{"id":"https://openalex.org/W3005165768","doi":"https://doi.org/10.3390/info11020095","title":"Using Deep Learning for Image-Based Different Degrees of Ginkgo Leaf Disease Classification","display_name":"Using Deep Learning for Image-Based Different Degrees of Ginkgo Leaf Disease Classification","publication_year":2020,"publication_date":"2020-02-10","ids":{"openalex":"https://openalex.org/W3005165768","doi":"https://doi.org/10.3390/info11020095","mag":"3005165768"},"language":"en","primary_location":{"id":"doi:10.3390/info11020095","is_oa":true,"landing_page_url":"https://doi.org/10.3390/info11020095","pdf_url":"https://www.mdpi.com/2078-2489/11/2/95/pdf?version=1583030844","source":{"id":"https://openalex.org/S4210219776","display_name":"Information","issn_l":"2078-2489","issn":["2078-2489"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Information","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2078-2489/11/2/95/pdf?version=1583030844","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5049664631","display_name":"Kaizhou Li","orcid":"https://orcid.org/0000-0001-5919-7264"},"institutions":[{"id":"https://openalex.org/I31683504","display_name":"Beijing Forestry University","ror":"https://ror.org/04xv2pc41","country_code":"CN","type":"education","lineage":["https://openalex.org/I1327237609","https://openalex.org/I31683504","https://openalex.org/I4210127390"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kaizhou Li","raw_affiliation_strings":["School of Technology, Beijing Forestry University, Beijing 100083, China"],"raw_orcid":"https://orcid.org/0000-0001-5919-7264","affiliations":[{"raw_affiliation_string":"School of Technology, Beijing Forestry University, Beijing 100083, China","institution_ids":["https://openalex.org/I31683504"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071321838","display_name":"Jianhui Lin","orcid":"https://orcid.org/0000-0002-5868-9360"},"institutions":[{"id":"https://openalex.org/I31683504","display_name":"Beijing Forestry University","ror":"https://ror.org/04xv2pc41","country_code":"CN","type":"education","lineage":["https://openalex.org/I1327237609","https://openalex.org/I31683504","https://openalex.org/I4210127390"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jianhui Lin","raw_affiliation_strings":["School of Technology, Beijing Forestry University, Beijing 100083, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Technology, Beijing Forestry University, Beijing 100083, China","institution_ids":["https://openalex.org/I31683504"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100611029","display_name":"Jinrong Liu","orcid":"https://orcid.org/0000-0003-3492-6445"},"institutions":[{"id":"https://openalex.org/I31683504","display_name":"Beijing Forestry University","ror":"https://ror.org/04xv2pc41","country_code":"CN","type":"education","lineage":["https://openalex.org/I1327237609","https://openalex.org/I31683504","https://openalex.org/I4210127390"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinrong Liu","raw_affiliation_strings":["School of Technology, Beijing Forestry University, Beijing 100083, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Technology, Beijing Forestry University, Beijing 100083, China","institution_ids":["https://openalex.org/I31683504"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030032799","display_name":"Yandong Zhao","orcid":"https://orcid.org/0000-0002-0269-2034"},"institutions":[{"id":"https://openalex.org/I31683504","display_name":"Beijing Forestry University","ror":"https://ror.org/04xv2pc41","country_code":"CN","type":"education","lineage":["https://openalex.org/I1327237609","https://openalex.org/I31683504","https://openalex.org/I4210127390"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yandong Zhao","raw_affiliation_strings":["School of Technology, Beijing Forestry University, Beijing 100083, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Technology, Beijing Forestry University, Beijing 100083, China","institution_ids":["https://openalex.org/I31683504"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5071321838"],"corresponding_institution_ids":["https://openalex.org/I31683504"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":9.2488,"has_fulltext":true,"cited_by_count":64,"citation_normalized_percentile":{"value":0.97741173,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"11","issue":"2","first_page":"95","last_page":"95"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.996399998664856,"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":0.996399998664856,"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/T12707","display_name":"Vehicle License Plate Recognition","score":0.9379000067710876,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14144","display_name":"Neurological Disease Mechanisms and Treatments","score":0.9276999831199646,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/ginkgo-biloba","display_name":"Ginkgo biloba","score":0.8380757570266724},{"id":"https://openalex.org/keywords/ginkgo","display_name":"Ginkgo","score":0.7824075222015381},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6029899716377258},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5440792441368103},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5284124612808228},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5136899352073669},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4666818082332611},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.4651288688182831},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4642449617385864},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.44501084089279175},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4385715126991272},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43838319182395935},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2604820430278778},{"id":"https://openalex.org/keywords/traditional-medicine","display_name":"Traditional medicine","score":0.21935772895812988},{"id":"https://openalex.org/keywords/botany","display_name":"Botany","score":0.2029131054878235},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.13980194926261902},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.10956153273582458}],"concepts":[{"id":"https://openalex.org/C2778998842","wikidata":"https://www.wikidata.org/wiki/Q43284","display_name":"Ginkgo biloba","level":2,"score":0.8380757570266724},{"id":"https://openalex.org/C2779706397","wikidata":"https://www.wikidata.org/wiki/Q149461","display_name":"Ginkgo","level":2,"score":0.7824075222015381},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6029899716377258},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5440792441368103},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5284124612808228},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5136899352073669},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4666818082332611},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.4651288688182831},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4642449617385864},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.44501084089279175},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4385715126991272},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43838319182395935},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2604820430278778},{"id":"https://openalex.org/C556039675","wikidata":"https://www.wikidata.org/wiki/Q771035","display_name":"Traditional medicine","level":1,"score":0.21935772895812988},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.2029131054878235},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.13980194926261902},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.10956153273582458},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/info11020095","is_oa":true,"landing_page_url":"https://doi.org/10.3390/info11020095","pdf_url":"https://www.mdpi.com/2078-2489/11/2/95/pdf?version=1583030844","source":{"id":"https://openalex.org/S4210219776","display_name":"Information","issn_l":"2078-2489","issn":["2078-2489"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Information","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:abe0124f1ea540d59b34864866a38eb6","is_oa":true,"landing_page_url":"https://doaj.org/article/abe0124f1ea540d59b34864866a38eb6","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Information, Vol 11, Iss 2, p 95 (2020)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2078-2489/11/2/95/","is_oa":true,"landing_page_url":"http://dx.doi.org/10.3390/info11020095","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Information","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/info11020095","is_oa":true,"landing_page_url":"https://doi.org/10.3390/info11020095","pdf_url":"https://www.mdpi.com/2078-2489/11/2/95/pdf?version=1583030844","source":{"id":"https://openalex.org/S4210219776","display_name":"Information","issn_l":"2078-2489","issn":["2078-2489"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Information","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7374290610","display_name":null,"funder_award_id":"2015ZCQ-GX-03","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G8134282400","display_name":null,"funder_award_id":"NO. 2015ZCQ- GX-03","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3005165768.pdf","grobid_xml":"https://content.openalex.org/works/W3005165768.grobid-xml"},"referenced_works_count":29,"referenced_works":["https://openalex.org/W2012343141","https://openalex.org/W2014408679","https://openalex.org/W2038175477","https://openalex.org/W2076063813","https://openalex.org/W2089843998","https://openalex.org/W2093090592","https://openalex.org/W2112796928","https://openalex.org/W2127799479","https://openalex.org/W2152415998","https://openalex.org/W2162772680","https://openalex.org/W2163500751","https://openalex.org/W2163605009","https://openalex.org/W2183341477","https://openalex.org/W2209016647","https://openalex.org/W2213241010","https://openalex.org/W2213612645","https://openalex.org/W2299597245","https://openalex.org/W2470368200","https://openalex.org/W2470803522","https://openalex.org/W2473156356","https://openalex.org/W2568155635","https://openalex.org/W2614850301","https://openalex.org/W2618530766","https://openalex.org/W2731165298","https://openalex.org/W2743583628","https://openalex.org/W2789255992","https://openalex.org/W2963801405","https://openalex.org/W6684191040","https://openalex.org/W7047264204"],"related_works":["https://openalex.org/W2317564093","https://openalex.org/W2367518817","https://openalex.org/W2517058087","https://openalex.org/W4299834831","https://openalex.org/W4387317288","https://openalex.org/W2366627961","https://openalex.org/W2305859635","https://openalex.org/W2377489352","https://openalex.org/W2502541559","https://openalex.org/W2319936300"],"abstract_inverted_index":{"Diseases":[0],"from":[1],"Ginkgo":[2,24],"biloba":[3,25],"have":[4,162],"brought":[5],"great":[6,41],"losses":[7,34],"to":[8,32,59],"medicine":[9],"and":[10,47,73,79,87,101,120,169],"the":[11,15,52,61,71,107,126,146,152,167],"economy.":[12],"Therefore,":[13],"if":[14],"degree":[16],"of":[17,64,148,151,158,172],"disease":[18,45],"can":[19],"be":[20],"automatically":[21],"identified":[22],"in":[23,35,43],"leaves,":[26],"people":[27],"will":[28,161],"take":[29],"appropriate":[30],"measures":[31],"avoid":[33],"advance.":[36],"Deep":[37],"learning":[38],"has":[39],"made":[40],"achievements":[42],"plant":[44,154],"identification":[46],"classification.":[48],"For":[49],"this":[50,159],"paper,":[51],"convolution":[53],"neural":[54],"network":[55],"model":[56,113,129],"was":[57,96,131],"used":[58,70],"classify":[60],"different":[62,149],"degrees":[63,150],"ginkgo":[65,173],"leaf":[66,174],"disease.":[67,155],"This":[68],"study":[69,160],"VGGNet-16":[72],"Inception":[74,111,127],"V3":[75,112,128],"models.":[76],"After":[77],"preprocessing":[78],"training":[80],"1322":[81],"original":[82,89],"images":[83,90],"under":[84,91,98,103,117,122],"laboratory":[85,99,118],"conditions":[86,100,105,119],"2408":[88],"field":[92,104,123,135],"conditions,":[93],"98.44%":[94],"accuracy":[95,116],"achieved":[97,114],"92.19%":[102],"with":[106],"VGG":[108],"model.":[109],"The":[110,156],"92.3%":[115],"93.2%":[121],"conditions.":[124,136],"Thus,":[125],"structure":[130],"more":[132],"suitable":[133],"for":[134],"To":[137],"our":[138],"knowledge,":[139],"there":[140],"is":[141],"very":[142],"little":[143],"research":[144],"on":[145,166],"classification":[147],"same":[153],"success":[157],"a":[163],"significant":[164],"impact":[165],"prediction":[168],"early":[170],"prevention":[171],"blight.":[175]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":13},{"year":2022,"cited_by_count":15},{"year":2021,"cited_by_count":11},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
