{"id":"https://openalex.org/W4416250556","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227746","title":"MFWNet: Optimizing UAV-based Detection of Wheat Fusarium Head Blight Using Multi-scale Feature Fusion and Wavelet Convolution","display_name":"MFWNet: Optimizing UAV-based Detection of Wheat Fusarium Head Blight Using Multi-scale Feature Fusion and Wavelet Convolution","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416250556","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227746"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11227746","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227746","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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/A5108050441","display_name":"Jialin Li","orcid":"https://orcid.org/0000-0003-3786-004X"},"institutions":[{"id":"https://openalex.org/I89652312","display_name":"Northwest A&F University","ror":"https://ror.org/0051rme32","country_code":"CN","type":"education","lineage":["https://openalex.org/I89652312"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jialin Li","raw_affiliation_strings":["Northwest A&F University,College of Information Engineering,Yangling,China"],"affiliations":[{"raw_affiliation_string":"Northwest A&F University,College of Information Engineering,Yangling,China","institution_ids":["https://openalex.org/I89652312"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101974536","display_name":"Feilong Li","orcid":"https://orcid.org/0009-0002-4430-4324"},"institutions":[{"id":"https://openalex.org/I89652312","display_name":"Northwest A&F University","ror":"https://ror.org/0051rme32","country_code":"CN","type":"education","lineage":["https://openalex.org/I89652312"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feilong Li","raw_affiliation_strings":["Northwest A&F University,College of Information Engineering,Yangling,China"],"affiliations":[{"raw_affiliation_string":"Northwest A&F University,College of Information Engineering,Yangling,China","institution_ids":["https://openalex.org/I89652312"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034764245","display_name":"Dexian Li","orcid":"https://orcid.org/0000-0002-6994-7767"},"institutions":[{"id":"https://openalex.org/I89652312","display_name":"Northwest A&F University","ror":"https://ror.org/0051rme32","country_code":"CN","type":"education","lineage":["https://openalex.org/I89652312"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongxiang Li","raw_affiliation_strings":["Northwest A&F University,College of Information Engineering,Yangling,China"],"affiliations":[{"raw_affiliation_string":"Northwest A&F University,College of Information Engineering,Yangling,China","institution_ids":["https://openalex.org/I89652312"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054791029","display_name":"Mingchen Feng","orcid":"https://orcid.org/0000-0002-9954-0757"},"institutions":[{"id":"https://openalex.org/I89652312","display_name":"Northwest A&F University","ror":"https://ror.org/0051rme32","country_code":"CN","type":"education","lineage":["https://openalex.org/I89652312"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingchen Feng","raw_affiliation_strings":["Northwest A&F University,College of Information Engineering,Yangling,China"],"affiliations":[{"raw_affiliation_string":"Northwest A&F University,College of Information Engineering,Yangling,China","institution_ids":["https://openalex.org/I89652312"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045406482","display_name":"Ben Gu","orcid":null},"institutions":[{"id":"https://openalex.org/I89652312","display_name":"Northwest A&F University","ror":"https://ror.org/0051rme32","country_code":"CN","type":"education","lineage":["https://openalex.org/I89652312"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ben Gu","raw_affiliation_strings":["Northwest A&F University,College of Information Engineering,Yangling,China"],"affiliations":[{"raw_affiliation_string":"Northwest A&F University,College of Information Engineering,Yangling,China","institution_ids":["https://openalex.org/I89652312"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100636819","display_name":"Ziqi Liu","orcid":"https://orcid.org/0000-0002-6845-0556"},"institutions":[{"id":"https://openalex.org/I89652312","display_name":"Northwest A&F University","ror":"https://ror.org/0051rme32","country_code":"CN","type":"education","lineage":["https://openalex.org/I89652312"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziqi Liu","raw_affiliation_strings":["Northwest A&F University,College of Information Engineering,Yangling,China"],"affiliations":[{"raw_affiliation_string":"Northwest A&F University,College of Information Engineering,Yangling,China","institution_ids":["https://openalex.org/I89652312"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002355096","display_name":"Jian\u2010Ge Wang","orcid":"https://orcid.org/0000-0003-3493-6694"},"institutions":[{"id":"https://openalex.org/I89652312","display_name":"Northwest A&F University","ror":"https://ror.org/0051rme32","country_code":"CN","type":"education","lineage":["https://openalex.org/I89652312"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiange Wang","raw_affiliation_strings":["Northwest A&F University,College of Information Engineering,Yangling,China"],"affiliations":[{"raw_affiliation_string":"Northwest A&F University,College of Information Engineering,Yangling,China","institution_ids":["https://openalex.org/I89652312"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081284290","display_name":"Rui Mao","orcid":"https://orcid.org/0000-0001-5345-8128"},"institutions":[{"id":"https://openalex.org/I89652312","display_name":"Northwest A&F University","ror":"https://ror.org/0051rme32","country_code":"CN","type":"education","lineage":["https://openalex.org/I89652312"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rui Mao","raw_affiliation_strings":["Northwest A&F University,College of Information Engineering,Yangling,China"],"affiliations":[{"raw_affiliation_string":"Northwest A&F University,College of Information Engineering,Yangling,China","institution_ids":["https://openalex.org/I89652312"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5108050441"],"corresponding_institution_ids":["https://openalex.org/I89652312"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.24712205,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.838100016117096,"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":0.838100016117096,"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.062199998646974564,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.039500001817941666,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6004999876022339},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5960999727249146},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5410000085830688},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.4973999857902527},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.49309998750686646},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4629000127315521},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.41929998993873596},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.40639999508857727},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.3977000117301941}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7160000205039978},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6725999712944031},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6004999876022339},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5960999727249146},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5410000085830688},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.4973999857902527},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.49309998750686646},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4629000127315521},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.41929998993873596},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.40639999508857727},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.3977000117301941},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.3912999927997589},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.3763999938964844},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.3693000078201294},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3659000098705292},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.359499990940094},{"id":"https://openalex.org/C2776429412","wikidata":"https://www.wikidata.org/wiki/Q4688011","display_name":"Aerial image","level":3,"score":0.3578999936580658},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.3005000054836273},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.29350000619888306},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2766000032424927},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.2727999985218048},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.2687000036239624},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.26829999685287476},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.26600000262260437},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.25619998574256897},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.25049999356269836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11227746","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227746","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320337504","display_name":"Research and Development","ror":"https://ror.org/027s68j25"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1902027874","https://openalex.org/W2194775991","https://openalex.org/W2395611524","https://openalex.org/W2560023338","https://openalex.org/W2787091153","https://openalex.org/W2916798096","https://openalex.org/W3118053069","https://openalex.org/W3138516171","https://openalex.org/W3170841864","https://openalex.org/W3211490618","https://openalex.org/W4214893857","https://openalex.org/W4226334005","https://openalex.org/W4254197176","https://openalex.org/W4286208835","https://openalex.org/W4289752563","https://openalex.org/W4312443924","https://openalex.org/W4327723655","https://openalex.org/W4385346076","https://openalex.org/W4389264715","https://openalex.org/W4390099726","https://openalex.org/W4390999417","https://openalex.org/W4396791518","https://openalex.org/W4396918399","https://openalex.org/W4401161899","https://openalex.org/W4403864167","https://openalex.org/W4403887646","https://openalex.org/W4409262904"],"related_works":[],"abstract_inverted_index":{"Fusarium":[0],"Head":[1],"Blight":[2],"(FHB)":[3],"poses":[4],"a":[5,55,162,168],"substantial":[6],"threat":[7],"to":[8,22],"global":[9],"wheat":[10,140],"production":[11],"and":[12,26,35,72,92,94,111,136,167,183],"food":[13,203],"security.":[14,204],"Current":[15],"manual":[16,133],"detection":[17,37,41,195],"methods":[18],"are":[19],"inadequate":[20],"due":[21],"their":[23],"labor-intensive":[24],"nature":[25],"inaccuracies,":[27],"whereas":[28],"advancements":[29],"in":[30,179],"Unmanned":[31],"Aerial":[32],"Vehicle":[33],"(UAV)":[34],"object":[36],"technologies":[38],"have":[39],"improved":[40,101],"accuracy":[42,182],"but":[43],"still":[44],"struggle":[45],"with":[46,155,199],"accurately":[47],"defining":[48],"disease":[49],"boundaries.":[50],"This":[51,185],"study":[52],"presents":[53],"MFWNet,":[54],"robust":[56],"UAV-based":[57,193],"semantic":[58],"segmentation":[59],"model":[60],"specifically":[61],"designed":[62],"for":[63,88,100,120,127,151,191,202],"FHB":[64,141,194],"detection.":[65],"MFWNet":[66,160],"innovatively":[67],"integrates":[68],"Multi-scale":[69],"Feature":[70,96],"Fusion":[71],"Wavelet":[73],"Convolution,":[74],"optimizing":[75],"large":[76],"kernel":[77],"convolutions.":[78],"Key":[79],"innovations":[80],"include":[81],"the":[82,95,115,125],"Efficient":[83],"Contextual":[84],"Convolutional":[85],"Module":[86,99],"(ECCM)":[87],"multi-scale":[89],"feature":[90,102],"extraction":[91],"fusion":[93],"Enhancement":[97],"(FE)":[98],"representation":[103],"focused":[104],"on":[105],"FHB,":[106],"thereby":[107],"improving":[108],"contextual":[109],"understanding":[110],"boundary":[112],"precision.":[113],"Additionally,":[114],"implementation":[116],"of":[117,144,165,173,181],"semi-supervised":[118],"learning":[119],"image":[121],"annotation":[122,134],"significantly":[123],"reduces":[124],"need":[126],"extensive":[128],"labeled":[129],"datasets,":[130],"which":[131],"decreases":[132],"efforts":[135],"boosts":[137],"scalability.":[138],"A":[139],"dataset":[142],"consisting":[143],"1,807":[145],"pixel-level":[146],"annotated":[147],"images":[148],"was":[149],"constructed":[150],"this":[152],"purpose.":[153],"Remarkably,":[154],"only":[156],"10.15":[157],"million":[158],"parameters,":[159],"achieves":[161],"mean":[163,169],"Accuracy":[164],"85.78%":[166],"Intersection":[170],"over":[171],"Union":[172],"73.54%,":[174],"outperforming":[175],"nine":[176],"comparative":[177],"models":[178],"terms":[180],"efficiency.":[184],"method":[186],"offers":[187],"an":[188],"effective":[189],"solution":[190],"large-scale":[192],"under":[196],"real-field":[197],"conditions,":[198],"significant":[200],"implications":[201]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-11-14T00:00:00"}