{"id":"https://openalex.org/W4386352887","doi":"https://doi.org/10.1109/agro-geoinformatics59224.2023.10233533","title":"Estimation of the ratio of leaf carbon to nitrogen in winter wheat based on hyperspectral data and machine learning method","display_name":"Estimation of the ratio of leaf carbon to nitrogen in winter wheat based on hyperspectral data and machine learning method","publication_year":2023,"publication_date":"2023-07-25","ids":{"openalex":"https://openalex.org/W4386352887","doi":"https://doi.org/10.1109/agro-geoinformatics59224.2023.10233533"},"language":"en","primary_location":{"id":"doi:10.1109/agro-geoinformatics59224.2023.10233533","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/agro-geoinformatics59224.2023.10233533","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/A5102144416","display_name":"Chengzhi Fan","orcid":null},"institutions":[{"id":"https://openalex.org/I80143920","display_name":"Shandong University of Science and Technology","ror":"https://ror.org/04gtjhw98","country_code":"CN","type":"education","lineage":["https://openalex.org/I80143920"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chengzhi Fan","raw_affiliation_strings":["Shandong University of Science and Technology,College of Geodesy and Geomatics,Qingdao,China","College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, China"],"affiliations":[{"raw_affiliation_string":"Shandong University of Science and Technology,College of Geodesy and Geomatics,Qingdao,China","institution_ids":["https://openalex.org/I80143920"]},{"raw_affiliation_string":"College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, China","institution_ids":["https://openalex.org/I80143920"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108707104","display_name":"Lixin Gao","orcid":null},"institutions":[{"id":"https://openalex.org/I80143920","display_name":"Shandong University of Science and Technology","ror":"https://ror.org/04gtjhw98","country_code":"CN","type":"education","lineage":["https://openalex.org/I80143920"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lixin Gao","raw_affiliation_strings":["Shandong University of Science and Technology,College of Geodesy and Geomatics,Qingdao,China","College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, China"],"affiliations":[{"raw_affiliation_string":"Shandong University of Science and Technology,College of Geodesy and Geomatics,Qingdao,China","institution_ids":["https://openalex.org/I80143920"]},{"raw_affiliation_string":"College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, China","institution_ids":["https://openalex.org/I80143920"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017381482","display_name":"Zhenhai Li","orcid":"https://orcid.org/0000-0001-9878-3274"},"institutions":[{"id":"https://openalex.org/I80143920","display_name":"Shandong University of Science and Technology","ror":"https://ror.org/04gtjhw98","country_code":"CN","type":"education","lineage":["https://openalex.org/I80143920"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenhai Li","raw_affiliation_strings":["Shandong University of Science and Technology,College of Geodesy and Geomatics,Qingdao,China","College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, China"],"affiliations":[{"raw_affiliation_string":"Shandong University of Science and Technology,College of Geodesy and Geomatics,Qingdao,China","institution_ids":["https://openalex.org/I80143920"]},{"raw_affiliation_string":"College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, China","institution_ids":["https://openalex.org/I80143920"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100599550","display_name":"Haikuan Feng","orcid":"https://orcid.org/0000-0003-3312-6200"},"institutions":[{"id":"https://openalex.org/I4210156423","display_name":"National Engineering Research Center for Information Technology in Agriculture","ror":"https://ror.org/04c3j3t84","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210156423"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haikuan Feng","raw_affiliation_strings":["Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture Beijing Research Center for Information Technology in Agriculture,Beijing,China","Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture Beijing Research Center for Information Technology in Agriculture, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture Beijing Research Center for Information Technology in Agriculture,Beijing,China","institution_ids":["https://openalex.org/I4210156423"]},{"raw_affiliation_string":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture Beijing Research Center for Information Technology in Agriculture, Beijing, China","institution_ids":["https://openalex.org/I4210156423"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5102144416"],"corresponding_institution_ids":["https://openalex.org/I80143920"],"apc_list":null,"apc_paid":null,"fwci":0.2558,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.6244132,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"8","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":0.9998000264167786,"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":0.9998000264167786,"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/T14365","display_name":"Leaf Properties and Growth Measurement","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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9865000247955322,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/multicollinearity","display_name":"Multicollinearity","score":0.7925819158554077},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.7673947811126709},{"id":"https://openalex.org/keywords/partial-least-squares-regression","display_name":"Partial least squares regression","score":0.7645964622497559},{"id":"https://openalex.org/keywords/winter-wheat","display_name":"Winter wheat","score":0.5250608325004578},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5164890289306641},{"id":"https://openalex.org/keywords/nitrogen","display_name":"Nitrogen","score":0.4806484282016754},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.4790140986442566},{"id":"https://openalex.org/keywords/canopy","display_name":"Canopy","score":0.45574402809143066},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.42101356387138367},{"id":"https://openalex.org/keywords/agronomy","display_name":"Agronomy","score":0.3728410005569458},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.322388231754303},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.27962368726730347},{"id":"https://openalex.org/keywords/botany","display_name":"Botany","score":0.22483551502227783},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.16861870884895325},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.09739997982978821},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09685862064361572}],"concepts":[{"id":"https://openalex.org/C189285262","wikidata":"https://www.wikidata.org/wiki/Q1332350","display_name":"Multicollinearity","level":3,"score":0.7925819158554077},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7673947811126709},{"id":"https://openalex.org/C22354355","wikidata":"https://www.wikidata.org/wiki/Q422009","display_name":"Partial least squares regression","level":2,"score":0.7645964622497559},{"id":"https://openalex.org/C3018661444","wikidata":"https://www.wikidata.org/wiki/Q6977574","display_name":"Winter wheat","level":2,"score":0.5250608325004578},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5164890289306641},{"id":"https://openalex.org/C537208039","wikidata":"https://www.wikidata.org/wiki/Q627","display_name":"Nitrogen","level":2,"score":0.4806484282016754},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4790140986442566},{"id":"https://openalex.org/C101000010","wikidata":"https://www.wikidata.org/wiki/Q5033434","display_name":"Canopy","level":2,"score":0.45574402809143066},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.42101356387138367},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.3728410005569458},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.322388231754303},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.27962368726730347},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.22483551502227783},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.16861870884895325},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.09739997982978821},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09685862064361572},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/agro-geoinformatics59224.2023.10233533","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/agro-geoinformatics59224.2023.10233533","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":[{"score":0.8100000023841858,"id":"https://metadata.un.org/sdg/2","display_name":"Zero hunger"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320324174","display_name":"Natural Science Foundation of Shandong Province","ror":null},{"id":"https://openalex.org/F4320337504","display_name":"Research and Development","ror":"https://ror.org/027s68j25"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1480602009","https://openalex.org/W1973680308","https://openalex.org/W2029567272","https://openalex.org/W2036003376","https://openalex.org/W2056352756","https://openalex.org/W2061063014","https://openalex.org/W2081734510","https://openalex.org/W2085965017","https://openalex.org/W2089464686","https://openalex.org/W2094420085","https://openalex.org/W2095939249","https://openalex.org/W2109006150","https://openalex.org/W2111947859","https://openalex.org/W2118703810","https://openalex.org/W2128438912","https://openalex.org/W2137608957","https://openalex.org/W2157760685","https://openalex.org/W2163410149","https://openalex.org/W2215930302","https://openalex.org/W2355151353","https://openalex.org/W2810662772","https://openalex.org/W2906575954","https://openalex.org/W3146097770","https://openalex.org/W4236621906","https://openalex.org/W4319289039","https://openalex.org/W6688115908","https://openalex.org/W6707098547","https://openalex.org/W6849296793"],"related_works":["https://openalex.org/W2362667440","https://openalex.org/W2381398885","https://openalex.org/W2061931914","https://openalex.org/W2081071406","https://openalex.org/W2279502141","https://openalex.org/W2566756418","https://openalex.org/W2393850233","https://openalex.org/W3012357114","https://openalex.org/W1968919190","https://openalex.org/W2049219354"],"abstract_inverted_index":{"Plant":[0],"C/N":[1,23,81,120,179],"ratio,":[2],"the":[3,19,54,61,115,125,156,177,189],"ratio":[4],"of":[5,22,29,36,39,50,57,82,119,127,139,158,166,180,195],"carbon":[6,190],"to":[7,79,186],"nitrogen,":[8],"is":[9,49,184],"an":[10],"important":[11],"factor":[12],"in":[13,60,121],"agriculture":[14],"and":[15,26,33,47,71,109,153,160,191,202],"ecology,":[16],"especially":[17],"for":[18,53],"comprehensive":[20],"diagnosis":[21],"balance,":[24],"growth":[25,59,200],"nutritional":[27],"status":[28,194],"wheat":[30,84,123,197],"plants.":[31],"Rapid":[32],"accurate":[34],"monitoring":[35],"spectral":[37,88,104],"characteristics":[38],"crop":[40,58],"canopy":[41],"by":[42,86,163],"remote":[43,173],"sensing":[44,174],"technology,":[45],"fast":[46],"non-destructive,":[48],"great":[51],"significance":[52],"dynamic":[55],"regulation":[56],"field.":[62],"In":[63,169],"this":[64,170],"study,":[65,171],"partial":[66],"least":[67],"squares":[68],"regression":[69,74],"(PLSR)":[70],"random":[72,164],"forest":[73],"(RF)":[75],"models":[76,118,162],"were":[77],"constructed":[78],"estimate":[80],"winter":[83,122,181,196],"leaves":[85],"optimizing":[87],"variables.":[89],"Results":[90],"showed":[91],"that":[92,138],"(1)":[93],"successive":[94],"projections":[95],"algorithm":[96,98],"(SPA)":[97],"combined":[99],"with":[100,106,114],"correlation":[101],"analysis":[102],"selected":[103],"variables":[105],"good":[107],"sensitivity":[108],"weak":[110],"multicollinearity;":[111],"(2)":[112],"Compared":[113],"two":[116],"inversion":[117],"leaves,":[124],"accuracy":[126,152],"PLSR":[128,148,159],"model":[129,142,149],"$\\left(R^{2}=0.65,":[130],"R":[131],"M":[132],"S":[133],"E=1.97\\right)$":[134],"was":[135],"higher":[136],"than":[137],"$R":[140],"F$":[141],"$\\left(R^{2}=0.63\\right.$,":[143],"RMSE":[144],"$\\left.=2.02\\right)$;":[145],"(3)":[146],"The":[147],"has":[150,203],"well":[151],"stability":[154],"through":[155],"cross-validation":[157],"RF":[161],"combination":[165],"multi-year":[167],"samples.":[168],"hyperspectral":[172],"accurately":[175],"estimated":[176],"leaf":[178],"wheat,":[182],"which":[183],"helpful":[185],"further":[187],"study":[188],"nitrogen":[192],"metabolism":[193],"at":[198],"different":[199],"stages,":[201],"greater":[204],"exploration":[205],"potential.":[206]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-21T01:58:51.020947","created_date":"2025-10-10T00:00:00"}
