{"id":"https://openalex.org/W7131635379","doi":"https://doi.org/10.1109/rif68108.2025.11406810","title":"Dataset Fusion and Multi-Task Learning for Robust Wheat Grain Storage Monitoring: A Survey","display_name":"Dataset Fusion and Multi-Task Learning for Robust Wheat Grain Storage Monitoring: A Survey","publication_year":2025,"publication_date":"2025-12-08","ids":{"openalex":"https://openalex.org/W7131635379","doi":"https://doi.org/10.1109/rif68108.2025.11406810"},"language":null,"primary_location":{"id":"doi:10.1109/rif68108.2025.11406810","is_oa":false,"landing_page_url":"https://doi.org/10.1109/rif68108.2025.11406810","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Research in Computing at Feminine (RIF)","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/A5126867361","display_name":"Djakhdjakha Lynda","orcid":null},"institutions":[{"id":"https://openalex.org/I4210097536","display_name":"University of Guelma","ror":"https://ror.org/00xe6p546","country_code":"DZ","type":"education","lineage":["https://openalex.org/I4210097536"]}],"countries":["DZ"],"is_corresponding":true,"raw_author_name":"Djakhdjakha Lynda","raw_affiliation_strings":["8 Mai 1945 Guelma University,LabSTIC Laboratory Computer Science Department,Guelma,Algeria,24000"],"affiliations":[{"raw_affiliation_string":"8 Mai 1945 Guelma University,LabSTIC Laboratory Computer Science Department,Guelma,Algeria,24000","institution_ids":["https://openalex.org/I4210097536"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126882759","display_name":"Abdelmoum\u00e8ne Hiba","orcid":null},"institutions":[{"id":"https://openalex.org/I4210097536","display_name":"University of Guelma","ror":"https://ror.org/00xe6p546","country_code":"DZ","type":"education","lineage":["https://openalex.org/I4210097536"]}],"countries":["DZ"],"is_corresponding":false,"raw_author_name":"Abdelmoum\u00e8ne Hiba","raw_affiliation_strings":["8 Mai 1945 Guelma University,Computer Science Department,Guelma,Algeria,24000"],"affiliations":[{"raw_affiliation_string":"8 Mai 1945 Guelma University,Computer Science Department,Guelma,Algeria,24000","institution_ids":["https://openalex.org/I4210097536"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126871013","display_name":"Bouacida Imane","orcid":null},"institutions":[{"id":"https://openalex.org/I4210097536","display_name":"University of Guelma","ror":"https://ror.org/00xe6p546","country_code":"DZ","type":"education","lineage":["https://openalex.org/I4210097536"]}],"countries":["DZ"],"is_corresponding":false,"raw_author_name":"Bouacida Imane","raw_affiliation_strings":["8 Mai 1945 Guelma University,LabSTIC Laboratory Computer Science Department,Guelma,Algeria,24000"],"affiliations":[{"raw_affiliation_string":"8 Mai 1945 Guelma University,LabSTIC Laboratory Computer Science Department,Guelma,Algeria,24000","institution_ids":["https://openalex.org/I4210097536"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5126933875","display_name":"Farou Brahim","orcid":null},"institutions":[{"id":"https://openalex.org/I4210097536","display_name":"University of Guelma","ror":"https://ror.org/00xe6p546","country_code":"DZ","type":"education","lineage":["https://openalex.org/I4210097536"]}],"countries":["DZ"],"is_corresponding":false,"raw_author_name":"Farou Brahim","raw_affiliation_strings":["8 Mai 1945 Guelma University,LabSTIC Laboratory Computer Science Department,Guelma,Algeria,24000"],"affiliations":[{"raw_affiliation_string":"8 Mai 1945 Guelma University,LabSTIC Laboratory Computer Science Department,Guelma,Algeria,24000","institution_ids":["https://openalex.org/I4210097536"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5126867361"],"corresponding_institution_ids":["https://openalex.org/I4210097536"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.82476938,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.5809999704360962,"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":0.5809999704360962,"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":0.27559998631477356,"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":0.010499999858438969,"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/quality","display_name":"Quality (philosophy)","score":0.5663999915122986},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.5335000157356262},{"id":"https://openalex.org/keywords/grain-quality","display_name":"Grain quality","score":0.48730000853538513},{"id":"https://openalex.org/keywords/wheat-grain","display_name":"Wheat grain","score":0.445499986410141},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.4025999903678894},{"id":"https://openalex.org/keywords/dual-purpose","display_name":"Dual purpose","score":0.310699999332428}],"concepts":[{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.5663999915122986},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.5335000157356262},{"id":"https://openalex.org/C2778520195","wikidata":"https://www.wikidata.org/wiki/Q16249780","display_name":"Grain quality","level":2,"score":0.48730000853538513},{"id":"https://openalex.org/C3020060512","wikidata":"https://www.wikidata.org/wiki/Q15645384","display_name":"Wheat grain","level":2,"score":0.445499986410141},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4287000000476837},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4187999963760376},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41749998927116394},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.4025999903678894},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39250001311302185},{"id":"https://openalex.org/C3019953569","wikidata":"https://www.wikidata.org/wiki/Q5310141","display_name":"Dual purpose","level":2,"score":0.310699999332428},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.30869999527931213},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.305400013923645},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.29789999127388},{"id":"https://openalex.org/C2992211155","wikidata":"https://www.wikidata.org/wiki/Q889514","display_name":"Grain yield","level":2,"score":0.2969000041484833},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.2815000116825104},{"id":"https://openalex.org/C88463610","wikidata":"https://www.wikidata.org/wiki/Q194118","display_name":"Agricultural engineering","level":1,"score":0.2678000032901764}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/rif68108.2025.11406810","is_oa":false,"landing_page_url":"https://doi.org/10.1109/rif68108.2025.11406810","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Research in Computing at Feminine (RIF)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/2","score":0.45302775502204895,"display_name":"Zero hunger"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W4200312742","https://openalex.org/W4210585700","https://openalex.org/W4214947750","https://openalex.org/W4285249424","https://openalex.org/W4313318274","https://openalex.org/W4319442249","https://openalex.org/W4377103899","https://openalex.org/W4385748583","https://openalex.org/W4387311095","https://openalex.org/W4388494095","https://openalex.org/W4388765364","https://openalex.org/W4389252731","https://openalex.org/W4396953858","https://openalex.org/W4399971500","https://openalex.org/W4400146891","https://openalex.org/W4404124697","https://openalex.org/W4407606555","https://openalex.org/W4411187405","https://openalex.org/W4412164554","https://openalex.org/W4413614441","https://openalex.org/W4413799713","https://openalex.org/W4416250127"],"related_works":[],"abstract_inverted_index":{"Stored":[0],"wheat":[1,62],"faces":[2],"dual":[3],"threats":[4],"from":[5],"intrinsic":[6],"quality":[7,64],"degradation":[8],"and":[9,43,52,66,108,111,125,131,137],"insect":[10,35,70,109],"infestations,":[11],"both":[12],"of":[13,41,57,133],"which":[14,37],"contribute":[15],"to":[16,122],"substantial":[17],"post-harvest":[18],"losses.":[19],"Existing":[20],"datasets":[21],"often":[22],"address":[23],"these":[24,75,134],"issues":[25],"separately,":[26],"focusing":[27],"either":[28],"on":[29,34,69,74],"grain":[30,63,106],"condition":[31,107],"classification":[32],"or":[33],"detection,":[36],"limits":[38],"the":[39],"development":[40],"robust":[42],"integrated":[44],"monitoring":[45],"solutions.":[46],"In":[47],"this":[48],"work,":[49],"we":[50,77],"describe":[51],"motivate":[53],"two":[54],"complementary":[55],"types":[56],"datasets:":[58],"those":[59,67],"oriented":[60],"toward":[61],"assessment":[65],"focused":[68],"infestation":[71],"monitoring.":[72],"Building":[73],"resources,":[76],"present":[78],"three":[79],"fusion":[80],"strategies:":[81],"(1)":[82],"Feature-Level":[83],"fusion,":[84],"where":[85,97,117],"extracted":[86],"features":[87],"are":[88,120],"merged":[89],"into":[90],"a":[91,98,140,144],"joint":[92],"representation;":[93],"(2)":[94],"Multi-Task":[95],"Learning,":[96],"shared":[99],"backbone":[100],"network":[101],"supports":[102],"separate":[103],"tasks":[104],"for":[105,143],"identification;":[110],"(3)":[112],"Decision-Level":[113],"Fusion":[114],"(Pipeline":[115],"Approach),":[116],"independent":[118],"models":[119],"combined":[121],"produce":[123],"interpretable":[124],"causally":[126],"informed":[127],"outcomes.":[128],"The":[129],"description":[130],"analysis":[132],"dataset":[135],"categories":[136],"strategies":[138],"provide":[139],"solid":[141],"foundation":[142],"forthcoming":[145],"comparative":[146],"study.":[147]},"counts_by_year":[],"updated_date":"2026-02-27T14:28:36.762950","created_date":"2026-02-27T00:00:00"}
