{"id":"https://openalex.org/W4415431580","doi":"https://doi.org/10.1007/s44163-025-00548-7","title":"A statistically validated stacking ensemble of CNNs and vision transformer for robust maize disease classification","display_name":"A statistically validated stacking ensemble of CNNs and vision transformer for robust maize disease classification","publication_year":2025,"publication_date":"2025-10-22","ids":{"openalex":"https://openalex.org/W4415431580","doi":"https://doi.org/10.1007/s44163-025-00548-7"},"language":"en","primary_location":{"id":"doi:10.1007/s44163-025-00548-7","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44163-025-00548-7","pdf_url":"https://link.springer.com/content/pdf/10.1007/s44163-025-00548-7.pdf","source":{"id":"https://openalex.org/S4210220416","display_name":"Discover Artificial Intelligence","issn_l":"2731-0809","issn":["2731-0809"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://link.springer.com/content/pdf/10.1007/s44163-025-00548-7.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5044235376","display_name":"Dawit Teklu Weldeslasie","orcid":"https://orcid.org/0009-0000-5388-5197"},"institutions":[{"id":"https://openalex.org/I78545622","display_name":"Aksum University","ror":"https://ror.org/003659f07","country_code":"ET","type":"education","lineage":["https://openalex.org/I78545622"]}],"countries":["ET"],"is_corresponding":true,"raw_author_name":"Dawit Teklu Weldeslasie","raw_affiliation_strings":["Department of Information Technology, Aksum University, Aksum, Tigray, Ethiopia"],"affiliations":[{"raw_affiliation_string":"Department of Information Technology, Aksum University, Aksum, Tigray, Ethiopia","institution_ids":["https://openalex.org/I78545622"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119734724","display_name":"Mebrahatom Yihdego Mekonen","orcid":null},"institutions":[{"id":"https://openalex.org/I78545622","display_name":"Aksum University","ror":"https://ror.org/003659f07","country_code":"ET","type":"education","lineage":["https://openalex.org/I78545622"]}],"countries":["ET"],"is_corresponding":false,"raw_author_name":"Mebrahatom Yihdego Mekonen","raw_affiliation_strings":["Department of Information Systems, Aksum University, Aksum, Tigray, Ethiopia"],"affiliations":[{"raw_affiliation_string":"Department of Information Systems, Aksum University, Aksum, Tigray, Ethiopia","institution_ids":["https://openalex.org/I78545622"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119734725","display_name":"Abrham Mamu Abebe","orcid":null},"institutions":[{"id":"https://openalex.org/I78545622","display_name":"Aksum University","ror":"https://ror.org/003659f07","country_code":"ET","type":"education","lineage":["https://openalex.org/I78545622"]}],"countries":["ET"],"is_corresponding":false,"raw_author_name":"Abrham Mamu Abebe","raw_affiliation_strings":["Department of Information Technology, Aksum University, Aksum, Tigray, Ethiopia"],"affiliations":[{"raw_affiliation_string":"Department of Information Technology, Aksum University, Aksum, Tigray, Ethiopia","institution_ids":["https://openalex.org/I78545622"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5119734723","display_name":"Kbrom Tekulu Desta","orcid":null},"institutions":[{"id":"https://openalex.org/I78545622","display_name":"Aksum University","ror":"https://ror.org/003659f07","country_code":"ET","type":"education","lineage":["https://openalex.org/I78545622"]}],"countries":["ET"],"is_corresponding":false,"raw_author_name":"Kbrom Tekulu Desta","raw_affiliation_strings":["Department of Industrial Control Engineering, Aksum University, Aksum, Tigray, Ethiopia"],"affiliations":[{"raw_affiliation_string":"Department of Industrial Control Engineering, Aksum University, Aksum, Tigray, Ethiopia","institution_ids":["https://openalex.org/I78545622"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5044235376"],"corresponding_institution_ids":["https://openalex.org/I78545622"],"apc_list":{"value":990,"currency":"EUR","value_usd":1067},"apc_paid":{"value":990,"currency":"EUR","value_usd":1067},"fwci":3.4996,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.93115041,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"5","issue":"1","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":0.9994999766349792,"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":0.9994999766349792,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9916999936103821,"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"}},{"id":"https://openalex.org/T12486","display_name":"Food Supply Chain Traceability","score":0.9557999968528748,"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/overfitting","display_name":"Overfitting","score":0.8039000034332275},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6230999827384949},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.5386999845504761},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5253000259399414},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.4927000105381012},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.45210000872612},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4156000018119812},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.4092000126838684},{"id":"https://openalex.org/keywords/cross-validation","display_name":"Cross-validation","score":0.3905999958515167}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.8039000034332275},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7121999859809875},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7084000110626221},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6230999827384949},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6151000261306763},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.5386999845504761},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5253000259399414},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.4927000105381012},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.45210000872612},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4156000018119812},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.4092000126838684},{"id":"https://openalex.org/C27181475","wikidata":"https://www.wikidata.org/wiki/Q541014","display_name":"Cross-validation","level":2,"score":0.3905999958515167},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.37720000743865967},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.36640000343322754},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.33660000562667847},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.3278000056743622},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32249999046325684},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.3215999901294708},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.2809000015258789},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.271699994802475},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.2711000144481659},{"id":"https://openalex.org/C22354355","wikidata":"https://www.wikidata.org/wiki/Q422009","display_name":"Partial least squares regression","level":2,"score":0.27059999108314514},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.26899999380111694},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.2648000121116638},{"id":"https://openalex.org/C33347731","wikidata":"https://www.wikidata.org/wiki/Q285210","display_name":"Stacking","level":2,"score":0.26260000467300415},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.2619999945163727},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.2615000009536743}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s44163-025-00548-7","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44163-025-00548-7","pdf_url":"https://link.springer.com/content/pdf/10.1007/s44163-025-00548-7.pdf","source":{"id":"https://openalex.org/S4210220416","display_name":"Discover Artificial Intelligence","issn_l":"2731-0809","issn":["2731-0809"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:fe1182139f1842d2833d0f020bbd38c5","is_oa":true,"landing_page_url":"https://doaj.org/article/fe1182139f1842d2833d0f020bbd38c5","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Discover Artificial Intelligence, Vol 5, Iss 1, Pp 1-17 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1007/s44163-025-00548-7","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44163-025-00548-7","pdf_url":"https://link.springer.com/content/pdf/10.1007/s44163-025-00548-7.pdf","source":{"id":"https://openalex.org/S4210220416","display_name":"Discover Artificial Intelligence","issn_l":"2731-0809","issn":["2731-0809"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4415431580.pdf"},"referenced_works_count":25,"referenced_works":["https://openalex.org/W1925749693","https://openalex.org/W2136922540","https://openalex.org/W2473156356","https://openalex.org/W2807970386","https://openalex.org/W3086198045","https://openalex.org/W3189528750","https://openalex.org/W4210282496","https://openalex.org/W4246562710","https://openalex.org/W4318570596","https://openalex.org/W4367728011","https://openalex.org/W4376651842","https://openalex.org/W4376865313","https://openalex.org/W4377704419","https://openalex.org/W4379743944","https://openalex.org/W4384281913","https://openalex.org/W4384340819","https://openalex.org/W4387499956","https://openalex.org/W4393442952","https://openalex.org/W4395465270","https://openalex.org/W4399525678","https://openalex.org/W4405074397","https://openalex.org/W4409409612","https://openalex.org/W4411300655","https://openalex.org/W4413061894","https://openalex.org/W4413319779"],"related_works":[],"abstract_inverted_index":{"Early":[0],"disease":[1,29,99,187],"detection":[2],"in":[3,9,26,90,115],"crops":[4],"remains":[5],"an":[6,166,213],"age-old":[7],"problem":[8],"the":[10,38,150,197,202,218],"quest":[11],"for":[12,124,184],"global":[13,87],"food":[14],"security.":[15],"While":[16,217],"deep":[17],"learning":[18],"has":[19],"transformed":[20],"image-based":[21],"diagnosis,":[22],"most":[23,190],"existing":[24],"models":[25,173],"maize":[27,185],"leaf":[28,186],"follow":[30],"single-view":[31],"approaches,":[32],"offer":[33],"limited":[34],"validation,":[35],"and":[36,71,85,117,212,238,249],"neglect":[37],"accuracy-computational":[39],"efficiency":[40],"trade-off.":[41],"In":[42],"this":[43,104,194,222],"regard,":[44],"we":[45],"suggest":[46],"a":[47,55,74,94,107,133,142,157,181,241],"robust":[48],"framework":[49],"to":[50,92,226,259],"combat":[51],"these":[52],"issues":[53],"with":[54,73,132,206],"heterogeneous":[56,203],"stacking":[57],"ensemble.":[58],"Our":[59],"suggested":[60],"model":[61,78,128],"combines":[62],"four":[63],"various":[64],"convolutional":[65],"neural":[66],"networks":[67],"DenseNet201,":[68],"InceptionV3,":[69],"NASNetMobile":[70],"VGG19":[72],"Vision":[75],"Transformer.":[76],"The":[77,127,160,178,189],"benefits":[79],"from":[80],"both":[81],"local":[82],"feature":[83],"extraction":[84],"more":[86,95,224],"contextual":[88],"analysis":[89,208],"trying":[91],"learn":[93],"holistic":[96],"representation":[97],"of":[98,110,137,193,201,209],"markers.":[100],"We":[101],"extensively":[102],"evaluated":[103],"ensemble":[105,161],"on":[106,165],"challenging":[108],"dataset":[109],"15,995":[111],"field":[112],"images":[113],"collected":[114],"Ethiopia":[116],"Kaggle":[118],"dataset,":[119],"utilizing":[120],"stratified":[121],"fivefold":[122],"cross-validation":[123],"stable":[125,130],"assessment.":[126],"exhibited":[129],"performance":[131],"mean":[134],"validation":[135,140],"accuracy":[136,164],"99.13%":[138],"across":[139],"folds":[141],"statistically":[143,198],"significant":[144],"improvement":[145],"p":[146],"<":[147],"0.05":[148],"over":[149],"best":[151],"individual":[152],"model,":[153],"as":[154,175,240],"assessed":[155],"by":[156,252],"paired":[158],"t-test.":[159],"recorded":[162],"99.15%":[163],"independent":[167],"test":[168],"set,":[169],"outperforming":[170],"state-of-the-art":[171],"lightweight":[172],"such":[174],"MobileNetV3":[176],"97.62%.":[177],"findings":[179],"set":[180],"new":[182],"baseline":[183],"classification.":[188],"important":[191],"contribution":[192],"work":[195],"is":[196,234],"validated":[199],"excellence":[200],"ensemble,":[204],"along":[205],"transparent":[207],"computational":[210,219],"requirements":[211],"extensive":[214],"overfitting":[215],"countermeasure.":[216],"demands":[220],"make":[221],"solution":[223],"amenable":[225],"server-side":[227],"rather":[228],"than":[229],"edge":[230],"device":[231],"implementation,":[232],"it":[233],"still":[235],"incredibly":[236],"reliable":[237],"generalizable":[239],"diagnostic":[242],"tool.":[243],"A":[244],"prototype":[245],"was":[246],"also":[247],"developed":[248],"warmly":[250],"received":[251],"users,":[253],"further":[254],"demonstrating":[255],"its":[256],"real-world":[257],"applicability":[258],"drive":[260],"data-driven":[261],"sustainable":[262],"agriculture.":[263]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-03-30T08:08:38.191290","created_date":"2025-10-24T00:00:00"}