{"id":"https://openalex.org/W3200017662","doi":"https://doi.org/10.1109/agro-geoinformatics50104.2021.9530356","title":"Machine Learning-based Pre-season Crop Type Mapping: A Comparative Study","display_name":"Machine Learning-based Pre-season Crop Type Mapping: A Comparative Study","publication_year":2021,"publication_date":"2021-07-26","ids":{"openalex":"https://openalex.org/W3200017662","doi":"https://doi.org/10.1109/agro-geoinformatics50104.2021.9530356","mag":"3200017662"},"language":"en","primary_location":{"id":"doi:10.1109/agro-geoinformatics50104.2021.9530356","is_oa":false,"landing_page_url":"https://doi.org/10.1109/agro-geoinformatics50104.2021.9530356","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 9th 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/A5088574472","display_name":"Alexander Yao","orcid":"https://orcid.org/0000-0002-9143-8942"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Alexander Yao","raw_affiliation_strings":["Thomas Jefferson High School for Science and Technology, Alexandria, 6560 Braddock Road, Alexandria, USA"],"affiliations":[{"raw_affiliation_string":"Thomas Jefferson High School for Science and Technology, Alexandria, 6560 Braddock Road, Alexandria, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012116347","display_name":"Liping Di","orcid":"https://orcid.org/0000-0002-3953-9965"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Liping Di","raw_affiliation_strings":["George Mason University, Fairfax, USA"],"affiliations":[{"raw_affiliation_string":"George Mason University, Fairfax, USA","institution_ids":["https://openalex.org/I162714631"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5088574472"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.5319,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.8472887,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":99},"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":0.9923999905586243,"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":0.9923999905586243,"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.9778000116348267,"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/T12045","display_name":"Rice Cultivation and Yield Improvement","score":0.9103000164031982,"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/machine-learning","display_name":"Machine learning","score":0.7373164892196655},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6218228340148926},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5894339084625244},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.575148344039917},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5742975473403931},{"id":"https://openalex.org/keywords/cropping","display_name":"Cropping","score":0.5733585953712463},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.5727384090423584},{"id":"https://openalex.org/keywords/agriculture","display_name":"Agriculture","score":0.5303448438644409},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4683228135108948},{"id":"https://openalex.org/keywords/growing-season","display_name":"Growing season","score":0.44741690158843994},{"id":"https://openalex.org/keywords/crop","display_name":"Crop","score":0.4380183517932892},{"id":"https://openalex.org/keywords/crop-yield","display_name":"Crop yield","score":0.42679864168167114},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.42463240027427673},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.41815364360809326},{"id":"https://openalex.org/keywords/agricultural-engineering","display_name":"Agricultural engineering","score":0.37643933296203613},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.19932764768600464},{"id":"https://openalex.org/keywords/agronomy","display_name":"Agronomy","score":0.14483720064163208},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09367862343788147},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08779698610305786},{"id":"https://openalex.org/keywords/forestry","display_name":"Forestry","score":0.0702960193157196}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7373164892196655},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6218228340148926},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5894339084625244},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.575148344039917},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5742975473403931},{"id":"https://openalex.org/C13558536","wikidata":"https://www.wikidata.org/wiki/Q785116","display_name":"Cropping","level":3,"score":0.5733585953712463},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.5727384090423584},{"id":"https://openalex.org/C118518473","wikidata":"https://www.wikidata.org/wiki/Q11451","display_name":"Agriculture","level":2,"score":0.5303448438644409},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4683228135108948},{"id":"https://openalex.org/C137660486","wikidata":"https://www.wikidata.org/wiki/Q732240","display_name":"Growing season","level":2,"score":0.44741690158843994},{"id":"https://openalex.org/C137580998","wikidata":"https://www.wikidata.org/wiki/Q235352","display_name":"Crop","level":2,"score":0.4380183517932892},{"id":"https://openalex.org/C126343540","wikidata":"https://www.wikidata.org/wiki/Q889514","display_name":"Crop yield","level":2,"score":0.42679864168167114},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.42463240027427673},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.41815364360809326},{"id":"https://openalex.org/C88463610","wikidata":"https://www.wikidata.org/wiki/Q194118","display_name":"Agricultural engineering","level":1,"score":0.37643933296203613},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.19932764768600464},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.14483720064163208},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09367862343788147},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08779698610305786},{"id":"https://openalex.org/C97137747","wikidata":"https://www.wikidata.org/wiki/Q38112","display_name":"Forestry","level":1,"score":0.0702960193157196},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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-geoinformatics50104.2021.9530356","is_oa":false,"landing_page_url":"https://doi.org/10.1109/agro-geoinformatics50104.2021.9530356","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 9th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5600000023841858,"display_name":"Zero hunger","id":"https://metadata.un.org/sdg/2"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2074632501","https://openalex.org/W2416782259","https://openalex.org/W2782450437","https://openalex.org/W2810601729","https://openalex.org/W2903343514","https://openalex.org/W2909543378","https://openalex.org/W2909728654","https://openalex.org/W2946413603","https://openalex.org/W2981691152","https://openalex.org/W3046918937","https://openalex.org/W3081044359","https://openalex.org/W3082547400","https://openalex.org/W3182299891","https://openalex.org/W6764271870","https://openalex.org/W6798361187"],"related_works":["https://openalex.org/W4296081764","https://openalex.org/W3100297620","https://openalex.org/W4319718059","https://openalex.org/W4382701299","https://openalex.org/W4212956667","https://openalex.org/W4385728794","https://openalex.org/W3210696866","https://openalex.org/W3204641204","https://openalex.org/W4313165475","https://openalex.org/W4293069612"],"abstract_inverted_index":{"Reliable":[0],"crop":[1,12,23,48,59,126],"type":[2,13,49,60],"information":[3,14],"is":[4],"crucial":[5],"for":[6,29,80],"decision-making":[7],"in":[8,98,128],"agriculture.":[9],"However,":[10],"post-season":[11],"cannot":[15],"support":[16],"in-season":[17],"estimation":[18],"and":[19,32,39,45,74,131],"monitoring.":[20],"Instead,":[21],"pre-season":[22,47,125],"mapping":[24],"can":[25],"provide":[26],"early":[27],"warnings":[28],"agricultural":[30],"yield":[31],"supply":[33],"chains":[34],"to":[35],"reduce":[36],"trade":[37],"tension":[38],"agriculture":[40],"risk.":[41],"This":[42],"research":[43],"analyzed":[44],"predicted":[46],"maps":[50,127],"using":[51],"multiple":[52,110],"machine":[53,65,88,111],"learning":[54,66,89,112],"algorithms":[55,67,113,118],"based":[56],"on":[57],"historical":[58],"data.":[61],"We":[62],"evaluated":[63],"three":[64],"including":[68],"Random":[69,120],"Forest,":[70],"Extreme":[71],"Gradient":[72],"Boosting,":[73],"Na\u00efve":[75],"Bayes":[76],"as":[77],"prediction":[78],"models":[79],"experimental":[81],"development.":[82],"The":[83,106],"results":[84],"show":[85],"that":[86,115],"the":[87],"algorithm":[90],"with":[91],"a":[92],"large":[93],"dataset":[94],"has":[95],"higher":[96],"accuracy":[97],"complex":[99,117],"cropping":[100,104],"patterns":[101],"than":[102],"simple":[103],"patterns.":[105],"comparative":[107],"study":[108],"of":[109],"shows":[114],"more":[116],"like":[119],"Forest":[121],"could":[122],"produce":[123],"reasonable":[124],"an":[129],"efficient":[130],"low-cost":[132],"way.":[133]},"counts_by_year":[{"year":2023,"cited_by_count":7}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
