{"id":"https://openalex.org/W4386361192","doi":"https://doi.org/10.1109/agro-geoinformatics59224.2023.10233473","title":"Early Crop Classification Based on Historical Annual Crop Inventory Data and Remote Sensing Data","display_name":"Early Crop Classification Based on Historical Annual Crop Inventory Data and Remote Sensing Data","publication_year":2023,"publication_date":"2023-07-25","ids":{"openalex":"https://openalex.org/W4386361192","doi":"https://doi.org/10.1109/agro-geoinformatics59224.2023.10233473"},"language":"en","primary_location":{"id":"doi:10.1109/agro-geoinformatics59224.2023.10233473","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/agro-geoinformatics59224.2023.10233473","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 11th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","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/A5104098870","display_name":"Yue Wu","orcid":"https://orcid.org/0009-0004-2428-0905"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yue Wu","raw_affiliation_strings":["Sun Yat-Sen University,School of Geospatial Engineering and Science,Zhuhai,Guangdong,China","School of Geospatial Engineering and Science, Sun Yat-Sen University, Zhuhai, Guangdong, China"],"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University,School of Geospatial Engineering and Science,Zhuhai,Guangdong,China","institution_ids":["https://openalex.org/I157773358"]},{"raw_affiliation_string":"School of Geospatial Engineering and Science, Sun Yat-Sen University, Zhuhai, Guangdong, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021411883","display_name":"Chunhua Liao","orcid":"https://orcid.org/0000-0002-5504-206X"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunhua Liao","raw_affiliation_strings":["Sun Yat-Sen University,School of Geospatial Engineering and Science,Zhuhai,Guangdong,China","School of Geospatial Engineering and Science, Sun Yat-Sen University, Zhuhai, Guangdong, China"],"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University,School of Geospatial Engineering and Science,Zhuhai,Guangdong,China","institution_ids":["https://openalex.org/I157773358"]},{"raw_affiliation_string":"School of Geospatial Engineering and Science, Sun Yat-Sen University, Zhuhai, Guangdong, China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5104098870"],"corresponding_institution_ids":["https://openalex.org/I157773358"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12404364,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"27","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9993000030517578,"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"}},"topics":[{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9993000030517578,"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"}},{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9968000054359436,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9800000190734863,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"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/crop","display_name":"Crop","score":0.6218044757843018},{"id":"https://openalex.org/keywords/agriculture","display_name":"Agriculture","score":0.6072654128074646},{"id":"https://openalex.org/keywords/food-security","display_name":"Food security","score":0.5301253199577332},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.5272616147994995},{"id":"https://openalex.org/keywords/transferability","display_name":"Transferability","score":0.47428256273269653},{"id":"https://openalex.org/keywords/growing-season","display_name":"Growing season","score":0.4519214630126953},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4172601103782654},{"id":"https://openalex.org/keywords/agricultural-engineering","display_name":"Agricultural engineering","score":0.3980867266654968},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3590976595878601},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.2511543035507202},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.23260128498077393},{"id":"https://openalex.org/keywords/agronomy","display_name":"Agronomy","score":0.12809377908706665},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.120382159948349},{"id":"https://openalex.org/keywords/forestry","display_name":"Forestry","score":0.0861501693725586}],"concepts":[{"id":"https://openalex.org/C137580998","wikidata":"https://www.wikidata.org/wiki/Q235352","display_name":"Crop","level":2,"score":0.6218044757843018},{"id":"https://openalex.org/C118518473","wikidata":"https://www.wikidata.org/wiki/Q11451","display_name":"Agriculture","level":2,"score":0.6072654128074646},{"id":"https://openalex.org/C549605437","wikidata":"https://www.wikidata.org/wiki/Q1229911","display_name":"Food security","level":3,"score":0.5301253199577332},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.5272616147994995},{"id":"https://openalex.org/C61272859","wikidata":"https://www.wikidata.org/wiki/Q7834031","display_name":"Transferability","level":3,"score":0.47428256273269653},{"id":"https://openalex.org/C137660486","wikidata":"https://www.wikidata.org/wiki/Q732240","display_name":"Growing season","level":2,"score":0.4519214630126953},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4172601103782654},{"id":"https://openalex.org/C88463610","wikidata":"https://www.wikidata.org/wiki/Q194118","display_name":"Agricultural engineering","level":1,"score":0.3980867266654968},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3590976595878601},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.2511543035507202},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.23260128498077393},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.12809377908706665},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.120382159948349},{"id":"https://openalex.org/C97137747","wikidata":"https://www.wikidata.org/wiki/Q38112","display_name":"Forestry","level":1,"score":0.0861501693725586},{"id":"https://openalex.org/C140331021","wikidata":"https://www.wikidata.org/wiki/Q1868104","display_name":"Logit","level":2,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/agro-geoinformatics59224.2023.10233473","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/agro-geoinformatics59224.2023.10233473","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 11th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/2","score":0.800000011920929,"display_name":"Zero hunger"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2013388282","https://openalex.org/W2071356948","https://openalex.org/W2148333466","https://openalex.org/W2379453731","https://openalex.org/W2585309444","https://openalex.org/W2971772286","https://openalex.org/W3167962161","https://openalex.org/W3205696749","https://openalex.org/W4223520639","https://openalex.org/W4224297249","https://openalex.org/W4253655398","https://openalex.org/W4292794316","https://openalex.org/W4383570398"],"related_works":["https://openalex.org/W4399895933","https://openalex.org/W2161221533","https://openalex.org/W4229699405","https://openalex.org/W1666484574","https://openalex.org/W2216382288","https://openalex.org/W2355491300","https://openalex.org/W4234629551","https://openalex.org/W2011110943","https://openalex.org/W2028856635","https://openalex.org/W4251615416"],"abstract_inverted_index":{"With":[0],"the":[1,4,35,48,56,82,106,122,136,149,153],"growth":[2],"of":[3,15,38,58,84,130,139,152],"global":[5],"population":[6],"and":[7,32,55,96,148,171],"food":[8,21],"demand,":[9],"near":[10,85,99],"real-time":[11,86],"agriculture":[12],"monitoring":[13],"is":[14,27],"great":[16],"significance":[17],"to":[18,47,79,163],"ensure":[19],"future":[20],"security.":[22],"Early-season":[23],"crop":[24,39,41,73,145,169],"classification":[25,42,83,124],"mapping":[26],"essential":[28],"for":[29,159],"agricultural":[30],"planning":[31],"management.":[33],"At":[34],"early":[36],"stages":[37],"growth,":[40],"faces":[43],"significant":[44],"challenges":[45,165],"due":[46],"difficulty":[49],"in":[50,81,142],"obtaining":[51],"ground":[52],"truth":[53],"data":[54,75,147],"absence":[57],"remote":[59,87],"sensing":[60,88],"images.":[61],"In":[62],"this":[63],"study,":[64],"we":[65],"present":[66],"a":[67],"method":[68,156],"that":[69,105],"effectively":[70],"integrates":[71],"historical":[72,144],"inventory":[74],"into":[76],"model":[77],"construction":[78],"assist":[80],"data.":[89],"We":[90],"conducted":[91],"tests":[92],"on":[93,111],"corn,":[94],"soybeans,":[95],"winter":[97,119],"wheat":[98,120],"London,":[100],"Ontario,":[101],"Canada.":[102],"Results":[103],"show":[104],"overall":[107],"accuracy":[108],"was":[109],"0.84":[110],"June":[112],"10,":[113],"2021.":[114],"Among":[115],"these":[116,133],"three":[117],"crops,":[118],"achieved":[121],"highest":[123],"accuracy,":[125],"with":[126],"an":[127],"F1":[128],"score":[129],"0.95.":[131],"Overall,":[132],"results":[134],"highlight":[135],"unique":[137],"advantages":[138],"our":[140],"approach":[141],"leveraging":[143],"type":[146],"temporal":[150],"transferability":[151],"model.":[154],"This":[155],"holds":[157],"promise":[158],"providing":[160],"new":[161],"solutions":[162],"address":[164],"such":[166],"as":[167],"early-season":[168],"identification":[170],"monitoring.":[172]},"counts_by_year":[],"updated_date":"2025-12-25T23:11:45.687758","created_date":"2025-10-10T00:00:00"}