{"id":"https://openalex.org/W4312199253","doi":"https://doi.org/10.1109/ifuzzy55320.2022.9985222","title":"Sugar Beets and Weed Detection using Semantic Segmentation","display_name":"Sugar Beets and Weed Detection using Semantic Segmentation","publication_year":2022,"publication_date":"2022-11-03","ids":{"openalex":"https://openalex.org/W4312199253","doi":"https://doi.org/10.1109/ifuzzy55320.2022.9985222"},"language":"en","primary_location":{"id":"doi:10.1109/ifuzzy55320.2022.9985222","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ifuzzy55320.2022.9985222","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Conference on Fuzzy Theory and Its Applications (iFUZZY)","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/A5003942715","display_name":"Xin-Zhi Hu","orcid":null},"institutions":[{"id":"https://openalex.org/I71007489","display_name":"Kyungnam University","ror":"https://ror.org/037pkxm09","country_code":"KR","type":"education","lineage":["https://openalex.org/I71007489"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Xin-Zhi Hu","raw_affiliation_strings":["University of Kyungnam,Department of IT Convergence Enineering,Changwon City,South Korea","Department of IT Convergence Enineering, University of Kyungnam, Changwon City, South Korea"],"affiliations":[{"raw_affiliation_string":"University of Kyungnam,Department of IT Convergence Enineering,Changwon City,South Korea","institution_ids":["https://openalex.org/I71007489"]},{"raw_affiliation_string":"Department of IT Convergence Enineering, University of Kyungnam, Changwon City, South Korea","institution_ids":["https://openalex.org/I71007489"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035813083","display_name":"Wang\u2010Su Jeon","orcid":"https://orcid.org/0000-0001-8887-2513"},"institutions":[{"id":"https://openalex.org/I71007489","display_name":"Kyungnam University","ror":"https://ror.org/037pkxm09","country_code":"KR","type":"education","lineage":["https://openalex.org/I71007489"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Wang-Su Jeon","raw_affiliation_strings":["University of Kyungnam,Department of Computer Engineering,Changwon City,South Korea","Department of Computer Engineering, University of Kyungnam, Changwon City, South Korea"],"affiliations":[{"raw_affiliation_string":"University of Kyungnam,Department of Computer Engineering,Changwon City,South Korea","institution_ids":["https://openalex.org/I71007489"]},{"raw_affiliation_string":"Department of Computer Engineering, University of Kyungnam, Changwon City, South Korea","institution_ids":["https://openalex.org/I71007489"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018274633","display_name":"Sang\u2010Yong Rhee","orcid":null},"institutions":[{"id":"https://openalex.org/I71007489","display_name":"Kyungnam University","ror":"https://ror.org/037pkxm09","country_code":"KR","type":"education","lineage":["https://openalex.org/I71007489"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sang-Yong Rhee","raw_affiliation_strings":["University of Kyungnam,Department of Computer Engineering,Changwon City,South Korea","Department of Computer Engineering, University of Kyungnam, Changwon City, South Korea"],"affiliations":[{"raw_affiliation_string":"University of Kyungnam,Department of Computer Engineering,Changwon City,South Korea","institution_ids":["https://openalex.org/I71007489"]},{"raw_affiliation_string":"Department of Computer Engineering, University of Kyungnam, Changwon City, South Korea","institution_ids":["https://openalex.org/I71007489"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5003942715"],"corresponding_institution_ids":["https://openalex.org/I71007489"],"apc_list":null,"apc_paid":null,"fwci":1.6713,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.85335706,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9994999766349792,"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":0.9994999766349792,"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/T12894","display_name":"Date Palm Research Studies","score":0.9646999835968018,"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/T12697","display_name":"Water Quality Monitoring Technologies","score":0.9448999762535095,"subfield":{"id":"https://openalex.org/subfields/2312","display_name":"Water Science and Technology"},"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/computer-science","display_name":"Computer science","score":0.7716224193572998},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6974908113479614},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.6638123989105225},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6569042205810547},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6296367645263672},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6294903755187988},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.573853075504303},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5256643891334534},{"id":"https://openalex.org/keywords/semantic-feature","display_name":"Semantic feature","score":0.5040437579154968},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40866619348526},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.33223870396614075},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12389734387397766},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08587446808815002}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7716224193572998},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6974908113479614},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6638123989105225},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6569042205810547},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6296367645263672},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6294903755187988},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.573853075504303},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5256643891334534},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.5040437579154968},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40866619348526},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33223870396614075},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12389734387397766},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08587446808815002},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ifuzzy55320.2022.9985222","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ifuzzy55320.2022.9985222","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Conference on Fuzzy Theory and Its Applications (iFUZZY)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W2185236817","https://openalex.org/W2200121095","https://openalex.org/W2216013554","https://openalex.org/W2286091602","https://openalex.org/W2603364874","https://openalex.org/W2620473470","https://openalex.org/W2621525431","https://openalex.org/W2739413041","https://openalex.org/W2789255992","https://openalex.org/W3102707396","https://openalex.org/W3109806460"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W4390516098","https://openalex.org/W4249847449","https://openalex.org/W2181948922","https://openalex.org/W2384362569","https://openalex.org/W44395729","https://openalex.org/W2953187864","https://openalex.org/W2888728082","https://openalex.org/W3121197456","https://openalex.org/W3129447544"],"abstract_inverted_index":{"Weeds":[0],"have":[1],"a":[2,9,13,28],"big":[3],"impact":[4],"on":[5,42,106,117],"beet":[6,39],"growth.":[7,24],"As":[8],"result,":[10],"there":[11],"is":[12,48],"growing":[14],"public":[15],"interest":[16],"in":[17,37],"weed":[18],"detection":[19],"and":[20,64,78,100,120,129],"identification":[21],"during":[22],"crop":[23],"This":[25],"paper":[26],"describes":[27],"UNet++":[29,46,101,119,121,135],"method":[30],"with":[31,102,122,136],"deep":[32,103,123,137],"supervision":[33,104,124,138],"for":[34],"detecting":[35],"weeds":[36,144],"sugar":[38],"fields":[40],"trained":[41],"the":[43,54,58,62,72,76,84,88,95,107,114,141],"dataset.":[44],"The":[45],"architecture":[47],"an":[49],"encoder-decoder":[50],"network":[51],"that":[52,113,134],"closes":[53],"semantic":[55],"gap":[56],"between":[57],"feature":[59,73],"maps":[60,74],"of":[61,75,97,143],"encoder":[63,77],"decoder":[65,79],"sub-networks":[66],"through":[67],"redesigned":[68],"skip":[69],"paths.":[70],"When":[71],"networks":[80],"are":[81,125],"semantically":[82],"similar,":[83],"optimizer":[85],"will":[86],"handle":[87],"learning":[89],"task":[90],"more":[91,145],"easily.":[92],"We":[93],"evaluate":[94],"comparison":[96],"UNet,":[98,118],"UNet++,":[99],"architectures":[105],"dataset":[108],"segmentation":[109],"task.":[110],"Experiments":[111],"show":[112],"mIoU":[115],"values":[116],"about":[126],"90.81%,":[127],"92.00%":[128],"92.34%.":[130],"It":[131],"turns":[132],"out":[133],"can":[139],"detect":[140],"location":[142],"effectively.":[146]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
