{"id":"https://openalex.org/W7141453335","doi":"https://doi.org/10.48550/arxiv.2603.25006","title":"Improving Fine-Grained Rice Leaf Disease Detection via Angular-Compactness Dual Loss Learning","display_name":"Improving Fine-Grained Rice Leaf Disease Detection via Angular-Compactness Dual Loss Learning","publication_year":2026,"publication_date":"2026-03-26","ids":{"openalex":"https://openalex.org/W7141453335","doi":"https://doi.org/10.48550/arxiv.2603.25006"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.25006","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25006","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.25006","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5119761654","display_name":"Md. Rokon Mia","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Mia, Md. Rokon","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130800318","display_name":"Rakib Hossain Sajib","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sajib, Rakib Hossain","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037710871","display_name":"Abdullah Al Noman","orcid":"https://orcid.org/0000-0002-5799-1293"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Noman, Abdullah Al","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008174805","display_name":"Abir Ahmed","orcid":"https://orcid.org/0000-0003-0005-4976"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ahmed, Abir","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130782315","display_name":"B M Taslimul Haque","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Haque, B M Taslimul","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5119761654"],"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":0.9761999845504761,"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":0.9761999845504761,"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":0.0027000000700354576,"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/T12660","display_name":"Plant Disease Management Techniques","score":0.001500000013038516,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6470999717712402},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5139999985694885},{"id":"https://openalex.org/keywords/staple-food","display_name":"Staple food","score":0.46309998631477356},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.42399999499320984},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4032000005245209},{"id":"https://openalex.org/keywords/crop","display_name":"Crop","score":0.39410001039505005},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38519999384880066},{"id":"https://openalex.org/keywords/statistical-learning","display_name":"Statistical learning","score":0.3368000090122223}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6470999717712402},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5139999985694885},{"id":"https://openalex.org/C2779249804","wikidata":"https://www.wikidata.org/wiki/Q736427","display_name":"Staple food","level":3,"score":0.46309998631477356},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4528000056743622},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42590001225471497},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.42399999499320984},{"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/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4032000005245209},{"id":"https://openalex.org/C137580998","wikidata":"https://www.wikidata.org/wiki/Q235352","display_name":"Crop","level":2,"score":0.39410001039505005},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38519999384880066},{"id":"https://openalex.org/C2982736386","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Statistical learning","level":2,"score":0.3368000090122223},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.33390000462532043},{"id":"https://openalex.org/C126343540","wikidata":"https://www.wikidata.org/wiki/Q889514","display_name":"Crop yield","level":2,"score":0.3337000012397766},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.32339999079704285},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.30799999833106995},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.3052999973297119},{"id":"https://openalex.org/C88463610","wikidata":"https://www.wikidata.org/wiki/Q194118","display_name":"Agricultural engineering","level":1,"score":0.30149999260902405},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.29789999127388},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.2953000068664551},{"id":"https://openalex.org/C2992726227","wikidata":"https://www.wikidata.org/wiki/Q5090","display_name":"Rice plant","level":2,"score":0.2937999963760376},{"id":"https://openalex.org/C118518473","wikidata":"https://www.wikidata.org/wiki/Q11451","display_name":"Agriculture","level":2,"score":0.29190000891685486},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.27399998903274536},{"id":"https://openalex.org/C2944601119","wikidata":"https://www.wikidata.org/wiki/Q43744058","display_name":"Residual neural network","level":3,"score":0.26510000228881836},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.2587999999523163}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.25006","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25006","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.25006","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25006","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"score":0.6799807548522949,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Early":[0],"detection":[1],"of":[2,18,24,36,86,118,138],"rice":[3,9,87],"leaf":[4,88],"diseases":[5,26],"is":[6,10,92],"critical,":[7],"as":[8],"a":[11,15,72],"staple":[12],"crop":[13,38],"supporting":[14],"substantial":[16],"share":[17],"the":[19,34,105,135,143],"world's":[20],"population.":[21],"Timely":[22],"identification":[23],"these":[25],"enables":[27],"more":[28],"effective":[29],"intervention":[30],"and":[31,58,79,101,121,130,154],"significantly":[32],"reduces":[33],"risk":[35],"large-scale":[37],"losses.":[39],"However,":[40],"traditional":[41],"deep":[42],"learning":[43],"models":[44],"primarily":[45],"rely":[46],"on":[47,104],"cross":[48],"entropy":[49],"loss,":[50],"which":[51],"often":[52],"struggles":[53],"with":[54,116],"high":[55],"intra-class":[56],"variance":[57],"inter-class":[59],"similarity,":[60],"common":[61],"challenges":[62],"in":[63,159],"plant":[64],"pathology":[65],"datasets.":[66],"To":[67],"tackle":[68],"this,":[69],"we":[70],"propose":[71],"dual-loss":[73],"framework":[74,144],"that":[75,127],"combines":[76],"Center":[77],"Loss":[78,81],"ArcFace":[80],"to":[82],"enhance":[83],"fine-grained":[84],"classification":[85],"diseases.":[89],"The":[90,124],"method":[91],"applied":[93],"into":[94],"three":[95],"state-of-the-art":[96],"backbone":[97],"architectures:":[98],"InceptionNetV3,":[99],"DenseNet201,":[100],"EfficientNetB0":[102],"trained":[103],"public":[106],"Rice":[107],"Leaf":[108],"Dataset.":[109],"Our":[110],"approach":[111],"achieves":[112],"significant":[113],"performance":[114],"gains,":[115],"accuracies":[117],"99.6%,":[119],"99.2%":[120,122],"respectively.":[123],"results":[125],"demonstrate":[126],"angular":[128],"margin-based":[129],"center-based":[131],"constraints":[132],"substantially":[133],"boost":[134],"discriminative":[136],"strength":[137],"feature":[139],"embeddings.":[140],"In":[141],"particular,":[142],"does":[145],"not":[146],"require":[147],"major":[148],"architectural":[149],"modifications,":[150],"making":[151],"it":[152],"efficient":[153],"practical":[155],"for":[156],"real-world":[157],"deployment":[158],"farming":[160],"environments.":[161]},"counts_by_year":[],"updated_date":"2026-03-28T06:16:51.555046","created_date":"2026-03-28T00:00:00"}
