{"id":"https://openalex.org/W4408062187","doi":"https://doi.org/10.5220/0013110800003890","title":"Fruit-HSNet: A Machine Learning Approach for Hyperspectral Image-Based Fruit Ripeness Prediction","display_name":"Fruit-HSNet: A Machine Learning Approach for Hyperspectral Image-Based Fruit Ripeness Prediction","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4408062187","doi":"https://doi.org/10.5220/0013110800003890"},"language":"en","primary_location":{"id":"doi:10.5220/0013110800003890","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0013110800003890","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 17th International Conference on Agents and Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.5220/0013110800003890","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5113051639","display_name":"Ahmed Baha Ben Jmaa","orcid":null},"institutions":[{"id":"https://openalex.org/I117841876","display_name":"Universit\u00e9 Paris-Panth\u00e9on-Assas","ror":"https://ror.org/04qb2qm38","country_code":"FR","type":"education","lineage":["https://openalex.org/I117841876"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Ahmed Baha Ben Jmaa","raw_affiliation_strings":["Efrei Research Lab, Paris Panth\u00e9on-Assas University, Paris, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Efrei Research Lab, Paris Panth\u00e9on-Assas University, Paris, France","institution_ids":["https://openalex.org/I117841876"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015939203","display_name":"Faten Chaieb","orcid":"https://orcid.org/0000-0002-2968-2426"},"institutions":[{"id":"https://openalex.org/I117841876","display_name":"Universit\u00e9 Paris-Panth\u00e9on-Assas","ror":"https://ror.org/04qb2qm38","country_code":"FR","type":"education","lineage":["https://openalex.org/I117841876"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Faten Chaieb","raw_affiliation_strings":["Efrei Research Lab, Paris Panth\u00e9on-Assas University, Paris, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Efrei Research Lab, Paris Panth\u00e9on-Assas University, Paris, France","institution_ids":["https://openalex.org/I117841876"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039149575","display_name":"Anna Fabija\u0144ska","orcid":"https://orcid.org/0000-0002-0249-7247"},"institutions":[{"id":"https://openalex.org/I188884621","display_name":"Lodz University of Technology","ror":"https://ror.org/00s8fpf52","country_code":"PL","type":"education","lineage":["https://openalex.org/I188884621"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Anna Fabija\u0144ska","raw_affiliation_strings":["Institute of Applied Computer Science, Lodz University of Technology, \u0141\u00f3d\u017a, Poland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Applied Computer Science, Lodz University of Technology, \u0141\u00f3d\u017a, Poland","institution_ids":["https://openalex.org/I188884621"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.0157,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.93035994,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"102","last_page":"111"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.992900013923645,"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":0.992900013923645,"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/T10616","display_name":"Smart Agriculture and AI","score":0.9567999839782715,"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/T14249","display_name":"Water Quality Monitoring and Analysis","score":0.9409000277519226,"subfield":{"id":"https://openalex.org/subfields/2311","display_name":"Waste Management and Disposal"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/ripeness","display_name":"Ripeness","score":0.9889217615127563},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8350037336349487},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5880780220031738},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5435447692871094},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.43455690145492554},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.39760398864746094},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3852904140949249},{"id":"https://openalex.org/keywords/agricultural-engineering","display_name":"Agricultural engineering","score":0.3346695303916931},{"id":"https://openalex.org/keywords/horticulture","display_name":"Horticulture","score":0.17020580172538757},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.14710062742233276},{"id":"https://openalex.org/keywords/ripening","display_name":"Ripening","score":0.12297698855400085},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.10020223259925842}],"concepts":[{"id":"https://openalex.org/C2780393073","wikidata":"https://www.wikidata.org/wiki/Q7335586","display_name":"Ripeness","level":3,"score":0.9889217615127563},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8350037336349487},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5880780220031738},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5435447692871094},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.43455690145492554},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.39760398864746094},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3852904140949249},{"id":"https://openalex.org/C88463610","wikidata":"https://www.wikidata.org/wiki/Q194118","display_name":"Agricultural engineering","level":1,"score":0.3346695303916931},{"id":"https://openalex.org/C144027150","wikidata":"https://www.wikidata.org/wiki/Q48803","display_name":"Horticulture","level":1,"score":0.17020580172538757},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14710062742233276},{"id":"https://openalex.org/C172353545","wikidata":"https://www.wikidata.org/wiki/Q2121926","display_name":"Ripening","level":2,"score":0.12297698855400085},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.10020223259925842}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.5220/0013110800003890","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0013110800003890","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 17th International Conference on Agents and Artificial Intelligence","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2608.01202","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.01202","pdf_url":"https://arxiv.org/pdf/2608.01202","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:HAL:hal-05312665v1","is_oa":false,"landing_page_url":"https://hal.science/hal-05312665","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"17th International Conference on Agents and Artificial Intelligence (ICAART 2025)-, Feb 2025, Porto, France. pp.102-111, &#x27E8;10.5220/0013110800003890&#x27E9;","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"doi:10.48550/arxiv.2608.01202","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2608.01202","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":"doi:10.5220/0013110800003890","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0013110800003890","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 17th International Conference on Agents and Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.49000000953674316,"display_name":"Zero hunger","id":"https://metadata.un.org/sdg/2"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321415","display_name":"Agence Universitaire de la Francophonie","ror":"https://ror.org/009mq9d73"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4390619974","https://openalex.org/W2395543297","https://openalex.org/W2163911526","https://openalex.org/W2320112513","https://openalex.org/W2012057384","https://openalex.org/W2189509717","https://openalex.org/W3162834845","https://openalex.org/W2729298216","https://openalex.org/W4210897843","https://openalex.org/W2522789803"],"abstract_inverted_index":{"Fruit":[0,130],"ripeness":[1,120],"prediction":[2],"(FRP)":[3],"is":[4],"a":[5,82,96,116,187],"classification-based":[6],"agricultural":[7],"computer":[8],"vision":[9],"task":[10],"that":[11,169],"has":[12],"attracted":[13],"much":[14],"attention,":[15],"thanks":[16],"to":[17,60,179],"its":[18],"wide-ranging":[19],"advantages":[20],"in":[21],"agriculture":[22],"field":[23],"for":[24,88,119,140],"both":[25],"pre-harvest":[26],"and":[27,31,53,64,105,115,154],"post-harvest":[28],"management.":[29],"Accurate":[30],"timely":[32],"FRP":[33],"can":[34,67],"be":[35],"achieved":[36],"using":[37,127],"machine/deep":[38],"learning-based":[39],"hyperspectral":[40,62,72,89,138,159],"image":[41],"classification":[42,90],"techniques.":[43],"However,":[44],"challenges":[45],"including":[46],"the":[47,54,69,128,132],"limited":[48],"availability":[49],"of":[50,56,71,91,149,164,184,192],"labeled":[51,136],"data":[52],"lack":[55],"robust":[57],"methods":[58],"generalizable":[59],"various":[61,162],"cameras":[63,160],"fruit":[65,92,142],"types":[66,148],"compromise":[68],"effectiveness":[70],"image-based":[73],"FRP.":[74],"Addressing":[75],"these":[76],"challenges,":[77],"this":[78],"paper":[79],"introduces":[80],"Fruit-HSNet,":[81],"machine":[83],"learning":[84,175],"architecture":[85,124],"specifically":[86],"designed":[87],"ripeness.":[93,165],"Fruit-HSNet":[94,170],"incorporates":[95],"spatio-spectral":[97],"feature":[98,113],"extraction":[99],"module":[100],"based":[101],"on":[102],"Fourier":[103],"Transform":[104],"central":[106],"pixel":[107],"spectral":[108],"signature":[109],"followed":[110],"by":[111],"learnable":[112],"fusion":[114],"classifier":[117],"optimized":[118],"classification.":[121],"The":[122],"proposed":[123],"was":[125],"evaluated":[126],"DeepHS":[129],"dataset,":[131],"largest":[133],"publicly":[134],"available":[135],"real-world":[137],"dataset":[139],"predicting":[141],"ripeness,":[143],"which":[144],"includes":[145],"five":[146],"different":[147],"fruits-avocado,":[150],"kiwi,":[151],"mango,":[152],"kaki,":[153],"papaya-captured":[155],"with":[156,182],"three":[157],"distinct":[158],"at":[161],"stages":[163],"Experimental":[166],"results":[167],"highlight":[168],"substantially":[171],"outperforms":[172],"existing":[173],"deep":[174],"methods,":[176],"from":[177],"baseline":[178],"state-of-the-art":[180,189],"models,":[181],"improvements":[183],"12%,":[185],"achieving":[186],"new":[188],"overall":[190],"accuracy":[191],"70.73%.":[193]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-08-18T07:49:30.821534","created_date":"2025-03-01T00:00:00"}
