{"id":"https://openalex.org/W7129357699","doi":"https://doi.org/10.48550/arxiv.2602.14408","title":"Feature Recalibration Based Olfactory-Visual Multimodal Model for Enhanced Rice Deterioration Detection","display_name":"Feature Recalibration Based Olfactory-Visual Multimodal Model for Enhanced Rice Deterioration Detection","publication_year":2026,"publication_date":"2026-02-16","ids":{"openalex":"https://openalex.org/W7129357699","doi":"https://doi.org/10.48550/arxiv.2602.14408"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.14408","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5004174956","display_name":"Rongqiang Zhao","orcid":"https://orcid.org/0000-0003-0131-9542"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Rongqiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126259150","display_name":"Hengrui Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Hengrui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100780978","display_name":"Yijing Wang","orcid":"https://orcid.org/0000-0002-0862-6564"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yijing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126183942","display_name":"Mingchun Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Mingchun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5126180241","display_name":"Jie Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Jie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13913201,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.7802000045776367,"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":0.7802000045776367,"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.12300000339746475,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.01899999938905239,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6891999840736389},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.5979999899864197},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5641999840736389},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.5209000110626221},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.487199991941452},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4837000072002411},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.40310001373291016}],"concepts":[{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6891999840736389},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6729000210762024},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6420000195503235},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.5979999899864197},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5641999840736389},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.5209000110626221},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.487199991941452},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4837000072002411},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.40310001373291016},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.3822000026702881},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.34380000829696655},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.32679998874664307},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3073999881744385},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28139999508857727},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.272599995136261},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.26820001006126404},{"id":"https://openalex.org/C2992726227","wikidata":"https://www.wikidata.org/wiki/Q5090","display_name":"Rice plant","level":2,"score":0.2653000056743622},{"id":"https://openalex.org/C120217122","wikidata":"https://www.wikidata.org/wiki/Q740083","display_name":"Precision agriculture","level":3,"score":0.25279998779296875}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.14408","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.14408","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.14408","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.14408","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"display_name":"Zero hunger","score":0.7432713508605957,"id":"https://metadata.un.org/sdg/2"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Multimodal":[0],"methods":[1,23],"are":[2],"widely":[3],"used":[4],"in":[5,14,138,155],"rice":[6,58,102],"deterioration":[7,59,63,83,99],"detection,":[8],"but":[9],"they":[10],"exhibit":[11],"limited":[12],"capability":[13],"representing":[15],"and":[16,31,38,94,141,157],"extracting":[17],"fine-grained":[18,62,82,98],"abnormal":[19],"features.":[20],"Moreover,":[21],"these":[22,45],"rely":[24],"on":[25,100],"devices":[26],"such":[27],"as":[28],"hyperspectral":[29],"cameras":[30],"mass":[32],"spectrometers,":[33],"which":[34],"increase":[35],"detection":[36,130,134],"costs":[37],"prolong":[39],"data":[40],"acquisition":[41],"time.":[42],"To":[43],"address":[44],"issues,":[46],"we":[47],"propose":[48],"a":[49],"feature":[50,75],"recalibration":[51,84],"based":[52],"olfactory-visual":[53],"multimodal":[54,73],"model":[55],"for":[56],"enhanced":[57],"detection.":[60],"A":[61,81],"embedding":[64],"constructor":[65],"(FDEC)":[66],"is":[67,88],"proposed":[68,89,108,145],"to":[69,90,97,151],"reconstruct":[70],"the":[71,101,107,129,158],"labeled":[72],"embedded":[74],"dataset,":[76],"thereby":[77],"enhancing":[78],"sample":[79],"representation.":[80],"attention":[85],"network":[86],"(FDRA-Net)":[87],"emphasize":[91],"signal":[92],"variations":[93],"improve":[95],"sensitivity":[96],"surface.":[103],"Compared":[104],"with":[105,115],"SS-Net,":[106],"method":[109,146],"improves":[110],"classification":[111],"accuracy":[112,140],"by":[113],"8.67%,":[114],"an":[116],"average":[117],"improvement":[118],"of":[119],"11.51%":[120],"over":[121],"other":[122,152],"traditional":[123],"baseline":[124],"models,":[125],"while":[126],"simultaneously":[127],"simplifying":[128],"procedure.":[131],"Furthermore,":[132],"field":[133],"results":[135],"demonstrate":[136],"advantages":[137],"both":[139],"operational":[142],"simplicity.":[143],"The":[144],"can":[147],"also":[148],"be":[149],"extended":[150],"agrifood":[153],"applications":[154],"agriculture":[156],"food":[159],"industry.":[160]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-02-18T00:00:00"}