{"id":"https://openalex.org/W4409475464","doi":"https://doi.org/10.1109/ised63599.2024.10957590","title":"MBConvNet-CNS: A Lightweight Convolutional Neural Network (CNN) Architecture for Plant Disease Detection","display_name":"MBConvNet-CNS: A Lightweight Convolutional Neural Network (CNN) Architecture for Plant Disease Detection","publication_year":2024,"publication_date":"2024-12-20","ids":{"openalex":"https://openalex.org/W4409475464","doi":"https://doi.org/10.1109/ised63599.2024.10957590"},"language":"en","primary_location":{"id":"doi:10.1109/ised63599.2024.10957590","doi":"https://doi.org/10.1109/ised63599.2024.10957590","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ised63599.2024.10957590","pdf_url":null,"source":{"id":"https://openalex.org/S7407088698","display_name":"International Conference on Intelligent Systems and Embedded Design (ISED)","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":"2024 12th International Conference on Intelligent Systems and Embedded Design (ISED)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","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":null,"display_name":"Shikhar Burman","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Shikhar Burman","raw_affiliation_strings":["School of Computer Science and Engineering, Vellore Institute of Technology,Chennai,INDIA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Vellore Institute of Technology,Chennai,INDIA","institution_ids":["https://openalex.org/I876193797"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Nilabh Pandey","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Nilabh Pandey","raw_affiliation_strings":["School of Computer Science and Engineering, Vellore Institute of Technology,Chennai,INDIA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Vellore Institute of Technology,Chennai,INDIA","institution_ids":["https://openalex.org/I876193797"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Chetna Srivastava","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Chetna Srivastava","raw_affiliation_strings":["School of Computer Science and Engineering, Vellore Institute of Technology,Chennai,INDIA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Vellore Institute of Technology,Chennai,INDIA","institution_ids":["https://openalex.org/I876193797"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066513950","display_name":"Manas Ranjan Prusty","orcid":"https://orcid.org/0000-0003-2704-505X","observed_orcids":["https://orcid.org/0000-0003-2704-505X"]},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Manas Ranjan Prusty","raw_affiliation_strings":["Vellore Institute of Technology,Centre for Cyber Physical Systems,Chennai,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Vellore Institute of Technology,Centre for Cyber Physical Systems,Chennai,India","institution_ids":["https://openalex.org/I876193797"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I876193797"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.41722108,"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":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9984999895095825,"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":0.9984999895095825,"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":0.0012000000569969416,"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/plant-disease-detection","display_name":"plant disease detection","score":0.9990000128746033},{"id":"https://openalex.org/keywords/lightweight-convolutional-neural-network","display_name":"lightweight convolutional neural network","score":0.9449999928474426},{"id":"https://openalex.org/keywords/mobilenet","display_name":"MobileNet","score":0.6990000009536743},{"id":"https://openalex.org/keywords/efficientnet","display_name":"EfficientNet","score":0.9990000128746033},{"id":"https://openalex.org/keywords/ghost-module","display_name":"Ghost module","score":0.7379999756813049},{"id":"https://openalex.org/keywords/squeeze-and-excitation","display_name":"squeeze-and-excitation","score":0.8500000238418579},{"id":"https://openalex.org/keywords/edge-deployment","display_name":"edge deployment","score":0.7260000109672546},{"id":"https://openalex.org/keywords/real-time-image-classification","display_name":"real-time image classification","score":0.7419999837875366},{"id":"https://openalex.org/keywords/leaf-image-classification","display_name":"leaf image classification","score":0.8539999723434448},{"id":"https://openalex.org/keywords/crop-loss-reduction","display_name":"crop loss reduction","score":0.4269999861717224}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8183727264404297},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7212095260620117},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.5317752957344055},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48584339022636414},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3237994611263275},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.05653035640716553},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ised63599.2024.10957590","doi":"https://doi.org/10.1109/ised63599.2024.10957590","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ised63599.2024.10957590","pdf_url":null,"source":{"id":"https://openalex.org/S7407088698","display_name":"International Conference on Intelligent Systems and Embedded Design (ISED)","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":"2024 12th International Conference on Intelligent Systems and Embedded Design (ISED)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/2","display_name":"Zero hunger","score":0.875}],"sustainable_development_goals_aurora":[{"id":"https://metadata.un.org/sdg/2","display_name":"Zero hunger","score":0.6100000143051147}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2473156356","https://openalex.org/W2752782242","https://openalex.org/W2789255992","https://openalex.org/W2884068670","https://openalex.org/W3129248922","https://openalex.org/W4223895421","https://openalex.org/W4296442033","https://openalex.org/W4313270759","https://openalex.org/W4327738674","https://openalex.org/W4328115538","https://openalex.org/W4361212918","https://openalex.org/W4361230837","https://openalex.org/W4381328810","https://openalex.org/W4388914055"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2033914206","https://openalex.org/W2042327336"],"abstract_inverted_index":{"Plant":[0],"diseases":[1],"pose":[2],"a":[3,42,81,108,164],"big":[4],"threat":[5],"to":[6,67,117,131],"agricultural":[7],"productivity":[8],"worldwide,":[9],"so":[10],"their":[11],"detection":[12,59],"technique":[13],"should":[14],"be":[15,53,68],"strong":[16],"and":[17,25,76,96,135,144,159],"efficient.":[18],"Classically,":[19],"plant":[20,57,104,123,171],"disease":[21,58,105],"identifications":[22],"are":[23,63],"time-consuming":[24],"usually":[26],"require":[27],"expert":[28],"knowledge;":[29],"this":[30],"makes":[31],"the":[32,46,89,132,152],"use":[33,47],"of":[34,44,48,91,120,122,155,170],"large-scale":[35],"agriculture":[36],"difficult.":[37],"The":[38,112],"following":[39],"paper":[40],"presents":[41],"review":[43],"how":[45],"lightweight":[49,136,156],"CNN":[50,157],"models":[51,61,137],"can":[52],"used":[54],"for":[55,103,142,167,177],"real-time":[56],"with":[60,107],"that":[62,99,151],"computationally":[64],"efficient":[65],"enough":[66],"deployed":[69],"on":[70,85,139],"edge":[71],"devices":[72],"such":[73],"as":[74],"smartphones":[75],"drones.":[77],"We":[78],"shall":[79,162],"propose":[80],"novel":[82],"model":[83,114],"based":[84,138],"state-of-the-art":[86],"architectures":[87,158],"comprising":[88],"likes":[90],"MobileNet,":[92],"EfficientNet,":[93],"Ghost":[94],"Modules,":[95],"Squeeze-and-Excitation":[97],"blocks":[98],"achieve":[100],"high":[101],"accuracy":[102,143],"identification":[106],"low":[109],"computation":[110],"footprint.":[111],"proposed":[113],"was":[115],"applied":[116],"different":[118],"datasets":[119],"images":[121],"leaves.":[124],"Experimental":[125],"results":[126],"proved":[127],"it":[128],"at":[129],"par":[130],"previous":[133],"heavyweight":[134],"deep":[140],"learning":[141],"time":[145],"consumed.":[146],"These":[147],"findings":[148],"point":[149],"out":[150],"correct":[153],"integration":[154],"innovative":[160],"modules":[161],"yield":[163],"feasible":[165],"solution":[166],"effective":[168],"management":[169],"diseases,":[172],"making":[173],"timely":[174],"interventions":[175],"possible":[176],"reducing":[178],"crop":[179],"losses.":[180]},"counts_by_year":[],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2025-10-10T00:00:00"}
