{"id":"https://openalex.org/W2895935128","doi":"https://doi.org/10.1109/agro-geoinformatics.2018.8476059","title":"Estimating Wheat Coverage Using Multispectral Images Collected by Unmanned Aerial Vehicles and a New Sensor","display_name":"Estimating Wheat Coverage Using Multispectral Images Collected by Unmanned Aerial Vehicles and a New Sensor","publication_year":2018,"publication_date":"2018-08-01","ids":{"openalex":"https://openalex.org/W2895935128","doi":"https://doi.org/10.1109/agro-geoinformatics.2018.8476059","mag":"2895935128"},"language":"en","primary_location":{"id":"doi:10.1109/agro-geoinformatics.2018.8476059","is_oa":false,"landing_page_url":"https://doi.org/10.1109/agro-geoinformatics.2018.8476059","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5054985228","display_name":"Jinran Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210160793","display_name":"Institute of Geographic Sciences and Natural Resources Research","ror":"https://ror.org/04t1cdb72","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210160793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinran Liu","raw_affiliation_strings":["Chinese Academy of Sciences, Institute of Geographical Science and Natural Resources Research, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, Institute of Geographical Science and Natural Resources Research, Beijing, China","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210160793"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100335049","display_name":"Pengfei Chen","orcid":"https://orcid.org/0000-0001-8591-1206"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210160793","display_name":"Institute of Geographic Sciences and Natural Resources Research","ror":"https://ror.org/04t1cdb72","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210160793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengfei Chen","raw_affiliation_strings":["Chinese Academy of Sciences, Institute of Geographical Science and Natural Resources Research, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, Institute of Geographical Science and Natural Resources Research, Beijing, China","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210160793"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070651286","display_name":"Xingang Xu","orcid":"https://orcid.org/0000-0002-8473-5631"},"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":"Xingang Xu","raw_affiliation_strings":["National Engineering Research, Center for Information Technology in Agriculture, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Engineering Research, Center for Information Technology in Agriculture, Beijing, China","institution_ids":["https://openalex.org/I4210156423"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"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":0.9972000122070312,"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":0.9972000122070312,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9718999862670898,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9688000082969666,"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/multispectral-image","display_name":"Multispectral image","score":0.8828718662261963},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.6645934581756592},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.5983158349990845},{"id":"https://openalex.org/keywords/precision-agriculture","display_name":"Precision agriculture","score":0.5854856371879578},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5171858072280884},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.3420845866203308},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3301438093185425},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.278138667345047},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.11817362904548645},{"id":"https://openalex.org/keywords/agriculture","display_name":"Agriculture","score":0.11671808362007141}],"concepts":[{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.8828718662261963},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.6645934581756592},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5983158349990845},{"id":"https://openalex.org/C120217122","wikidata":"https://www.wikidata.org/wiki/Q740083","display_name":"Precision agriculture","level":3,"score":0.5854856371879578},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5171858072280884},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.3420845866203308},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3301438093185425},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.278138667345047},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.11817362904548645},{"id":"https://openalex.org/C118518473","wikidata":"https://www.wikidata.org/wiki/Q11451","display_name":"Agriculture","level":2,"score":0.11671808362007141},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/agro-geoinformatics.2018.8476059","is_oa":false,"landing_page_url":"https://doi.org/10.1109/agro-geoinformatics.2018.8476059","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Zero hunger","score":0.7699999809265137,"id":"https://metadata.un.org/sdg/2"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1572510186","https://openalex.org/W1978788419","https://openalex.org/W1995711721","https://openalex.org/W2009029295","https://openalex.org/W2012686349","https://openalex.org/W2056352756","https://openalex.org/W2087839810","https://openalex.org/W2116242877","https://openalex.org/W2161815745","https://openalex.org/W2166516660","https://openalex.org/W2167787089","https://openalex.org/W2167869331","https://openalex.org/W2367319125","https://openalex.org/W2552742159","https://openalex.org/W2626521779","https://openalex.org/W2765127940","https://openalex.org/W2775538150","https://openalex.org/W2794104823","https://openalex.org/W2802121422","https://openalex.org/W2988471827","https://openalex.org/W4231956273","https://openalex.org/W4385178990","https://openalex.org/W6745451609","https://openalex.org/W6746590077"],"related_works":["https://openalex.org/W4318664220","https://openalex.org/W2771047279","https://openalex.org/W4388409104","https://openalex.org/W1544811710","https://openalex.org/W2124951708","https://openalex.org/W172072032","https://openalex.org/W2006066416","https://openalex.org/W3157073418","https://openalex.org/W2039041387","https://openalex.org/W2139294397"],"abstract_inverted_index":{"Coverage":[0],"is":[1,67,78],"an":[2],"important":[3],"parameter":[4],"for":[5,31,157,223,248],"indicating":[6],"wheat":[7,18,81,107,126,135,192,252,264],"growth":[8,129],"and":[9,23,40,52,94,228,274,293,298,307,309,315,325,332,334,340],"health.":[10],"Remote":[11],"sensing":[12],"technology":[13],"has":[14],"utility":[15],"in":[16,20,69,117,127,140,250],"monitoring":[17],"coverage":[19,82,136,193,224,233],"a":[21,27,88,95,102,232],"timely":[22],"nondestructive":[24],"manner":[25],"over":[26],"given":[28],"spatial":[29,51],"scale":[30],"precision":[32],"agriculture.":[33],"Unmanned":[34],"aerial":[35],"vehicles":[36],"(UAVs)":[37],"are":[38,221,260],"flexible":[39],"can":[41,46],"easily":[42],"be":[43],"manipulated.":[44],"They":[45],"acquire":[47],"images":[48,85,124,241],"with":[49,60,87],"high":[50],"temporal":[53],"resolutions":[54],"at":[55,109,137],"low":[56],"cost":[57],"when":[58],"equipped":[59],"sensors.":[61],"However,":[62],"the":[63,110,151,158,165,170,191,197,201,211,256,269,281,286,291,294,301,310,320,327,335],"application":[64],"of":[65,75,125,255],"UAVs":[66],"still":[68],"its":[70],"initial":[71],"phases.":[72],"The":[73,236],"objective":[74],"this":[76,100,214],"study":[77],"to":[79,189,209,230,263],"estimate":[80],"using":[83,243,290],"multispectral":[84,240],"obtained":[86,166,242,289],"low-cost":[89],"UAV":[90],"sensor":[91],"named":[92],"RedEdge-M":[93,244],"four-rotor":[96],"UAV.":[97],"To":[98],"meet":[99],"goal,":[101],"nitrogen":[103],"fertilization":[104],"experiment":[105],"on":[106,150],"conducted":[108],"Xiaotangshan":[111],"National":[112],"Precision":[113],"Agriculture":[114],"Experimental":[115],"Base":[116],"Changping":[118],"district,":[119],"Beijing,":[120],"was":[121],"used.":[122],"Multispectral":[123],"Feekes":[128],"stage":[130],"4":[131],"were":[132,143,161,173,187,207,226,296,305,313,323,330,338],"obtained.":[133],"Additionally,":[134],"representative":[138],"points":[139,160,185,205],"each":[141],"plot":[142],"measured":[144],"by":[145],"traditional":[146],"photographic":[147],"methods.":[148],"Based":[149],"data":[152,156,172],"described":[153],"above,":[154],"spectral":[155,218,258],"sampling":[159,171,184,204],"first":[162],"extracted":[163],"from":[164],"RedEdge":[167],"images.":[168],"Second,":[169],"divided":[174],"into":[175],"two":[176],"parts.":[177],"One":[178],"part":[179,199],"contained":[180,200],"24":[181],"randomly":[182],"selected":[183,227,257],"that":[186,206,220,239],"used":[188,208,217,229],"design":[190],"estimation":[194,234],"model,":[195],"whereas":[196],"other":[198],"remaining":[202],"8":[203],"test":[210],"model.":[212,235],"During":[213,284,317],"process,":[215],"commonly":[216],"indices":[219,259],"suitable":[222],"prediction":[225],"produce":[231],"results":[237],"showed":[238],"have":[245],"great":[246],"potential":[247],"use":[249],"estimating":[251],"coverage.":[253,265],"All":[254],"closely":[261],"related":[262],"Of":[266],"these":[267],"indices,":[268],"Triangular":[270],"Vegetation":[271],"Index":[272],"(TVI)":[273],"Normalized":[275],"Difference":[276],"Red":[277],"Edge":[278],"(NDRE)":[279],"displayed":[280],"best":[282],"performance.":[283],"calibration,":[285],"R2":[287,321],"values":[288,304,312,322,329,337],"TVI":[292],"NDRE":[295],"0.96":[297],"0.97,":[299],"respectively;":[300],"corresponding":[302],"RMSE":[303,328],"1.56%":[306],"1.50,":[308],"RMSE%":[311,336],"8.91":[314],"8.55.":[316],"model":[318],"validation,":[319],"0.90":[324],"0.90,":[326],"3.11%":[331],"3.31%,":[333],"16.96":[339],"18.05,":[341],"respectively.":[342]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}
