{"id":"https://openalex.org/W3199691619","doi":"https://doi.org/10.1109/icufn49451.2021.9528658","title":"Classification of Growth Conditions in Paprika Leaf Using Deep Neural Network and Hyperspectral Images","display_name":"Classification of Growth Conditions in Paprika Leaf Using Deep Neural Network and Hyperspectral Images","publication_year":2021,"publication_date":"2021-08-17","ids":{"openalex":"https://openalex.org/W3199691619","doi":"https://doi.org/10.1109/icufn49451.2021.9528658","mag":"3199691619"},"language":"en","primary_location":{"id":"doi:10.1109/icufn49451.2021.9528658","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icufn49451.2021.9528658","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","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/A5026461547","display_name":"Kangin Choi","orcid":null},"institutions":[{"id":"https://openalex.org/I4210131650","display_name":"Korea Electronics Technology Institute","ror":"https://ror.org/039k6f508","country_code":"KR","type":"facility","lineage":["https://openalex.org/I2801339556","https://openalex.org/I4210089395","https://openalex.org/I4210131650"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Kangin Choi","raw_affiliation_strings":["IT Application Research Center, Korea Electronics Technology Institute, Jeon-ju, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"IT Application Research Center, Korea Electronics Technology Institute, Jeon-ju, Republic of Korea","institution_ids":["https://openalex.org/I4210131650"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002194333","display_name":"Keunho Park","orcid":"https://orcid.org/0000-0002-8804-6374"},"institutions":[{"id":"https://openalex.org/I4210131650","display_name":"Korea Electronics Technology Institute","ror":"https://ror.org/039k6f508","country_code":"KR","type":"facility","lineage":["https://openalex.org/I2801339556","https://openalex.org/I4210089395","https://openalex.org/I4210131650"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Keunho Park","raw_affiliation_strings":["IT Application Research Center, Korea Electronics Technology Institute, Jeon-ju, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"IT Application Research Center, Korea Electronics Technology Institute, Jeon-ju, Republic of Korea","institution_ids":["https://openalex.org/I4210131650"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015038893","display_name":"Sunghwan Jeong","orcid":"https://orcid.org/0000-0001-7945-3317"},"institutions":[{"id":"https://openalex.org/I4210131650","display_name":"Korea Electronics Technology Institute","ror":"https://ror.org/039k6f508","country_code":"KR","type":"facility","lineage":["https://openalex.org/I2801339556","https://openalex.org/I4210089395","https://openalex.org/I4210131650"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sunghwan Jeong","raw_affiliation_strings":["IT Application Research Center, Korea Electronics Technology Institute, Jeon-ju, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"IT Application Research Center, Korea Electronics Technology Institute, Jeon-ju, Republic of Korea","institution_ids":["https://openalex.org/I4210131650"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5026461547"],"corresponding_institution_ids":["https://openalex.org/I4210131650"],"apc_list":null,"apc_paid":null,"fwci":0.8754,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.79965524,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14365","display_name":"Leaf Properties and Growth Measurement","score":0.994700014591217,"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/T14365","display_name":"Leaf Properties and Growth Measurement","score":0.994700014591217,"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":0.9878000020980835,"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/T10616","display_name":"Smart Agriculture and AI","score":0.9864000082015991,"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/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.937558650970459},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.7393627166748047},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7160664200782776},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6059005856513977},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.5643067359924316},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5523216128349304},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5351897478103638},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.48123645782470703},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.36837488412857056}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.937558650970459},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.7393627166748047},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7160664200782776},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6059005856513977},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5643067359924316},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5523216128349304},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5351897478103638},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.48123645782470703},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.36837488412857056}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icufn49451.2021.9528658","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icufn49451.2021.9528658","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320325370","display_name":"National Research Council of Science and Technology","ror":"https://ror.org/058rymf81"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W2065524520","https://openalex.org/W2780615681","https://openalex.org/W2791655303","https://openalex.org/W3042976221","https://openalex.org/W6637373629","https://openalex.org/W6780440183"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W3034375524","https://openalex.org/W4230131218","https://openalex.org/W2070598848","https://openalex.org/W2060875994","https://openalex.org/W2027399350","https://openalex.org/W2044184146","https://openalex.org/W4313014865","https://openalex.org/W2353456014"],"abstract_inverted_index":{"Recently,":[0],"the":[1,11,30,38,63,68,82,92,99,121,124,136,154],"analysis":[2,33],"research":[3],"of":[4,13,27,41,55,108,123,128],"crop's":[5,42],"growth":[6,44,148],"condition":[7,45],"is":[8,57,65,78],"done":[9],"with":[10],"use":[12],"hyperspectral":[14,31,47,60],"image.":[15,157],"However,":[16],"there":[17],"are":[18,103,151],"many":[19],"factors":[20,24],"such":[21,112],"as":[22,113],"physical":[23],"and":[25,49,62,88,110,115,120,144],"complexity":[26],"data":[28,83,137],"make":[29],"image":[32],"difficult.":[34],"This":[35],"study":[36],"presents":[37],"classification":[39],"method":[40,132,139],"leaf":[43],"using":[46],"image(HSI)":[48],"Deep":[50],"Neural":[51],"Network(DNN).":[52],"Major":[53],"information":[54,69],"plants":[56],"acquired":[58],"through":[59],"image,":[61],"preprocessing":[64,77],"followed":[66],"for":[67,73,91],"to":[70,94],"be":[71,95],"used":[72,79],"DNN":[74,143],"learning.":[75],"The":[76,130],"by":[80,117],"cutting":[81],"in":[84,153],"small":[85],"patch":[86],"size":[87],"rotating":[89],"it":[90],"models":[93],"operated":[96],"effectively.":[97],"In":[98],"experiment,":[100],"paprika":[101],"leaves":[102,109],"divided":[104],"into":[105],"four":[106],"types":[107],"backgrounds":[111],"normal":[114],"damaged":[116],"harmful":[118],"insects,":[119],"result":[122],"experiment":[125],"showed":[126],"90.9%":[127],"accuracy.":[129],"presented":[131],"has":[133],"advantages":[134],"that":[135,150],"generation":[138],"does":[140],"not":[141],"affect":[142],"can":[145],"classify":[146],"various":[147],"conditions":[149],"difficult":[152],"existing":[155],"RGB":[156]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}