{"id":"https://openalex.org/W4417100572","doi":"https://doi.org/10.48550/arxiv.2510.10573","title":"Deep semi-supervised approach based on consistency regularization and similarity learning for weeds classification","display_name":"Deep semi-supervised approach based on consistency regularization and similarity learning for weeds classification","publication_year":2025,"publication_date":"2025-10-12","ids":{"openalex":"https://openalex.org/W4417100572","doi":"https://doi.org/10.48550/arxiv.2510.10573"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2510.10573","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.10573","pdf_url":"https://arxiv.org/pdf/2510.10573","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2510.10573","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120697799","display_name":"Farouq Benchallal","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Benchallal, Farouq","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061197809","display_name":"Adel Hafiane","orcid":"https://orcid.org/0000-0003-3185-9996"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hafiane, Adel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078554238","display_name":"Nicolas Ragot","orcid":"https://orcid.org/0000-0003-2321-942X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ragot, Nicolas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5068195100","display_name":"Rapha\u00ebl Canals","orcid":"https://orcid.org/0000-0001-9100-7539"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Canals, Raphael","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":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"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":0.9889000058174133,"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":0.9889000058174133,"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":0.002199999988079071,"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":0.0013000000035390258,"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/deep-learning","display_name":"Deep learning","score":0.6625000238418579},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5771999955177307},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.5734999775886536},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5246000289916992},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.4478999972343445},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.40139999985694885},{"id":"https://openalex.org/keywords/scarcity","display_name":"Scarcity","score":0.3409000039100647},{"id":"https://openalex.org/keywords/precision-agriculture","display_name":"Precision agriculture","score":0.32690000534057617}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6730999946594238},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6625000238418579},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6157000064849854},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5871999859809875},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5771999955177307},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5734999775886536},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5246000289916992},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.4478999972343445},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.40139999985694885},{"id":"https://openalex.org/C109747225","wikidata":"https://www.wikidata.org/wiki/Q815758","display_name":"Scarcity","level":2,"score":0.3409000039100647},{"id":"https://openalex.org/C120217122","wikidata":"https://www.wikidata.org/wiki/Q740083","display_name":"Precision agriculture","level":3,"score":0.32690000534057617},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.31310001015663147},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.31150001287460327},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3089999854564667},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.30709999799728394},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.29919999837875366},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.29440000653266907},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.29440000653266907},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.29339998960494995},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.2759999930858612},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.2752000093460083},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2587999999523163}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:arXiv.org:2510.10573","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.10573","pdf_url":"https://arxiv.org/pdf/2510.10573","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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-05403316v1","is_oa":false,"landing_page_url":"https://hal.science/hal-05403316","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":"2025","raw_type":"info:eu-repo/semantics/preprint"},{"id":"doi:10.48550/arxiv.2510.10573","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.10573","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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:oai:arXiv.org:2510.10573","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.10573","pdf_url":"https://arxiv.org/pdf/2510.10573","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Weed":[0],"species":[1],"classification":[2,134],"represents":[3,96],"an":[4,190],"important":[5],"step":[6],"for":[7,92,103,189],"the":[8,16,49,53,62,104,108,111,121,126,158,167],"development":[9],"of":[10,18,34,57,106,110,116,120,171,193],"automated":[11],"targeting":[12],"systems":[13],"that":[14,142],"allow":[15],"adoption":[17],"precision":[19],"agriculture":[20],"practices.":[21],"To":[22],"reduce":[23],"costs":[24],"and":[25,48,89,129,132,161,169],"yield":[26],"losses":[27],"caused":[28],"by":[29],"their":[30,42,58],"presence.":[31],"The":[32],"identification":[33],"weeds":[35],"is":[36],"a":[37,97,138],"challenging":[38],"problem":[39],"due":[40],"to":[41,52,66,72,176],"shared":[43],"similarities":[44],"with":[45,61,146],"crop":[46],"plants":[47],"variability":[50],"related":[51],"differences":[54],"in":[55,64,68,99,118,163,174],"terms":[56,119],"types.":[59],"Along":[60],"variations":[63],"relation":[65],"changes":[67],"field":[69],"conditions.":[70],"Moreover,":[71],"fully":[73,80,178],"benefit":[74],"from":[75],"deep":[76,139,152,180],"learning-based":[77],"methods,":[78],"large":[79],"annotated":[81],"datasets":[82],"are":[83],"needed.":[84],"This":[85],"requires":[86],"time":[87],"intensive":[88],"laborious":[90],"process":[91],"data":[93,123],"labeling,":[94],"which":[95],"limitation":[98],"agricultural":[100],"applications.":[101],"Hence,":[102],"aim":[105],"improving":[107],"utilization":[109],"unlabeled":[112],"data,":[113],"regarding":[114],"conditions":[115,165],"scarcity":[117],"labeled":[122],"available":[124],"during":[125],"learning":[127,181,197],"phase":[128],"provide":[130],"robust":[131],"high":[133],"performance.":[135],"We":[136],"propose":[137],"semi-supervised":[140],"approach,":[141],"combines":[143],"consistency":[144],"regularization":[145],"similarity":[147],"learning.":[148],"Through":[149],"our":[150,172,194],"developed":[151],"auto-encoder":[153],"architecture,":[154],"experiments":[155],"realized":[156],"on":[157],"DeepWeeds":[159],"dataset":[160],"inference":[162],"noisy":[164],"demonstrated":[166],"effectiveness":[168],"robustness":[170],"method":[173],"comparison":[175],"state-of-the-art":[177],"supervised":[179],"models.":[182],"Furthermore,":[183],"we":[184],"carried":[185],"out":[186],"ablation":[187],"studies":[188],"extended":[191],"analysis":[192],"proposed":[195],"joint":[196],"strategy.":[198]},"counts_by_year":[],"updated_date":"2026-07-03T08:13:44.112507","created_date":"2025-10-15T00:00:00"}
