{"id":"https://openalex.org/W4386259179","doi":"https://doi.org/10.1109/icsse58758.2023.10227192","title":"Fusion of ViT Technique and Image Filtering in Deep Learning for Plant Pests and Diseases Recognition","display_name":"Fusion of ViT Technique and Image Filtering in Deep Learning for Plant Pests and Diseases Recognition","publication_year":2023,"publication_date":"2023-07-27","ids":{"openalex":"https://openalex.org/W4386259179","doi":"https://doi.org/10.1109/icsse58758.2023.10227192"},"language":"en","primary_location":{"id":"doi:10.1109/icsse58758.2023.10227192","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icsse58758.2023.10227192","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Conference on System Science and Engineering (ICSSE)","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/A5011476665","display_name":"Van-Dung Hoang","orcid":"https://orcid.org/0000-0001-7554-1707"},"institutions":[{"id":"https://openalex.org/I4210148201","display_name":"Ho Chi Minh City University of Technology and Education","ror":"https://ror.org/05hzn5427","country_code":"VN","type":"education","lineage":["https://openalex.org/I4210148201"]}],"countries":["VN"],"is_corresponding":true,"raw_author_name":"Van-Dung Hoang","raw_affiliation_strings":["HCMC University of Technology and Education,Faculty of Information Technology,Ho Chi Minh City,Vietnam","Faculty of Information Technology, HCMC University of Technology and Education, Ho Chi Minh City, Vietnam"],"affiliations":[{"raw_affiliation_string":"HCMC University of Technology and Education,Faculty of Information Technology,Ho Chi Minh City,Vietnam","institution_ids":["https://openalex.org/I4210148201"]},{"raw_affiliation_string":"Faculty of Information Technology, HCMC University of Technology and Education, Ho Chi Minh City, Vietnam","institution_ids":["https://openalex.org/I4210148201"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057509369","display_name":"Thanh-an Pham","orcid":"https://orcid.org/0000-0001-6231-2569"},"institutions":[{"id":"https://openalex.org/I3129629310","display_name":"Ho Chi Minh University of Banking","ror":"https://ror.org/03fsckq75","country_code":"VN","type":"education","lineage":["https://openalex.org/I3129629310"]}],"countries":["VN"],"is_corresponding":false,"raw_author_name":"Thanh-An Pham","raw_affiliation_strings":["Ho Chi Minh University of Banking,Ho Chi Minh City,Vietnam","Ho Chi Minh University of Banking, Ho Chi Minh City, Vietnam"],"affiliations":[{"raw_affiliation_string":"Ho Chi Minh University of Banking,Ho Chi Minh City,Vietnam","institution_ids":["https://openalex.org/I3129629310"]},{"raw_affiliation_string":"Ho Chi Minh University of Banking, Ho Chi Minh City, Vietnam","institution_ids":["https://openalex.org/I3129629310"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5011476665"],"corresponding_institution_ids":["https://openalex.org/I4210148201"],"apc_list":null,"apc_paid":null,"fwci":0.4123,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.77752725,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"438","last_page":"443"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9991999864578247,"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":0.9991999864578247,"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/T12894","display_name":"Date Palm Research Studies","score":0.9286999702453613,"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/computer-science","display_name":"Computer science","score":0.7902117967605591},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7780382633209229},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7031618356704712},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6994040012359619},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5842894911766052},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.5742061138153076},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5331992506980896},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5231010913848877},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4413835406303406},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4408808648586273},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4132435917854309},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.324849009513855},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.30173224210739136},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07892706990242004}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7902117967605591},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7780382633209229},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7031618356704712},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6994040012359619},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5842894911766052},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.5742061138153076},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5331992506980896},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5231010913848877},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4413835406303406},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4408808648586273},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4132435917854309},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.324849009513855},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.30173224210739136},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07892706990242004},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icsse58758.2023.10227192","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icsse58758.2023.10227192","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Conference on System Science and Engineering (ICSSE)","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":21,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W2117539524","https://openalex.org/W2194775991","https://openalex.org/W2792194228","https://openalex.org/W2943686626","https://openalex.org/W2962843773","https://openalex.org/W2963446712","https://openalex.org/W2964137095","https://openalex.org/W3094502228","https://openalex.org/W3138516171","https://openalex.org/W3210931653","https://openalex.org/W4214636423","https://openalex.org/W4246193833","https://openalex.org/W4284670866","https://openalex.org/W4285289682","https://openalex.org/W4287203292","https://openalex.org/W4292794065","https://openalex.org/W4293450984","https://openalex.org/W6637373629","https://openalex.org/W6788135285","https://openalex.org/W6794559225"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W2397288865","https://openalex.org/W4321487865","https://openalex.org/W4313906399","https://openalex.org/W4239306820","https://openalex.org/W2368524271","https://openalex.org/W4391266461","https://openalex.org/W2590798552","https://openalex.org/W2095030957","https://openalex.org/W2066827917"],"abstract_inverted_index":{"Over":[0],"a":[1,84,89,124],"decade,":[2],"deep":[3,222,243],"learning":[4,25,57,99,199,244],"methods":[5,52],"using":[6,160,178,206],"convolutional":[7,179],"neural":[8,116],"network":[9,137],"(CNN)":[10],"architecture":[11],"have":[12,59],"achieved":[13],"breakthroughs":[14],"in":[15,73,169,181],"the":[16,22,55,70,74,101,154,170,182,196,230,234],"precision":[17,38],"criterion,":[18],"which":[19,87],"compared":[20,204],"to":[21,43,62,127,134,147,153,205,212,215,217],"traditional":[23],"machine":[24],"methods.":[26,245],"However,":[27],"those":[28],"approaches":[29],"still":[30],"faced":[31],"some":[32,50,114],"limitations":[33],"of":[34,76,92,103,159,177,198],"processing":[35,156,174,180],"time":[36,175,203],"and":[37,46,78,95,107,131,201,209,233],"when":[39],"they":[40],"are":[41,237],"applied":[42,61],"large":[44],"samples":[45],"hard":[47],"datasets.":[48],"Recently,":[49],"new":[51,85],"based":[53,220],"on":[54,221],"transformer":[56,97],"approach":[58,67],"been":[60],"image":[63,93,118,144],"processing.":[64],"This":[65,81],"direction":[66],"has":[68],"illustrated":[69,228],"promising":[71],"results":[72,122,152,186,226],"terms":[75],"accuracy":[77,214],"computational":[79],"time.":[80],"paper":[82],"presents":[83],"approach,":[86],"combines":[88],"pre-processing":[90,146],"technique":[91],"filtering":[94,145,190],"vision":[96],"(ViT)":[98],"for":[100,138],"problem":[102],"plant":[104],"insect":[105],"pests":[106],"diseases":[108],"recognition.":[109],"The":[110,140,224],"proposed":[111,141,235],"solution":[112,232],"involves":[113],"stages:":[115],"network-based":[117],"filtering,":[119],"then":[120,132],"passes":[121],"through":[123],"ViT":[125,155,161,207,231],"module":[126],"extract":[128],"feature":[129],"map,":[130],"fed":[133],"multiple":[135],"head":[136],"classification.":[139],"method":[142,236],"applies":[143],"highlight":[148],"features":[149],"before":[150],"passing":[151],"stage":[157],"instead":[158,176],"from":[162],"raw":[163],"input":[164],"images.":[165],"Furthermore,":[166],"element-wise":[167],"multiplication":[168],"frequency":[171],"domain":[172],"reduces":[173],"spatial":[183],"domain.":[184],"Experimental":[185],"demonstrate":[187],"that":[188,229],"applying":[189],"preprocessing":[191],"does":[192],"not":[193],"significantly":[194],"increase":[195],"number":[197],"parameters":[200],"training":[202],"directly":[208],"it":[210],"leverages":[211],"improve":[213],"compare":[216],"well-known":[218],"models":[219],"CNN.":[223],"research":[225],"also":[227],"reached":[238],"more":[239],"accurate":[240],"than":[241],"CNN-based":[242]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-24T23:09:58.560324","created_date":"2025-10-10T00:00:00"}
