":[27],"checkout":[30],"scale":[31],"is":[32,63,73,79,88,124,187],"time":[33,161,180],"consuming":[34,114],"may":[36],"be":[37],"burdensome":[38],"for":[39,51,81,130,138,191],"cashiers,":[40],"who":[41],"need":[42],"look":[44],"up":[45],"or":[46],"remember":[47],"identification":[49],"code":[50],"each":[52],"product.":[53],"Not":[54],"any":[55],"more:":[56],"We":[57,94,117],"built":[58],"a":[59,76,106,119,134,171,188,202],"real-life":[60],"application,":[61],"which":[62,86,123],"capable":[64],"doing":[66],"both":[67],"tasks":[68],"simultaneously.":[69],"presented":[71],"research":[72],"focused":[74],"case":[77],"that":[78,99],"attractive":[80],"its":[82],"practical":[83],"applications,":[84],"in":[85],"data":[87],"expanded":[89],"product":[91],"weight":[92,143],"information.":[93],"approach":[95],"problem":[97],"as":[98,105],"estimating":[101],"object":[103,115,139,153,164],"count":[104,154],"task":[108],"evade":[110],"more":[112],"resource":[113],"detection.":[116],"introduce":[118],"new":[120],"hybrid":[121],"architecture":[122,150,169],"an":[125,178],"ensemble":[126],"EfficientNet":[128],"[31]":[129],"image":[131],"Decision":[135],"Tree":[136],"[3]":[137],"based":[141],"previous":[145],"result.":[147],"trained":[149],"provides":[151],"accurate":[152],"requires":[156],"fewer":[157],"resources":[158],"less":[160],"than":[162],"current":[163],"detection":[165],"architectures.":[166],"proposed":[168],"accomplishes":[170],"accuracy":[173],"around":[175],"80%":[176],"inference":[179],"0.2":[182],"sec.":[183],"per":[184],"image.":[185],"It":[186],"good":[189],"candidate":[190],"handling":[192],"huge":[193],"amount":[194],"visual":[196],"information":[197],"involving":[198],"fast":[199],"processing":[200],"CPU.":[203]},"abstract_inverted_index_v3":null,"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4226194683","counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2025-04-04T08:18:26.957080","created_date":"2022-05-05"}