{"id":"https://openalex.org/W7083627846","doi":"https://doi.org/10.32604/cmc.2025.069528","title":"A Hybrid Model of Transfer Learning and Convolutional Neural Networks for Accurate Coffee Leaf Miner (CLM) Classification","display_name":"A Hybrid Model of Transfer Learning and Convolutional Neural Networks for Accurate Coffee Leaf Miner (CLM) Classification","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W7083627846","doi":"https://doi.org/10.32604/cmc.2025.069528"},"language":"en","primary_location":{"id":"doi:10.32604/cmc.2025.069528","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.069528","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials &amp; Continua","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.32604/cmc.2025.069528","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Nameer Baht","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Nameer Baht","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Enrique Dom\u00ednguez","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Enrique Dom\u00ednguez","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.65999749,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"85","issue":"3","first_page":"4441","last_page":"4455"},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T11610","display_name":"Food Security and Health in Diverse Populations","score":0.6061000227928162,"subfield":{"id":"https://openalex.org/subfields/3600","display_name":"General Health Professions"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11610","display_name":"Food Security and Health in Diverse Populations","score":0.6061000227928162,"subfield":{"id":"https://openalex.org/subfields/3600","display_name":"General Health Professions"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10596","display_name":"Child Nutrition and Water Access","score":0.0625,"subfield":{"id":"https://openalex.org/subfields/2916","display_name":"Nutrition and Dietetics"},"field":{"id":"https://openalex.org/fields/29","display_name":"Nursing"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T14242","display_name":"Nutrition, Health, and Society Studies","score":0.019700000062584877,"subfield":{"id":"https://openalex.org/subfields/1106","display_name":"Food 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/transfer-of-learning","display_name":"Transfer of learning","score":0.7109000086784363},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6291000247001648},{"id":"https://openalex.org/keywords/confusion-matrix","display_name":"Confusion matrix","score":0.5841000080108643},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5396999716758728},{"id":"https://openalex.org/keywords/production","display_name":"Production (economics)","score":0.43560001254081726},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.3896999955177307},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3776000142097473}],"concepts":[{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.7109000086784363},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.704200029373169},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6478000283241272},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6291000247001648},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.619700014591217},{"id":"https://openalex.org/C138602881","wikidata":"https://www.wikidata.org/wiki/Q2709591","display_name":"Confusion matrix","level":2,"score":0.5841000080108643},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5396999716758728},{"id":"https://openalex.org/C2778348673","wikidata":"https://www.wikidata.org/wiki/Q739302","display_name":"Production (economics)","level":2,"score":0.43560001254081726},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.3896999955177307},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3776000142097473},{"id":"https://openalex.org/C128383755","wikidata":"https://www.wikidata.org/wiki/Q3816336","display_name":"Agricultural productivity","level":3,"score":0.31349998712539673},{"id":"https://openalex.org/C2989409935","wikidata":"https://www.wikidata.org/wiki/Q11451","display_name":"Crop production","level":3,"score":0.2937999963760376},{"id":"https://openalex.org/C3019235130","wikidata":"https://www.wikidata.org/wiki/Q188956","display_name":"Plant disease","level":2,"score":0.29350000619888306},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2874000072479248},{"id":"https://openalex.org/C2781140086","wikidata":"https://www.wikidata.org/wiki/Q557945","display_name":"Confusion","level":2,"score":0.28540000319480896},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.2727000117301941},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2630000114440918}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.32604/cmc.2025.069528","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.069528","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials &amp; Continua","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.32604/cmc.2025.069528","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.069528","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials &amp; Continua","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W2963820222","https://openalex.org/W2998829203","https://openalex.org/W3163544951","https://openalex.org/W3173551454","https://openalex.org/W4205920687","https://openalex.org/W4289594494","https://openalex.org/W4319440047","https://openalex.org/W4320016953","https://openalex.org/W4328115538","https://openalex.org/W4384701573","https://openalex.org/W4386378523","https://openalex.org/W4390414459","https://openalex.org/W4391956279","https://openalex.org/W4399782562","https://openalex.org/W4402269627","https://openalex.org/W4403210283","https://openalex.org/W4406167171","https://openalex.org/W4406676486","https://openalex.org/W4407029343","https://openalex.org/W4407773151","https://openalex.org/W4407779783","https://openalex.org/W4409208828","https://openalex.org/W4409641098"],"related_works":[],"abstract_inverted_index":{"Coffee":[0,42],"is":[1,9,15,25,60,152],"an":[2],"important":[3],"agricultural":[4,188],"commodity,":[5],"and":[6,86,116,125,158,177,181,207],"its":[7],"production":[8,37],"threatened":[10],"by":[11],"various":[12],"diseases.":[13],"It":[14],"also":[16],"a":[17],"source":[18],"of":[19,58,72],"concern":[20],"for":[21,32,52,62,89,122,165,173,184],"coffee-exporting":[22],"countries,":[23],"which":[24,139],"causing":[26],"them":[27],"to":[28],"rethink":[29],"their":[30],"strategies":[31],"the":[33,70,99,102,133,141,150],"future.":[34],"Maintaining":[35],"crop":[36],"requires":[38],"early":[39,174],"diagnosis.":[40],"Notably,":[41],"Leaf":[43],"Miner":[44],"(CLM)":[45],"Machine":[46],"learning":[47,80,210],"(ML)":[48],"offers":[49],"promising":[50],"tools":[51],"automated":[53],"disease":[54,175,179],"detection.":[55],"Early":[56],"detection":[57,176],"CLM":[59],"crucial":[61],"minimising":[63],"yield":[64],"losses.":[65],"However,":[66],"this":[67,146,167],"study":[68],"explores":[69],"effectiveness":[71],"using":[73,198],"Convolutional":[74],"Neural":[75],"Networks":[76],"(CNNs)":[77],"with":[78],"transfer":[79],"algorithms":[81],"ResNet50,":[82],"DenseNet121,":[83],"MobileNet,":[84],"Inception,":[85],"hybrid":[87,104],"VGG19":[88,105],"classifying":[90],"coffee":[91,171],"leaf":[92],"images":[93],"as":[94,192],"healthy":[95,157],"or":[96],"CLM-infected.":[97],"Leveraging":[98],"JMuBEN1":[100],"dataset,":[101,147],"proposed":[103],"model":[106,151],"achieved":[107],"exceptional":[108],"performance,":[109],"reaching":[110],"97%":[111],"accuracy":[112],"on":[113,145],"both":[114],"training":[115],"validation":[117],"data.":[118],"Additionally,":[119],"high":[120],"scores":[121],"precision,":[123],"recall,":[124],"F1-score.":[126],"The":[127],"confusion":[128],"matrix":[129],"shows":[130],"that":[131,149,202],"all":[132],"test":[134],"samples":[135],"were":[136],"correctly":[137],"classified,":[138],"indicates":[140],"model\u2019s":[142],"strong":[143,163],"performance":[144],"demonstrating":[148],"effective":[153],"in":[154,169,187,195],"distinguishing":[155],"between":[156],"CLM-infected":[159],"leaves.":[160],"This":[161],"suggests":[162],"potential":[164],"implementing":[166],"approach":[168],"real-world":[170],"plantations":[172],"improved":[178],"management,":[180],"adapting":[182],"it":[183],"practical":[185],"deployment":[186],"settings.":[189],"As":[190],"well":[191],"supporting":[193],"farmers":[194],"detecting":[196],"diseases":[197],"modern,":[199],"inexpensive":[200],"methods":[201],"do":[203],"not":[204],"require":[205],"specialists,":[206],"utilising":[208],"deep":[209],"technologies.":[211]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
