{"id":"https://openalex.org/W4399938994","doi":"https://doi.org/10.1109/access.2024.3419006","title":"Hyperspectral RGB Imaging Combined With Deep Learning for Maize Seed Variety Identification","display_name":"Hyperspectral RGB Imaging Combined With Deep Learning for Maize Seed Variety Identification","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4399938994","doi":"https://doi.org/10.1109/access.2024.3419006"},"language":"en","primary_location":{"id":"doi:10.1109/access.2024.3419006","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3419006","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":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://doi.org/10.1109/access.2024.3419006","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100402467","display_name":"Jian Li","orcid":"https://orcid.org/0000-0002-0864-5108"},"institutions":[{"id":"https://openalex.org/I4210152006","display_name":"Jilin Agricultural University","ror":"https://ror.org/05dmhhd41","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152006"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jian Li","raw_affiliation_strings":["College of Information Technology, Jilin Agricultural University, Changchun, China"],"affiliations":[{"raw_affiliation_string":"College of Information Technology, Jilin Agricultural University, Changchun, China","institution_ids":["https://openalex.org/I4210152006"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5099431792","display_name":"Fan Xu","orcid":"https://orcid.org/0009-0005-6535-9297"},"institutions":[{"id":"https://openalex.org/I4210152006","display_name":"Jilin Agricultural University","ror":"https://ror.org/05dmhhd41","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152006"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fan Xu","raw_affiliation_strings":["College of Information Technology, Jilin Agricultural University, Changchun, China"],"affiliations":[{"raw_affiliation_string":"College of Information Technology, Jilin Agricultural University, Changchun, China","institution_ids":["https://openalex.org/I4210152006"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063007976","display_name":"Shaozhong Song","orcid":null},"institutions":[{"id":"https://openalex.org/I4210102505","display_name":"Jilin Engineering Normal University","ror":"https://ror.org/018gks972","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210102505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shaozhong Song","raw_affiliation_strings":["School of Data Science and Artificial Intelligence, Jilin Engineering Normal University, Changchun, China"],"affiliations":[{"raw_affiliation_string":"School of Data Science and Artificial Intelligence, Jilin Engineering Normal University, Changchun, China","institution_ids":["https://openalex.org/I4210102505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075561806","display_name":"Ji Qi","orcid":"https://orcid.org/0000-0002-0539-8818"},"institutions":[{"id":"https://openalex.org/I4210152006","display_name":"Jilin Agricultural University","ror":"https://ror.org/05dmhhd41","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152006"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qi Ji","raw_affiliation_strings":["College of Engineering Technical, Jilin Agricultural University, Changchun, China"],"affiliations":[{"raw_affiliation_string":"College of Engineering Technical, Jilin Agricultural University, Changchun, China","institution_ids":["https://openalex.org/I4210152006"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100701949","display_name":"Junling Liu","orcid":"https://orcid.org/0000-0002-6492-3408"},"institutions":[{"id":"https://openalex.org/I4210102505","display_name":"Jilin Engineering Normal University","ror":"https://ror.org/018gks972","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210102505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junling Liu","raw_affiliation_strings":["School of Data Science and Artificial Intelligence, Jilin Engineering Normal University, Changchun, China"],"affiliations":[{"raw_affiliation_string":"School of Data Science and Artificial Intelligence, Jilin Engineering Normal University, Changchun, China","institution_ids":["https://openalex.org/I4210102505"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100402467"],"corresponding_institution_ids":["https://openalex.org/I4210152006"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.2462,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.75780132,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"12","issue":null,"first_page":"184477","last_page":"184486"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9868000149726868,"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"}},"topics":[{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9868000149726868,"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9508000016212463,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.9046000242233276},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.6442054510116577},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6139892339706421},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6021662354469299},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.575837254524231},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.49510207772254944},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.438070684671402},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.4255322813987732},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41408392786979675},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.35699641704559326},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.1275826394557953},{"id":"https://openalex.org/keywords/botany","display_name":"Botany","score":0.10257220268249512},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.09719038009643555}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.9046000242233276},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.6442054510116577},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6139892339706421},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6021662354469299},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.575837254524231},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.49510207772254944},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.438070684671402},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4255322813987732},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41408392786979675},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.35699641704559326},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.1275826394557953},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.10257220268249512},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.09719038009643555}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2024.3419006","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3419006","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":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:db1fcac0120c4939b1342fa8914f0a0c","is_oa":true,"landing_page_url":"https://doaj.org/article/db1fcac0120c4939b1342fa8914f0a0c","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 12, Pp 184477-184486 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2024.3419006","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3419006","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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.7099999785423279,"id":"https://metadata.un.org/sdg/2","display_name":"Zero hunger"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W348678141","https://openalex.org/W1677796994","https://openalex.org/W2055525259","https://openalex.org/W2063057245","https://openalex.org/W2073023583","https://openalex.org/W2096985690","https://openalex.org/W2144169294","https://openalex.org/W2194775991","https://openalex.org/W2252452514","https://openalex.org/W2280795818","https://openalex.org/W2496844762","https://openalex.org/W2776202363","https://openalex.org/W2780348300","https://openalex.org/W2792817593","https://openalex.org/W2907164533","https://openalex.org/W2911220996","https://openalex.org/W2917883159","https://openalex.org/W2962878352","https://openalex.org/W2963420686","https://openalex.org/W2987761193","https://openalex.org/W3049379723","https://openalex.org/W3092667979","https://openalex.org/W3124859927","https://openalex.org/W3159427491","https://openalex.org/W3177052299","https://openalex.org/W4200272464","https://openalex.org/W4200590595","https://openalex.org/W4220653958","https://openalex.org/W4254690144","https://openalex.org/W4289752563","https://openalex.org/W6746023985"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2060875994","https://openalex.org/W3034375524","https://openalex.org/W4230131218","https://openalex.org/W2070598848","https://openalex.org/W2044184146","https://openalex.org/W4313014865","https://openalex.org/W2019190440","https://openalex.org/W2343470940"],"abstract_inverted_index":{"Variety":[0],"purity":[1],"is":[2,12],"an":[3,212],"essential":[4],"indicator":[5],"in":[6,87],"seed":[7,20,241],"quality":[8],"detection.":[9],"Thus,":[10],"it":[11,183],"necessary":[13],"to":[14,37,49,65,117,138,211],"rapidly":[15],"and":[16,44,71,93,124,142,182,209,237],"non-destructively":[17],"detect":[18],"the":[19,51,59,68,99,106,112,118,126,136,156,158,163,178,194,197,200,239],"purity.":[21],"Unlike":[22],"traditional":[23],"methods":[24],"for":[25,83,235],"processing":[26],"hyperspectral":[27,42,62,224],"data,":[28],"this":[29],"study":[30,220],"focuses":[31],"on":[32,199],"computer":[33],"vision.":[34],"It":[35],"aims":[36],"reconstruct":[38],"RGB":[39,180,188],"images":[40],"from":[41],"data":[43,64],"employ":[45],"deep":[46,228],"learning":[47,229],"techniques":[48],"identify":[50],"varieties":[52],"of":[53,61,120,170,196,216],"corn":[54],"seeds.":[55],"Firstly,":[56],"we":[57,104],"utilized":[58],"diversity":[60],"band":[63],"selectively":[66],"screen":[67],"R,":[69],"G,":[70],"B":[72],"bands":[73,79],"with":[74,91,131,227],"strong":[75],"feature":[76],"correlations.":[77],"These":[78],"were":[80],"then":[81],"employed":[82],"pseudocolor":[84],"reconstruction,":[85],"resulting":[86],"a":[88,167,174,232],"reconstructed":[89,159,201],"dataset":[90,160,181,202],"distinct":[92],"more":[94,140],"interpretive":[95],"color":[96],"characteristics":[97],"than":[98],"original":[100,179],"dataset.":[101],"After":[102],"that,":[103],"improved":[105],"classic":[107],"ResNet50":[108],"model":[109,137,192,198],"by":[110,162],"adding":[111],"coordinate":[113],"attention":[114],"(CA)":[115],"mechanism":[116],"end":[119],"each":[121],"residual":[122],"block":[123],"replacing":[125],"global":[127],"ReLU":[128],"activation":[129],"function":[130],"SiLU.":[132],"This":[133],"improvement":[134,176,215],"enabled":[135],"capture":[139],"precise":[141],"detailed":[143],"features":[144],"while":[145],"enhancing":[146],"its":[147],"predictive":[148],"capability.":[149],"The":[150,218],"results":[151],"showed":[152],"that":[153,222],"without":[154],"improving":[155],"model,":[157],"generated":[161,187],"proposed":[164],"method":[165],"achieved":[166],"classification":[168],"accuracy":[169,195,214],"86.28%,":[171],"which":[172],"was":[173],"2.94%":[175],"over":[177],"outperforms":[184],"100":[185],"randomly":[186],"combinations.":[189],"While":[190],"incorporating":[191],"improvements,":[193],"reached":[203],"88.18%,":[204],"surpassing":[205],"other":[206],"relevant":[207],"models":[208],"leading":[210],"overall":[213,219],"4.79%.":[217],"demonstrated":[221],"combining":[223],"image":[225],"reconstruction":[226],"could":[230],"be":[231],"meaningful":[233],"tool":[234],"identifying":[236],"detecting":[238],"maize":[240],"variety.":[242]},"counts_by_year":[{"year":2025,"cited_by_count":5}],"updated_date":"2025-12-22T23:10:17.713674","created_date":"2025-10-10T00:00:00"}
