{"id":"https://openalex.org/W4390071631","doi":"https://doi.org/10.1109/idaacs58523.2023.10348939","title":"Detection of Defects in Soybean Seeds by Extracting Deep Features with SqueezeNet","display_name":"Detection of Defects in Soybean Seeds by Extracting Deep Features with SqueezeNet","publication_year":2023,"publication_date":"2023-09-07","ids":{"openalex":"https://openalex.org/W4390071631","doi":"https://doi.org/10.1109/idaacs58523.2023.10348939"},"language":"en","primary_location":{"id":"doi:10.1109/idaacs58523.2023.10348939","is_oa":false,"landing_page_url":"https://doi.org/10.1109/idaacs58523.2023.10348939","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 12th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)","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/A5012042437","display_name":"Murat K\u00f6kl\u00fc","orcid":"https://orcid.org/0000-0002-2737-2360"},"institutions":[{"id":"https://openalex.org/I137996928","display_name":"Sel\u00e7uk University","ror":"https://ror.org/045hgzm75","country_code":"TR","type":"education","lineage":["https://openalex.org/I137996928"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Murat Koklu","raw_affiliation_strings":["Selcuk University,Department of Computer Engineering,Konya,Turkiye","Department of Computer Engineering, Selcuk University, Konya, Turkiye"],"raw_orcid":"https://orcid.org/0000-0002-2737-2360","affiliations":[{"raw_affiliation_string":"Selcuk University,Department of Computer Engineering,Konya,Turkiye","institution_ids":["https://openalex.org/I137996928"]},{"raw_affiliation_string":"Department of Computer Engineering, Selcuk University, Konya, Turkiye","institution_ids":["https://openalex.org/I137996928"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061155450","display_name":"Ramazan Kursun","orcid":"https://orcid.org/0000-0002-6729-1055"},"institutions":[{"id":"https://openalex.org/I137996928","display_name":"Sel\u00e7uk University","ror":"https://ror.org/045hgzm75","country_code":"TR","type":"education","lineage":["https://openalex.org/I137996928"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Ramazan Kursun","raw_affiliation_strings":["Guneysinir Vocational School, Selcuk University,Konya,Türkiye"],"raw_orcid":"https://orcid.org/0000-0002-6729-1055","affiliations":[{"raw_affiliation_string":"Guneysinir Vocational School, Selcuk University,Konya,Türkiye","institution_ids":["https://openalex.org/I137996928"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023379741","display_name":"Elham Tahsin Yasin","orcid":"https://orcid.org/0000-0003-3246-6000"},"institutions":[{"id":"https://openalex.org/I137996928","display_name":"Sel\u00e7uk University","ror":"https://ror.org/045hgzm75","country_code":"TR","type":"education","lineage":["https://openalex.org/I137996928"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Elham Tahsin Yasin","raw_affiliation_strings":["Graduate School of Natural and Applied Sciences, Selcuk University,Konya,Turkiye","Graduate School of Natural and Applied Sciences, Selcuk University, Konya, Turkiye"],"raw_orcid":"https://orcid.org/0000-0003-3246-6000","affiliations":[{"raw_affiliation_string":"Graduate School of Natural and Applied Sciences, Selcuk University,Konya,Turkiye","institution_ids":["https://openalex.org/I137996928"]},{"raw_affiliation_string":"Graduate School of Natural and Applied Sciences, Selcuk University, Konya, Turkiye","institution_ids":["https://openalex.org/I137996928"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069293570","display_name":"Yavuz Selim Ta\u015fp\u0131nar","orcid":"https://orcid.org/0000-0002-7278-4241"},"institutions":[{"id":"https://openalex.org/I137996928","display_name":"Sel\u00e7uk University","ror":"https://ror.org/045hgzm75","country_code":"TR","type":"education","lineage":["https://openalex.org/I137996928"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Yavuz Selim Taspinar","raw_affiliation_strings":["Doganhisar Vocational School, Selcuk University,Konya,Turkiye","Doganhisar Vocational School, Selcuk University, Konya, Turkiye"],"raw_orcid":"https://orcid.org/0000-0002-7278-4241","affiliations":[{"raw_affiliation_string":"Doganhisar Vocational School, Selcuk University,Konya,Turkiye","institution_ids":["https://openalex.org/I137996928"]},{"raw_affiliation_string":"Doganhisar Vocational School, Selcuk University, Konya, Turkiye","institution_ids":["https://openalex.org/I137996928"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I137996928"],"apc_list":null,"apc_paid":null,"fwci":2.5269,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.90171725,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"713","last_page":"717"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9944999814033508,"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":0.9944999814033508,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9944000244140625,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14365","display_name":"Leaf Properties and Growth Measurement","score":0.9509999752044678,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.8021503686904907},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7374097108840942},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6284031867980957},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6013169288635254},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5994143486022949},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5565913915634155},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.5328481793403625},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4906062185764313},{"id":"https://openalex.org/keywords/product","display_name":"Product (mathematics)","score":0.48160526156425476},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.45922794938087463},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4477255642414093},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4191119968891144},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.26519304513931274},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.18555456399917603}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.8021503686904907},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7374097108840942},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6284031867980957},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6013169288635254},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5994143486022949},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5565913915634155},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.5328481793403625},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4906062185764313},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.48160526156425476},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.45922794938087463},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4477255642414093},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4191119968891144},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26519304513931274},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.18555456399917603},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","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/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","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:10.1109/idaacs58523.2023.10348939","is_oa":false,"landing_page_url":"https://doi.org/10.1109/idaacs58523.2023.10348939","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 12th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/12","score":0.4000000059604645,"display_name":"Responsible consumption and production"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W2301363727","https://openalex.org/W3206646460","https://openalex.org/W4200411312","https://openalex.org/W4230668145","https://openalex.org/W4241522665","https://openalex.org/W4283270853","https://openalex.org/W4283277971","https://openalex.org/W4289594494","https://openalex.org/W4289831746","https://openalex.org/W4290987601","https://openalex.org/W4292833879","https://openalex.org/W4295117217","https://openalex.org/W4313499824","https://openalex.org/W4321242754","https://openalex.org/W4367187643","https://openalex.org/W6667973304","https://openalex.org/W6695314431","https://openalex.org/W6780462793","https://openalex.org/W6787578429","https://openalex.org/W6807959990","https://openalex.org/W6840947145","https://openalex.org/W6846847374","https://openalex.org/W6848161030","https://openalex.org/W6854378844"],"related_works":["https://openalex.org/W3193043704","https://openalex.org/W4386259002","https://openalex.org/W1546989560","https://openalex.org/W3171520305","https://openalex.org/W3135126032","https://openalex.org/W4396689146","https://openalex.org/W4200112873","https://openalex.org/W2955796858","https://openalex.org/W4224941037","https://openalex.org/W2004826645"],"abstract_inverted_index":{"A":[0,127],"vital":[1],"part":[2],"of":[3,7,20,40,92,130,162,176],"ensuring":[4],"the":[5,37,72,90,160,172],"quality":[6,39,169],"soybean":[8,21,30,41,66,124,131,177],"products":[9,42],"is":[10,34,137,149],"detecting":[11],"defects.":[12,48,179],"The":[13],"current":[14],"study":[15],"presents":[16],"a":[17,75],"five-category":[18],"classification":[19,175],"seed":[22,67,125,132,178],"defects:":[23],"broken,":[24],"immature,":[25],"intact,":[26],"skin-damaged,":[27],"and":[28,46,52,96,118,141,174],"spotted":[29],"seeds.":[31],"Our":[32],"goal":[33],"to":[35,59,122,139],"improve":[36],"overall":[38],"through":[43],"accurately":[44],"identifying":[45],"categorizing":[47],"Computer":[49],"vision":[50],"techniques":[51,165],"machine":[53,107,163],"learning":[54,108,164],"algorithms":[55],"are":[56,69],"combined":[57],"comprehensively":[58],"achieve":[60],"this":[61],"goal.":[62],"To":[63],"begin":[64],"with,":[65],"images":[68,133],"analyzed":[70],"using":[71],"SqueezeNet":[73],"model,":[74],"deep-learning":[76],"architecture":[77],"known":[78,135],"for":[79,151],"its":[80],"efficiency":[81],"in":[82,166],"image":[83],"analysis.":[84],"Features":[85],"extracted":[86],"from":[87],"soybeans":[88],"indicate":[89],"types":[91],"defects":[93,136],"they":[94],"present":[95],"their":[97],"key":[98],"visual":[99],"characteristics.":[100],"Then,":[101],"we":[102],"applied":[103],"three":[104],"widely":[105],"used":[106,138,150],"algorithms,":[109],"including":[110],"Artificial":[111],"Neural":[112],"Network":[113],"(ANN),":[114],"Logistic":[115],"Regression":[116],"(LR),":[117],"Random":[119],"Forest":[120],"(RF),":[121],"classify":[123],"images.":[126],"labeled":[128],"dataset":[129],"with":[134],"train":[140],"fine-tune":[142],"each":[143,153],"algorithm.":[144,154],"An":[145],"appropriate":[146],"evaluation":[147],"metric":[148],"assessing":[152],"It":[155],"provides":[156],"valuable":[157],"insights":[158],"into":[159],"application":[161],"improving":[167,171],"product":[168],"by":[170],"detection":[173]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2023,"cited_by_count":2}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}