{"id":"https://openalex.org/W4415707551","doi":"https://doi.org/10.1109/access.2025.3626714","title":"A Method for Quality Grading and Endogenous Impurity Detection of Walnut Kernels Based on Lightweight Deep Learning","display_name":"A Method for Quality Grading and Endogenous Impurity Detection of Walnut Kernels Based on Lightweight Deep Learning","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4415707551","doi":"https://doi.org/10.1109/access.2025.3626714"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3626714","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3626714","pdf_url":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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://doi.org/10.1109/access.2025.3626714","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102638506","display_name":"Hongsen Liao","orcid":"https://orcid.org/0009-0006-7980-9057"},"institutions":[{"id":"https://openalex.org/I25399270","display_name":"Southwest Forestry University","ror":"https://ror.org/03dfa9f06","country_code":"CN","type":"education","lineage":["https://openalex.org/I25399270"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hongsen Liao","raw_affiliation_strings":["School of Mechanical and Transportation Engineering, Southwest Forestry University, Kunming, China"],"raw_orcid":"https://orcid.org/0009-0006-7980-9057","affiliations":[{"raw_affiliation_string":"School of Mechanical and Transportation Engineering, Southwest Forestry University, Kunming, China","institution_ids":["https://openalex.org/I25399270"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Wei Li","orcid":"https://orcid.org/0009-0002-2908-2807"},"institutions":[{"id":"https://openalex.org/I25399270","display_name":"Southwest Forestry University","ror":"https://ror.org/03dfa9f06","country_code":"CN","type":"education","lineage":["https://openalex.org/I25399270"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Li","raw_affiliation_strings":["School of Mechanical and Transportation Engineering, Southwest Forestry University, Kunming, China"],"raw_orcid":"https://orcid.org/0009-0002-2908-2807","affiliations":[{"raw_affiliation_string":"School of Mechanical and Transportation Engineering, Southwest Forestry University, Kunming, China","institution_ids":["https://openalex.org/I25399270"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005037287","display_name":"Shaomin Lu","orcid":null},"institutions":[{"id":"https://openalex.org/I25399270","display_name":"Southwest Forestry University","ror":"https://ror.org/03dfa9f06","country_code":"CN","type":"education","lineage":["https://openalex.org/I25399270"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shaomin Lu","raw_affiliation_strings":["School of Mechanical and Transportation Engineering, Southwest Forestry University, Kunming, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mechanical and Transportation Engineering, Southwest Forestry University, Kunming, China","institution_ids":["https://openalex.org/I25399270"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100610167","display_name":"Jianlong Li","orcid":"https://orcid.org/0000-0001-7845-6006"},"institutions":[{"id":"https://openalex.org/I25399270","display_name":"Southwest Forestry University","ror":"https://ror.org/03dfa9f06","country_code":"CN","type":"education","lineage":["https://openalex.org/I25399270"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianlong Li","raw_affiliation_strings":["School of Mechanical and Transportation Engineering, Southwest Forestry University, Kunming, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mechanical and Transportation Engineering, Southwest Forestry University, Kunming, China","institution_ids":["https://openalex.org/I25399270"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jianchao Wang","orcid":"https://orcid.org/0009-0005-4128-2352"},"institutions":[{"id":"https://openalex.org/I25399270","display_name":"Southwest Forestry University","ror":"https://ror.org/03dfa9f06","country_code":"CN","type":"education","lineage":["https://openalex.org/I25399270"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianchao Wang","raw_affiliation_strings":["School of Mechanical and Transportation Engineering, Southwest Forestry University, Kunming, China"],"raw_orcid":"https://orcid.org/0009-0005-4128-2352","affiliations":[{"raw_affiliation_string":"School of Mechanical and Transportation Engineering, Southwest Forestry University, Kunming, China","institution_ids":["https://openalex.org/I25399270"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063417397","display_name":"Jianan Bai","orcid":"https://orcid.org/0000-0002-7064-5280"},"institutions":[{"id":"https://openalex.org/I25399270","display_name":"Southwest Forestry University","ror":"https://ror.org/03dfa9f06","country_code":"CN","type":"education","lineage":["https://openalex.org/I25399270"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianan Bai","raw_affiliation_strings":["School of Mechanical and Transportation Engineering, Southwest Forestry University, Kunming, China"],"raw_orcid":"https://orcid.org/0009-0000-5614-0502","affiliations":[{"raw_affiliation_string":"School of Mechanical and Transportation Engineering, Southwest Forestry University, Kunming, China","institution_ids":["https://openalex.org/I25399270"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120153209","display_name":"Hailong Ti","orcid":"https://orcid.org/0009-0002-2652-5254"},"institutions":[{"id":"https://openalex.org/I25399270","display_name":"Southwest Forestry University","ror":"https://ror.org/03dfa9f06","country_code":"CN","type":"education","lineage":["https://openalex.org/I25399270"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hailong Ti","raw_affiliation_strings":["School of Mechanical and Transportation Engineering, Southwest Forestry University, Kunming, China"],"raw_orcid":"https://orcid.org/0009-0002-2652-5254","affiliations":[{"raw_affiliation_string":"School of Mechanical and Transportation Engineering, Southwest Forestry University, Kunming, China","institution_ids":["https://openalex.org/I25399270"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100347848","display_name":"Jiahao Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I25399270","display_name":"Southwest Forestry University","ror":"https://ror.org/03dfa9f06","country_code":"CN","type":"education","lineage":["https://openalex.org/I25399270"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiahao Liu","raw_affiliation_strings":["School of Mechanical and Transportation Engineering, Southwest Forestry University, Kunming, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mechanical and Transportation Engineering, Southwest Forestry University, Kunming, China","institution_ids":["https://openalex.org/I25399270"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5102638506"],"corresponding_institution_ids":["https://openalex.org/I25399270"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.21678282,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":null,"first_page":"191267","last_page":"191281"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.4535999894142151,"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":0.4535999894142151,"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":0.2759999930858612,"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/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.07689999788999557,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6696000099182129},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.49559998512268066},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4496999979019165},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.3921999931335449},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.38909998536109924},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38609999418258667},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.3463999927043915},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.34450000524520874}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7290999889373779},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7281000018119812},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6696000099182129},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.49559998512268066},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4733000099658966},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4496999979019165},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.3921999931335449},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.38909998536109924},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38609999418258667},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3463999927043915},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.34450000524520874},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.33250001072883606},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.3276999890804291},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3109000027179718},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.3098999857902527},{"id":"https://openalex.org/C106436119","wikidata":"https://www.wikidata.org/wiki/Q836575","display_name":"Quality assurance","level":3,"score":0.30880001187324524},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.305400013923645},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.27649998664855957},{"id":"https://openalex.org/C2780898871","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Performance metric","level":2,"score":0.2639999985694885},{"id":"https://openalex.org/C2777286243","wikidata":"https://www.wikidata.org/wiki/Q5591926","display_name":"Grading (engineering)","level":2,"score":0.2632000148296356},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.2614000141620636},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.2563999891281128},{"id":"https://openalex.org/C108882727","wikidata":"https://www.wikidata.org/wiki/Q2991685","display_name":"Solid modeling","level":2,"score":0.2526000142097473},{"id":"https://openalex.org/C2777984123","wikidata":"https://www.wikidata.org/wiki/Q9248237","display_name":"Pointwise","level":2,"score":0.25130000710487366}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3626714","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3626714","pdf_url":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:da3b5703f51248fbb46064b516f09a8c","is_oa":true,"landing_page_url":"https://doaj.org/article/da3b5703f51248fbb46064b516f09a8c","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 13, Pp 191267-191281 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3626714","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3626714","pdf_url":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3971765221","display_name":null,"funder_award_id":"202402AE090027","funder_id":"https://openalex.org/F4320335960","funder_display_name":"National Major Science and Technology Projects of China"},{"id":"https://openalex.org/G4767806783","display_name":null,"funder_award_id":"52465023","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/F4320335960","display_name":"National Major Science and Technology Projects of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W2005119684","https://openalex.org/W2883780447","https://openalex.org/W3019319451","https://openalex.org/W3023573652","https://openalex.org/W3024262385","https://openalex.org/W3034330299","https://openalex.org/W3157905703","https://openalex.org/W4205339850","https://openalex.org/W4293149054","https://openalex.org/W4308157696","https://openalex.org/W4315474362","https://openalex.org/W4316810517","https://openalex.org/W4367187019","https://openalex.org/W4380201446","https://openalex.org/W4382931781","https://openalex.org/W4383032755","https://openalex.org/W4387132759","https://openalex.org/W4390110745","https://openalex.org/W4392460995","https://openalex.org/W4399593484","https://openalex.org/W4399946473","https://openalex.org/W4401019060","https://openalex.org/W4402732941","https://openalex.org/W4403991712","https://openalex.org/W4404998271","https://openalex.org/W4407360620","https://openalex.org/W4407802537","https://openalex.org/W4408399998","https://openalex.org/W4409429686","https://openalex.org/W4410235364","https://openalex.org/W4410607301"],"related_works":[],"abstract_inverted_index":{"This":[0,71,201],"paper":[1],"proposes":[2],"a":[3,98],"lightweight":[4],"detection":[5,29,217],"model":[6,79,173,202],"YOLO-WQ":[7,134],"based":[8],"on":[9,137,188],"improved":[10],"YOLOv11n":[11],"to":[12,38,108,118],"meet":[13],"the":[14,78,85,90,120,144,162,172,180],"multiple":[15,189],"requirements":[16],"of":[17,92,123],"high":[18,64,204],"precision,":[19],"lightweight,":[20],"and":[21,26,47,54,68,94,106,127,146,151,158,179,206,208,215],"real-time":[22,212],"for":[23,211],"quality":[24,213],"grading":[25],"endogenous":[27],"impurity":[28,216],"in":[30,219],"walnut":[31,45,124,221],"kernel":[32],"processing.":[33],"Traditional":[34],"methods":[35],"are":[36],"difficult":[37],"effectively":[39],"identify":[40],"small":[41],"targets":[42],"such":[43],"as":[44,84],"kernels":[46],"their":[48],"impurities":[49],"with":[50],"complex":[51],"surface":[52],"textures":[53],"similar":[55],"shapes,":[56],"while":[57],"existing":[58],"deep":[59],"learning":[60],"models":[61],"suffer":[62],"from":[63,77],"computational":[65,95,166],"resource":[66],"consumption":[67],"deployment":[69],"difficulties.":[70],"study":[72],"conducts":[73],"triple":[74],"optimization":[75],"starting":[76],"structure:":[80],"ShuffleNetV2":[81],"is":[82,209],"used":[83],"backbone":[86],"network,":[87],"significantly":[88],"reducing":[89],"number":[91],"parameters":[93],"burden;":[96],"Design":[97],"Slim":[99],"neck":[100,101],"structure":[102],"that":[103,133],"integrates":[104],"GSConv":[105],"VoV-GSCSP":[107],"enhance":[109],"multi-scale":[110],"feature":[111],"fusion":[112],"capability;":[113],"Introducing":[114],"SEAM":[115],"attention":[116],"mechanism":[117],"improve":[119],"perception":[121],"accuracy":[122,145],"groove":[125],"details":[126],"occluded":[128],"areas.":[129],"Experiments":[130],"have":[131],"shown":[132],"achieves":[135],"success":[136],"self":[138],"built":[139],"datasets":[140,194],"mAP@0.5":[141],"At":[142],"90.6%,":[143],"recall":[147],"rates":[148],"reached":[149,183],"84.8%":[150],"87.4%,":[152],"respectively,":[153],"which":[154],"were":[155],"2.5%,":[156],"4.2%,":[157],"3.7%":[159],"higher":[160],"than":[161],"original":[163],"model.":[164],"The":[165],"load":[167],"(FLOPs)":[168],"decreased":[169],"by":[170,177],"44.4%,":[171],"volume":[174],"was":[175],"reduced":[176],"20%,":[178],"inference":[181],"speed":[182],"157.09":[184],"fps.":[185],"Further":[186],"testing":[187],"publicly":[190],"available":[191],"agricultural":[192],"product":[193],"has":[195],"confirmed":[196],"its":[197],"excellent":[198],"generalization":[199],"performance.":[200],"combines":[203],"precision":[205],"efficiency,":[207],"suitable":[210],"monitoring":[214],"tasks":[218],"actual":[220],"processing":[222],"production":[223],"lines.":[224]},"counts_by_year":[],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-30T00:00:00"}