{"id":"https://openalex.org/W2980432044","doi":"https://doi.org/10.1109/access.2019.2947510","title":"Plant Leaves Classification: A Few-Shot Learning Method Based on Siamese Network","display_name":"Plant Leaves Classification: A Few-Shot Learning Method Based on Siamese Network","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2980432044","doi":"https://doi.org/10.1109/access.2019.2947510","mag":"2980432044"},"language":"en","primary_location":{"id":"doi:10.1109/access.2019.2947510","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2947510","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08869770.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08869770.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100372349","display_name":"Bin Wang","orcid":"https://orcid.org/0000-0002-3329-2156"},"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":"Bin Wang","raw_affiliation_strings":["School of Technology, Beijing Forestry University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-3329-2156","affiliations":[{"raw_affiliation_string":"School of Technology, Beijing Forestry University, Beijing, China","institution_ids":["https://openalex.org/I31683504"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054552774","display_name":"Dian Wang","orcid":"https://orcid.org/0000-0003-4447-3822"},"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":"Dian Wang","raw_affiliation_strings":["School of Technology, Beijing Forestry University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Technology, Beijing Forestry University, Beijing, China","institution_ids":["https://openalex.org/I31683504"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100372349"],"corresponding_institution_ids":["https://openalex.org/I31683504"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":13.0412,"has_fulltext":true,"cited_by_count":115,"citation_normalized_percentile":{"value":0.98586609,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"7","issue":null,"first_page":"151754","last_page":"151763"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9987000226974487,"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":0.9987000226974487,"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":0.9524999856948853,"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.9427000284194946,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7298158407211304},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7073782682418823},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6616334319114685},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6412056088447571},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.5386831760406494},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5334503650665283},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.4730084240436554},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4547916054725647},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.44885843992233276},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42574793100357056},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.4179137349128723},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4103221893310547},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.12072432041168213}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7298158407211304},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7073782682418823},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6616334319114685},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6412056088447571},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.5386831760406494},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5334503650665283},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.4730084240436554},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4547916054725647},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.44885843992233276},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42574793100357056},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.4179137349128723},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4103221893310547},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.12072432041168213},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2019.2947510","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2947510","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08869770.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:a2cd617d5e05449294b3e79b2fa5b28f","is_oa":true,"landing_page_url":"https://doaj.org/article/a2cd617d5e05449294b3e79b2fa5b28f","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":"IEEE Access, Vol 7, Pp 151754-151763 (2019)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2019.2947510","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2947510","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08869770.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2649565651","display_name":null,"funder_award_id":"2015ZCQ-GX-01","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G3209530012","display_name":null,"funder_award_id":"2015ZCQ-GX-01","funder_id":"https://openalex.org/F4320321392","funder_display_name":"Northwestern Polytechnical University"}],"funders":[{"id":"https://openalex.org/F4320321392","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86"},{"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/W2980432044.pdf","grobid_xml":"https://content.openalex.org/works/W2980432044.grobid-xml"},"referenced_works_count":37,"referenced_works":["https://openalex.org/W645748567","https://openalex.org/W1171247708","https://openalex.org/W1181899764","https://openalex.org/W1949176574","https://openalex.org/W1968896562","https://openalex.org/W2010635848","https://openalex.org/W2060964605","https://openalex.org/W2081072138","https://openalex.org/W2157364932","https://openalex.org/W2163605009","https://openalex.org/W2171590421","https://openalex.org/W2213241010","https://openalex.org/W2253429366","https://openalex.org/W2293708215","https://openalex.org/W2360747104","https://openalex.org/W2601450892","https://openalex.org/W2618747515","https://openalex.org/W2758692375","https://openalex.org/W2783847648","https://openalex.org/W2790299291","https://openalex.org/W2791899572","https://openalex.org/W2792026505","https://openalex.org/W2795866616","https://openalex.org/W2800902001","https://openalex.org/W2879596577","https://openalex.org/W2885442682","https://openalex.org/W2898204262","https://openalex.org/W2905563945","https://openalex.org/W2909139008","https://openalex.org/W2919115771","https://openalex.org/W2927613875","https://openalex.org/W6621161076","https://openalex.org/W6684191040","https://openalex.org/W6696653927","https://openalex.org/W6735236233","https://openalex.org/W6749235195","https://openalex.org/W6992483707"],"related_works":["https://openalex.org/W2785302227","https://openalex.org/W2959160600","https://openalex.org/W2354127086","https://openalex.org/W4309224981","https://openalex.org/W2519241726","https://openalex.org/W2107754327","https://openalex.org/W2153677915","https://openalex.org/W128856181","https://openalex.org/W2005234362","https://openalex.org/W1997235926"],"abstract_inverted_index":{"In":[0,34,111],"recent":[1],"years,":[2],"the":[3,43,61,80,88,121,131,148,174,177,184],"method":[4,40,118,186],"of":[5,29,63,133,176,197],"plant":[6],"leaf":[7,19,52,94,103,134],"classification":[8,53,154,191],"by":[9,69],"deep":[10,23],"learning":[11,24,39],"has":[12],"gradually":[13],"become":[14],"mature.":[15],"However,":[16],"training":[17],"a":[18,26,37,51,56,70,83,113,137,159,189,194],"classifier":[20,141],"based":[21,41],"on":[22,42],"requires":[25],"large":[27],"number":[28],"samples":[30,95,104],"for":[31,119],"supervised":[32,198],"training.":[33],"this":[35],"paper,":[36],"few-shot":[38],"Siamese":[44],"network":[45,75,81],"framework":[46],"is":[47,124,142,156],"proposed":[48,185],"to":[49,86,98,129,144,172],"solve":[50],"problem":[54],"with":[55,76,193],"small":[57,195],"sample":[58],"size.":[59],"First,":[60],"features":[62],"two":[64],"different":[65,102],"images":[66],"are":[67,96,105,170],"extracted":[68],"parallel":[71],"two-way":[72],"convolutional":[73],"neural":[74],"weight":[77],"sharing.":[78],"Then,":[79],"uses":[82],"loss":[84],"function":[85],"learn":[87],"metric":[89,122,150],"space,":[90],"in":[91,147],"which":[92,126],"similar":[93],"close":[97],"each":[99,109],"other":[100],"and":[101,167],"far":[106],"away":[107],"from":[108],"other.":[110],"addition,":[112],"spatial":[114],"structure":[115],"optimizer":[116],"(SSO)":[117],"constructing":[120],"space":[123],"proposed,":[125],"will":[127],"help":[128],"improve":[130],"accuracy":[132,155,192],"classification.":[135],"Finally,":[136],"k-nearest":[138],"neighbor":[139],"(kNN)":[140],"used":[143,157,171],"classify":[145],"leaves":[146],"learned":[149],"space.":[151],"The":[152,162,179],"average":[153],"as":[158],"performance":[160,175],"measure.":[161],"open":[163],"access":[164],"Flavia,":[165],"Swedish":[166],"Leafsnap":[168],"datasets":[169],"evaluate":[173],"method.":[178],"experimental":[180],"results":[181],"show":[182],"that":[183],"can":[187],"achieve":[188],"high":[190],"size":[196],"samples.":[199]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":25},{"year":2024,"cited_by_count":16},{"year":2023,"cited_by_count":23},{"year":2022,"cited_by_count":21},{"year":2021,"cited_by_count":13},{"year":2020,"cited_by_count":11}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}