{"id":"https://openalex.org/W2929705482","doi":"https://doi.org/10.1080/08839514.2019.1592343","title":"Performance Evaluation of Best Feature Subsets for Crop Yield Prediction Using Machine Learning Algorithms","display_name":"Performance Evaluation of Best Feature Subsets for Crop Yield Prediction Using Machine Learning Algorithms","publication_year":2019,"publication_date":"2019-04-05","ids":{"openalex":"https://openalex.org/W2929705482","doi":"https://doi.org/10.1080/08839514.2019.1592343","mag":"2929705482"},"language":"en","primary_location":{"id":"doi:10.1080/08839514.2019.1592343","is_oa":false,"landing_page_url":"https://doi.org/10.1080/08839514.2019.1592343","pdf_url":null,"source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doaj.org/article/5c787db1c089461b81e35725acaafbf7","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5059623278","display_name":"M. S.","orcid":"https://orcid.org/0009-0004-5734-1268"},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Maya Gopal P. S.","raw_affiliation_strings":["School of Computing Science and Engineering, VIT University, Chennai, India"],"affiliations":[{"raw_affiliation_string":"School of Computing Science and Engineering, VIT University, Chennai, India","institution_ids":["https://openalex.org/I876193797"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055273726","display_name":"R. Bhargavi","orcid":null},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Bhargavi R.","raw_affiliation_strings":["School of Computing Science and Engineering, VIT University, Chennai, India"],"affiliations":[{"raw_affiliation_string":"School of Computing Science and Engineering, VIT University, Chennai, India","institution_ids":["https://openalex.org/I876193797"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5059623278"],"corresponding_institution_ids":["https://openalex.org/I876193797"],"apc_list":{"value":2195,"currency":"USD","value_usd":2195},"apc_paid":null,"fwci":15.7296,"has_fulltext":false,"cited_by_count":160,"citation_normalized_percentile":{"value":0.98967718,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"33","issue":"7","first_page":"621","last_page":"642"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9907000064849854,"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":0.9907000064849854,"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":0.9674999713897705,"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/computer-science","display_name":"Computer science","score":0.7840691804885864},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.7679387331008911},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.7262593507766724},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6578918099403381},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6453925371170044},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6448943614959717},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5789773464202881},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4942217171192169},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.48068293929100037},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4546206593513489},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.45315834879875183},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4243409335613251},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3483898341655731},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.12473747134208679},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1058172881603241}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7840691804885864},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.7679387331008911},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7262593507766724},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6578918099403381},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6453925371170044},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6448943614959717},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5789773464202881},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4942217171192169},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.48068293929100037},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4546206593513489},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.45315834879875183},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4243409335613251},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3483898341655731},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.12473747134208679},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1058172881603241},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1080/08839514.2019.1592343","is_oa":false,"landing_page_url":"https://doi.org/10.1080/08839514.2019.1592343","pdf_url":null,"source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:5c787db1c089461b81e35725acaafbf7","is_oa":true,"landing_page_url":"https://doaj.org/article/5c787db1c089461b81e35725acaafbf7","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Applied Artificial Intelligence, Vol 33, Iss 7, Pp 621-642 (2019)","raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:doaj.org/article:5c787db1c089461b81e35725acaafbf7","is_oa":true,"landing_page_url":"https://doaj.org/article/5c787db1c089461b81e35725acaafbf7","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Applied Artificial Intelligence, Vol 33, Iss 7, Pp 621-642 (2019)","raw_type":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/2","score":0.7699999809265137,"display_name":"Zero hunger"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1588159435","https://openalex.org/W1673734644","https://openalex.org/W1965606617","https://openalex.org/W1982910530","https://openalex.org/W2008056655","https://openalex.org/W2012796776","https://openalex.org/W2017198208","https://openalex.org/W2037995585","https://openalex.org/W2050009461","https://openalex.org/W2073968193","https://openalex.org/W2080010866","https://openalex.org/W2084341220","https://openalex.org/W2116905012","https://openalex.org/W2138632244","https://openalex.org/W2140190241","https://openalex.org/W2145774049","https://openalex.org/W2147713768","https://openalex.org/W2187126867","https://openalex.org/W2202019762","https://openalex.org/W2250636764","https://openalex.org/W2343193908","https://openalex.org/W2523192248","https://openalex.org/W2555658613","https://openalex.org/W2561862420","https://openalex.org/W2580808806","https://openalex.org/W2595327349","https://openalex.org/W2735868225","https://openalex.org/W2738849672","https://openalex.org/W2747485148","https://openalex.org/W2782404771","https://openalex.org/W2790454634","https://openalex.org/W2792605755","https://openalex.org/W2793811319","https://openalex.org/W2797092231","https://openalex.org/W2802707542","https://openalex.org/W2805142011","https://openalex.org/W2808964638","https://openalex.org/W3133173605"],"related_works":["https://openalex.org/W4200112873","https://openalex.org/W2955796858","https://openalex.org/W4224941037","https://openalex.org/W2004826645","https://openalex.org/W3135818052","https://openalex.org/W2048488252","https://openalex.org/W4288365262","https://openalex.org/W2940614149","https://openalex.org/W4289884158","https://openalex.org/W2787485953"],"abstract_inverted_index":{"The":[0,31,64,117],"rapid":[1],"innovations":[2],"and":[3,23,76,97,104],"liberalized":[4],"market":[5],"economy":[6],"in":[7,11,48],"agriculture":[8],"demand":[9],"accuracy":[10,127],"Crop":[12],"Yield":[13],"Prediction":[14],"(CYP).":[15],"In":[16],"accurate":[17,62],"prediction,":[18],"machine":[19],"learning":[20],"(ML)":[21],"algorithms":[22],"the":[24,40,49,57,102,110,114,121,125,137,142],"selected":[25,96],"features":[26,47,60],"play":[27],"a":[28,43],"major":[29],"role.":[30],"performance":[32],"of":[33,42,46,88,92,130],"any":[34],"ML":[35,65],"algorithm":[36,123],"may":[37],"improve":[38],"with":[39],"utilization":[41],"distinct":[44,138],"set":[45],"same":[50,143],"training":[51,144],"dataset.":[52],"This":[53],"research":[54],"work":[55],"evaluates":[56],"most":[58],"needed":[59],"for":[61,82,108,135],"CYP.":[63],"algorithms,":[66],"namely,":[67],"Artificial":[68],"Neural":[69],"Network,":[70],"Support":[71],"Vector":[72],"Regression,":[73],"K-Nearest":[74],"Neighbour":[75],"Random":[77],"Forest":[78],"(RF)":[79],"are":[80,94,98,106],"proposed":[81],"better":[83],"accuracy.":[84],"Agricultural":[85],"dataset":[86],"consists":[87],"745":[89],"instances;":[90],"70%":[91],"data":[93],"randomly":[95],"used":[99,107],"to":[100,112],"train":[101],"model":[103,111],"30%":[105],"testing":[109],"assess":[113],"predictive":[115],"ability.":[116],"results":[118],"show":[119],"that":[120],"RF":[122],"reaches":[124],"highest":[126],"by":[128],"means":[129],"its":[131],"error":[132],"analysis":[133],"values":[134],"all":[136],"feature":[139],"subsets":[140],"using":[141],"agricultural":[145],"data.":[146]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":28},{"year":2024,"cited_by_count":41},{"year":2023,"cited_by_count":32},{"year":2022,"cited_by_count":33},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":8},{"year":2019,"cited_by_count":1}],"updated_date":"2026-04-02T15:55:50.835912","created_date":"2025-10-10T00:00:00"}
