{"id":"https://openalex.org/W7147966330","doi":"https://doi.org/10.1109/cnml68938.2026.11453125","title":"ERS-KL: Entropy\u2013KL Ratio Scoring for OOD Detection in Plant Disease Recognition","display_name":"ERS-KL: Entropy\u2013KL Ratio Scoring for OOD Detection in Plant Disease Recognition","publication_year":2026,"publication_date":"2026-01-30","ids":{"openalex":"https://openalex.org/W7147966330","doi":"https://doi.org/10.1109/cnml68938.2026.11453125"},"language":null,"primary_location":{"id":"doi:10.1109/cnml68938.2026.11453125","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cnml68938.2026.11453125","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Communication Networks and Machine Learning (CNML)","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/A5126215831","display_name":"Peng Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I902764099","display_name":"SEGi University","ror":"https://ror.org/05crr5s63","country_code":"MY","type":"education","lineage":["https://openalex.org/I902764099"]}],"countries":["MY"],"is_corresponding":true,"raw_author_name":"Peng Wang","raw_affiliation_strings":["SEGi University,Built Environment & Information Technology,Petaling Jaya,Selangor,Malaysia"],"affiliations":[{"raw_affiliation_string":"SEGi University,Built Environment & Information Technology,Petaling Jaya,Selangor,Malaysia","institution_ids":["https://openalex.org/I902764099"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5122892882","display_name":"Siti Norbaya Daud","orcid":null},"institutions":[{"id":"https://openalex.org/I902764099","display_name":"SEGi University","ror":"https://ror.org/05crr5s63","country_code":"MY","type":"education","lineage":["https://openalex.org/I902764099"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Siti Norbaya Daud","raw_affiliation_strings":["SEGi University,Built Environment & Information Technology,Petaling Jaya,Selangor,Malaysia"],"affiliations":[{"raw_affiliation_string":"SEGi University,Built Environment & Information Technology,Petaling Jaya,Selangor,Malaysia","institution_ids":["https://openalex.org/I902764099"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5126215831"],"corresponding_institution_ids":["https://openalex.org/I902764099"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.9490898,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"709","last_page":"712"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9904999732971191,"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":0.9904999732971191,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.0012000000569969416,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.0007999999797903001,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/softmax-function","display_name":"Softmax function","score":0.8848999738693237},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5995000004768372},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.545199990272522},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.45329999923706055},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.45159998536109924},{"id":"https://openalex.org/keywords/plant-disease","display_name":"Plant disease","score":0.43959999084472656},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4120999872684479},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4106000065803528},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4097000062465668}],"concepts":[{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.8848999738693237},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6389999985694885},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5995000004768372},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5698000192642212},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.545199990272522},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5408999919891357},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.45329999923706055},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.45159998536109924},{"id":"https://openalex.org/C3019235130","wikidata":"https://www.wikidata.org/wiki/Q188956","display_name":"Plant disease","level":2,"score":0.43959999084472656},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4120999872684479},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4106000065803528},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4097000062465668},{"id":"https://openalex.org/C171752962","wikidata":"https://www.wikidata.org/wiki/Q255166","display_name":"Kullback\u2013Leibler divergence","level":2,"score":0.40299999713897705},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.37929999828338623},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.35510000586509705},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.33820000290870667},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.3280999958515167},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.3264000117778778},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.3203999996185303},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.3111000061035156},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.29260000586509705},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2913999855518341},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.29120001196861267},{"id":"https://openalex.org/C153402090","wikidata":"https://www.wikidata.org/wiki/Q6030480","display_name":"Infomax","level":4,"score":0.2612000107765198},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2603999972343445},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.25110000371932983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cnml68938.2026.11453125","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cnml68938.2026.11453125","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Communication Networks and Machine Learning (CNML)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W4382998689","https://openalex.org/W4386695594","https://openalex.org/W4404720262","https://openalex.org/W4405365703","https://openalex.org/W4408891187","https://openalex.org/W4415797229","https://openalex.org/W7129673176","https://openalex.org/W7133197973"],"related_works":[],"abstract_inverted_index":{"Out-of-distribution":[0],"(OOD)":[1],"detection":[2,151],"is":[3],"essential":[4],"for":[5],"deploying":[6],"deep":[7],"neural":[8],"networks":[9],"in":[10],"real-world":[11],"plant":[12,124],"disease":[13,125,138],"recognition":[14,126],"systems,":[15],"where":[16],"test":[17],"samples":[18],"may":[19,44],"deviate":[20],"significantly":[21],"from":[22,87],"curated":[23],"training":[24,114,165],"data.":[25],"Existing":[26],"post-hoc":[27,58],"scoring":[28,60],"methods,":[29],"such":[30],"as":[31,131,140],"maximum":[32],"softmax":[33],"probability":[34],"and":[35,43,106,134,163],"energy-based":[36],"scores,":[37],"primarily":[38],"rely":[39],"on":[40,99],"confidence":[41],"magnitude":[42],"conflate":[45],"different":[46,93],"sources":[47],"of":[48],"predictive":[49,68,101],"uncertainty.":[50],"In":[51],"this":[52],"work,":[53],"we":[54],"propose":[55],"a":[56,122],"simple":[57],"OOD":[59,150],"function,":[61],"termed":[62],"Entropy\u2013KL":[63],"Ratio":[64],"(ERS-KL),":[65],"which":[66],"combines":[67],"entropy":[69],"with":[70],"the":[71,75,100,113,118],"Kullback\u2013Leibler":[72],"divergence":[73],"to":[74,112],"uniform":[76],"distribution.":[77],"The":[78],"proposed":[79,119],"ratio":[80],"explicitly":[81],"penalizes":[82],"high-entropy":[83],"predictions":[84],"that":[85,146],"arise":[86],"near-uniform":[88],"posteriors,":[89],"thereby":[90],"distinguishing":[91],"structurally":[92],"uncertainty":[94],"patterns.":[95],"ERS-KL":[96,147],"operates":[97],"solely":[98],"distribution":[102],"at":[103],"inference":[104],"time":[105],"does":[107],"not":[108],"require":[109],"any":[110],"modification":[111],"objective.":[115],"We":[116],"evaluate":[117],"method":[120],"under":[121,154],"realistic":[123],"setting":[127],"using":[128],"PlantVillage-style":[129],"datasets":[130],"in-distribution":[132],"data":[133],"major":[135],"crops":[136],"leaf":[137],"images":[139],"out-of-distribution":[141],"samples.":[142],"Experimental":[143],"results":[144],"show":[145],"consistently":[148],"improves":[149],"performance,":[152],"particularly":[153],"low":[155],"false-positive-rate":[156],"operating":[157],"regimes,":[158],"across":[159],"multiple":[160],"model":[161],"architectures":[162],"common":[164],"strategies.":[166]},"counts_by_year":[],"updated_date":"2026-04-02T13:53:19.096889","created_date":"2026-04-02T00:00:00"}