{"id":"https://openalex.org/W3197946167","doi":"https://doi.org/10.1145/3462676.3462686","title":"Corn Ear Quality Recognition Based on DCGAN Data Enhancement and Transfer Learning","display_name":"Corn Ear Quality Recognition Based on DCGAN Data Enhancement and Transfer Learning","publication_year":2021,"publication_date":"2021-04-09","ids":{"openalex":"https://openalex.org/W3197946167","doi":"https://doi.org/10.1145/3462676.3462686","mag":"3197946167"},"language":"en","primary_location":{"id":"doi:10.1145/3462676.3462686","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3462676.3462686","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 4th International Conference on Electronics, Communications and Control Engineering","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/A5067897065","display_name":"Benzheng Shi","orcid":null},"institutions":[{"id":"https://openalex.org/I80143920","display_name":"Shandong University of Science and Technology","ror":"https://ror.org/04gtjhw98","country_code":"CN","type":"education","lineage":["https://openalex.org/I80143920"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Benzheng Shi","raw_affiliation_strings":["Shandong University of Science&amp;Technology, China"],"affiliations":[{"raw_affiliation_string":"Shandong University of Science&amp;Technology, China","institution_ids":["https://openalex.org/I80143920"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043838342","display_name":"Xiaolei Zhou","orcid":"https://orcid.org/0000-0002-4198-4451"},"institutions":[{"id":"https://openalex.org/I80143920","display_name":"Shandong University of Science and Technology","ror":"https://ror.org/04gtjhw98","country_code":"CN","type":"education","lineage":["https://openalex.org/I80143920"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaolei Zhou","raw_affiliation_strings":["Shandong University of Science&amp;Technology, China"],"affiliations":[{"raw_affiliation_string":"Shandong University of Science&amp;Technology, China","institution_ids":["https://openalex.org/I80143920"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026486009","display_name":"Zhikang Qin","orcid":null},"institutions":[{"id":"https://openalex.org/I80143920","display_name":"Shandong University of Science and Technology","ror":"https://ror.org/04gtjhw98","country_code":"CN","type":"education","lineage":["https://openalex.org/I80143920"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhikang Qin","raw_affiliation_strings":["Shandong University of Science&amp;Technology, China"],"affiliations":[{"raw_affiliation_string":"Shandong University of Science&amp;Technology, China","institution_ids":["https://openalex.org/I80143920"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067365328","display_name":"Lu Sun","orcid":"https://orcid.org/0000-0003-3527-2295"},"institutions":[{"id":"https://openalex.org/I80143920","display_name":"Shandong University of Science and Technology","ror":"https://ror.org/04gtjhw98","country_code":"CN","type":"education","lineage":["https://openalex.org/I80143920"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lu Sun","raw_affiliation_strings":["Shandong University of Science&amp;Technology, China"],"affiliations":[{"raw_affiliation_string":"Shandong University of Science&amp;Technology, China","institution_ids":["https://openalex.org/I80143920"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051481965","display_name":"Yan Xu","orcid":"https://orcid.org/0000-0002-4981-0165"},"institutions":[{"id":"https://openalex.org/I115455960","display_name":"Centre for Quantum Technologies","ror":"https://ror.org/01mgdzc49","country_code":"SG","type":"facility","lineage":["https://openalex.org/I115455960"]},{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Yan Xu","raw_affiliation_strings":["Shandong University of Science&amp;Technology and Center for Quantum Technologies and Physics Department, Faculty of Science, National University of Singapore, China"],"affiliations":[{"raw_affiliation_string":"Shandong University of Science&amp;Technology and Center for Quantum Technologies and Physics Department, Faculty of Science, National University of Singapore, China","institution_ids":["https://openalex.org/I115455960","https://openalex.org/I165932596"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5067897065"],"corresponding_institution_ids":["https://openalex.org/I80143920"],"apc_list":null,"apc_paid":null,"fwci":0.6648,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.77763022,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"62","last_page":"68"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.8511000275611877,"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":0.8511000275611877,"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/T10828","display_name":"Biometric Identification and Security","score":0.8427000045776367,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T13568","display_name":"Wood and Agarwood Research","score":0.8306000232696533,"subfield":{"id":"https://openalex.org/subfields/1605","display_name":"Organic Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.7635434865951538},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6987393498420715},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6511794328689575},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6311705708503723},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6026586890220642},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6012113094329834},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.56577467918396},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5376831889152527},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4838187098503113},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4786871075630188},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.47185254096984863},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.44414401054382324},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4180954098701477},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40250036120414734},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3900165557861328}],"concepts":[{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.7635434865951538},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6987393498420715},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6511794328689575},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6311705708503723},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6026586890220642},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6012113094329834},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.56577467918396},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5376831889152527},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4838187098503113},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4786871075630188},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.47185254096984863},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.44414401054382324},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4180954098701477},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40250036120414734},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3900165557861328},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3462676.3462686","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3462676.3462686","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 4th International Conference on Electronics, Communications and Control Engineering","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"No poverty","id":"https://metadata.un.org/sdg/1","score":0.5199999809265137}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3201126466","https://openalex.org/W4282827391","https://openalex.org/W4386828785","https://openalex.org/W3165580226","https://openalex.org/W2786391746","https://openalex.org/W4381430104","https://openalex.org/W2995102745","https://openalex.org/W4226059458","https://openalex.org/W2914559142","https://openalex.org/W1990237101"],"abstract_inverted_index":{"Aiming":[0],"at":[1],"the":[2,21,49,60,69,74,79,84,102,107,126,131,136,140,143,157,167,174,179,189],"problems":[3],"of":[4,16,130,135,142,178],"poor":[5],"feature":[6],"extraction":[7],"ability,":[8],"low":[9,11],"accuracy,":[10],"recognition":[12,35,176],"efficiency,":[13],"and":[14,45,54,101,117,121,138],"over-fitting":[15],"convolutional":[17,103],"neural":[18],"networks":[19],"when":[20],"data":[22,76,155],"set":[23],"(training":[24],"set)":[25],"is":[26,87,99,112,163,182,185],"small,":[27],"we":[28],"propose":[29],"a":[30,90,93],"new":[31,94,119],"corn":[32],"ear":[33],"quality":[34],"model":[36,109,137,147,162,181],"called":[37],"CornNet.":[38],"Taking":[39],"normal,":[40],"messy":[41],"kernels,":[42],"mildew,":[43],"mottled,":[44],"lacking":[46],"kernels":[47],"as":[48],"research":[50],"objects,":[51],"self-made":[52],"datasets,":[53],"made":[55],"classification":[56],"labels.":[57],"First,":[58],"use":[59],"Deep":[61],"Convolution":[62],"Generative":[63],"Adversarial":[64],"Network":[65],"(DCGAN)":[66],"that":[67,173],"introduces":[68],"Dropout2d":[70],"method":[71],"to":[72,77,114,165],"enhance":[73],"sample":[75],"expand":[78],"image":[80],"training":[81,146,161],"set.":[82],"Then,":[83],"DPN-92":[85],"network":[86],"simplified":[88],"into":[89],"DPN-68":[91,108],"network,":[92],"fully":[95,132],"connected":[96,133],"layer":[97,104,134],"module":[98],"designed,":[100],"trained":[105,125],"on":[106,110],"ImageNet":[111],"transferred":[113],"this":[115],"model,":[116],"only":[118],"learning":[120,123,128,145,160],"transfer":[122,144,159],"are":[124,148],"three":[127],"methods":[129],"all":[139],"layers":[141],"compared":[149],"for":[150],"experiments.":[151],"Finally,":[152],"after":[153],"DCGAN":[154],"enhancement,":[156],"entire":[158],"used":[164],"form":[166],"CornNet":[168,180],"model.":[169],"The":[170],"results":[171],"show":[172],"average":[175],"rate":[177],"98.75%,":[183],"which":[184],"3.58%\u223c12.04%":[186],"higher":[187],"than":[188],"state-of-the-art":[190],"results.":[191]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":3}],"updated_date":"2026-04-03T22:45:19.894376","created_date":"2025-10-10T00:00:00"}
