{"id":"https://openalex.org/W7124435271","doi":"https://doi.org/10.1109/icicos68590.2025.11330012","title":"Multi Channel Fusion Framework for Tomato Leaf Disease Classification Using Modified Cnn Backbones and Preprocessing Aware Input Design","display_name":"Multi Channel Fusion Framework for Tomato Leaf Disease Classification Using Modified Cnn Backbones and Preprocessing Aware Input Design","publication_year":2025,"publication_date":"2025-10-29","ids":{"openalex":"https://openalex.org/W7124435271","doi":"https://doi.org/10.1109/icicos68590.2025.11330012"},"language":"en","primary_location":{"id":"doi:10.1109/icicos68590.2025.11330012","doi":"https://doi.org/10.1109/icicos68590.2025.11330012","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icicos68590.2025.11330012","pdf_url":null,"source":{"id":"https://openalex.org/S7407086313","display_name":"International Conference on Informatics and Computational Sciences (ICICoS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 8th International Conference on Informatics and Computational Sciences (ICICoS)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5093335924","display_name":"Rudi Limantara","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210113684","display_name":"Universitas Ciputra","ror":"https://ror.org/01zj4g759","country_code":"ID","type":"education","lineage":["https://openalex.org/I4210113684"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Rudi Limantara","raw_affiliation_strings":["School of Information Technology Universitas Ciputra,Surabaya,Indonesia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Technology Universitas Ciputra,Surabaya,Indonesia","institution_ids":["https://openalex.org/I4210113684"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060919978","display_name":"Elizabeth Nathania Witanto","orcid":"https://orcid.org/0000-0003-1085-4272","observed_orcids":["https://orcid.org/0000-0003-1085-4272"]},"institutions":[{"id":"https://openalex.org/I4210113684","display_name":"Universitas Ciputra","ror":"https://ror.org/01zj4g759","country_code":"ID","type":"education","lineage":["https://openalex.org/I4210113684"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Elizabeth Nathania Witanto","raw_affiliation_strings":["School of Information Technology Universitas Ciputra,Surabaya,Indonesia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Technology Universitas Ciputra,Surabaya,Indonesia","institution_ids":["https://openalex.org/I4210113684"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088614683","display_name":"Christopher Andreas","orcid":"https://orcid.org/0000-0001-5418-0412","observed_orcids":["https://orcid.org/0000-0001-5418-0412"]},"institutions":[{"id":"https://openalex.org/I4210113684","display_name":"Universitas Ciputra","ror":"https://ror.org/01zj4g759","country_code":"ID","type":"education","lineage":["https://openalex.org/I4210113684"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Christopher Andreas","raw_affiliation_strings":["School of Information Technology Universitas Ciputra,Surabaya,Indonesia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Technology Universitas Ciputra,Surabaya,Indonesia","institution_ids":["https://openalex.org/I4210113684"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123205500","display_name":"Satria Adi Nugraha","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210113684","display_name":"Universitas Ciputra","ror":"https://ror.org/01zj4g759","country_code":"ID","type":"education","lineage":["https://openalex.org/I4210113684"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Satria Adi Nugraha","raw_affiliation_strings":["School of Information Technology Universitas Ciputra,Surabaya,Indonesia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Technology Universitas Ciputra,Surabaya,Indonesia","institution_ids":["https://openalex.org/I4210113684"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047027799","display_name":"Adi Suryaputra Paramita","orcid":"https://orcid.org/0000-0002-9709-2655","observed_orcids":["https://orcid.org/0000-0002-9709-2655"]},"institutions":[{"id":"https://openalex.org/I4210113684","display_name":"Universitas Ciputra","ror":"https://ror.org/01zj4g759","country_code":"ID","type":"education","lineage":["https://openalex.org/I4210113684"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Adi Suryaputra Paramita","raw_affiliation_strings":["School of Information Technology Universitas Ciputra,Surabaya,Indonesia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Technology Universitas Ciputra,Surabaya,Indonesia","institution_ids":["https://openalex.org/I4210113684"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088614683","display_name":"Christopher Andreas","orcid":"https://orcid.org/0000-0001-5418-0412","observed_orcids":["https://orcid.org/0000-0001-5418-0412"]},"institutions":[{"id":"https://openalex.org/I4210113684","display_name":"Universitas Ciputra","ror":"https://ror.org/01zj4g759","country_code":"ID","type":"education","lineage":["https://openalex.org/I4210113684"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Christopher Andreas","raw_affiliation_strings":["School of Information Technology Universitas Ciputra,Surabaya,Indonesia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Technology Universitas Ciputra,Surabaya,Indonesia","institution_ids":["https://openalex.org/I4210113684"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210113684"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"98","last_page":"102"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9990000128746033,"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":0.9990000128746033,"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":0.000699999975040555,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.0,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/tomato-leaf-disease-classification","display_name":"tomato leaf disease classification","score":0.9929999709129333},{"id":"https://openalex.org/keywords/leaf-segmentation","display_name":"leaf segmentation","score":0.8040000200271606},{"id":"https://openalex.org/keywords/u-net","display_name":"U-Net","score":0.7749999761581421},{"id":"https://openalex.org/keywords/clahe-contrast-enhancement","display_name":"CLAHE contrast enhancement","score":0.953000009059906},{"id":"https://openalex.org/keywords/efficientnet-b0","display_name":"EfficientNetB0","score":0.8119999766349792},{"id":"https://openalex.org/keywords/resnet-50","display_name":"ResNet50","score":0.8640000224113464},{"id":"https://openalex.org/keywords/mobilenetv2","display_name":"MobileNetV2","score":1.0},{"id":"https://openalex.org/keywords/data-augmentation","display_name":"data augmentation","score":0.925000011920929},{"id":"https://openalex.org/keywords/input-design","display_name":"input design","score":0.3779999911785126}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7297999858856201},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7257000207901001},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.6498000025749207},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6074000000953674},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.49410000443458557},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.46790000796318054},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.4487999975681305},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.444599986076355},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4410000145435333},{"id":"https://openalex.org/C136943445","wikidata":"https://www.wikidata.org/wiki/Q1970240","display_name":"Histogram equalization","level":4,"score":0.4278999865055084},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4174000024795532},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.41370001435279846},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.39010000228881836},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3865000009536743},{"id":"https://openalex.org/C30387639","wikidata":"https://www.wikidata.org/wiki/Q4680744","display_name":"Adaptive histogram equalization","level":5,"score":0.3732999861240387},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.3725999891757965},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3682999908924103},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.35109999775886536},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.3422999978065491},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3418999910354614},{"id":"https://openalex.org/C22029948","wikidata":"https://www.wikidata.org/wiki/Q45089","display_name":"Dice","level":2,"score":0.3312000036239624},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32839998602867126},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.32280001044273376},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.3174999952316284},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.31130000948905945},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.29089999198913574},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.28610000014305115},{"id":"https://openalex.org/C5339829","wikidata":"https://www.wikidata.org/wiki/Q1425977","display_name":"Machine vision","level":2,"score":0.2565000057220459}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icicos68590.2025.11330012","doi":"https://doi.org/10.1109/icicos68590.2025.11330012","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icicos68590.2025.11330012","pdf_url":null,"source":{"id":"https://openalex.org/S7407086313","display_name":"International Conference on Informatics and Computational Sciences (ICICoS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 8th International Conference on Informatics and Computational Sciences (ICICoS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.991100013256073,"display_name":"Zero hunger","id":"https://metadata.un.org/sdg/2"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W2614850301","https://openalex.org/W2758893285","https://openalex.org/W2997663236","https://openalex.org/W3111451782","https://openalex.org/W3127894531","https://openalex.org/W3172372023","https://openalex.org/W3217226567","https://openalex.org/W4293009562","https://openalex.org/W4308391678","https://openalex.org/W4310193739","https://openalex.org/W4361213023","https://openalex.org/W4362613151","https://openalex.org/W4368275735","https://openalex.org/W4386802652","https://openalex.org/W4391952750","https://openalex.org/W4392188537","https://openalex.org/W4396689118","https://openalex.org/W4401718269","https://openalex.org/W4403248893","https://openalex.org/W4404061135","https://openalex.org/W4407318896","https://openalex.org/W4408017924","https://openalex.org/W4410296435"],"related_works":["https://openalex.org/W4411714681","https://openalex.org/W7202032069","https://openalex.org/W7218125149","https://openalex.org/W4413179415","https://openalex.org/W6893228759","https://openalex.org/W7154599718","https://openalex.org/W4324385677","https://openalex.org/W4415465507","https://openalex.org/W7160879190","https://openalex.org/W7147184419"],"abstract_inverted_index":{"This":[0],"research":[1],"develops":[2],"a":[3,36,187],"deep":[4],"learning":[5],"system":[6,148],"for":[7,190],"identifying":[8],"tomato":[9],"leaf":[10,43,125,160],"diseases":[11],"by":[12],"combining":[13],"multiple":[14],"image":[15,58],"processing":[16],"techniques":[17],"with":[18,83,114,129,141],"advanced":[19],"neural":[20,111],"networks.":[21],"The":[22,77,124,146,168],"approach":[23,104],"takes":[24],"input":[25,103],"images":[26],"and":[27,45,63,75,86,131,159,179,208,217],"creates":[28],"three":[29,57],"different":[30,154],"versions:":[31],"the":[32,42,110,117,166,219],"original":[33],"RGB":[34],"photo,":[35],"segmentation":[37,126],"mask":[38],"that":[39,99,162],"isolates":[40],"just":[41],"area,":[44],"contrast-enhanced":[46],"version":[47],"made":[48],"using":[49,100,133],"Contrast":[50],"Limited":[51],"Adaptive":[52],"Histogram":[53],"Equalization":[54],"(CLAHE).":[55],"These":[56],"types":[59],"get":[60],"merged":[61],"together":[62,174],"fed":[64],"into":[65],"modified":[66],"versions":[67],"of":[68,122],"established":[69],"CNN":[70],"models":[71,78],"-":[72],"ResNet50,":[73],"EfficientNetB0,":[74],"MobileNetV2.":[76],"also":[79],"use":[80],"data":[81],"augmentation":[82],"various":[84],"geometric":[85],"lighting":[87,157],"changes":[88],"to":[89],"help":[90],"them":[91],"work":[92,198],"better":[93],"on":[94,201,205],"new":[95],"images.":[96],"Results":[97],"show":[98],"this":[101,203],"combined":[102],"boosts":[105],"classification":[106,184],"performance":[107],"across":[108],"all":[109],"network":[112],"models,":[113],"EfficientNetB0":[115],"reaching":[116],"best":[118],"F":[119],"1":[120],"-score":[121],"0.999.":[123],"part,":[127],"done":[128],"U-Net":[130,132],"FP32":[134],"precision,":[135],"accurately":[136],"separates":[137],"leaves":[138],"from":[139],"backgrounds":[140],"Dice":[142],"scores":[143],"over":[144],"0.98.":[145],"complete":[147],"handles":[149],"real-world":[150],"challenges":[151],"well,":[152],"including":[153],"surface":[155],"textures,":[156],"conditions,":[158],"orientations":[161],"farmers":[163,214],"encounter":[164],"in":[165,182,194],"field.":[167],"main":[169],"contribution":[170],"here":[171],"is":[172],"bringing":[173],"semantic":[175],"understanding,":[176],"structural":[177],"information,":[178],"visual":[180],"details":[181],"one":[183],"system,":[185],"creating":[186],"practical":[188],"tool":[189],"automated":[191],"disease":[192],"detection":[193],"modern":[195],"farming.":[196],"Future":[197],"could":[199],"focus":[200],"making":[202],"run":[204],"mobile":[206],"devices":[207],"adding":[209],"AI":[210],"explanation":[211],"features":[212],"so":[213],"can":[215],"understand":[216],"trust":[218],"system's":[220],"decisions":[221],"better.":[222]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-01-17T00:00:00"}