{"id":"https://openalex.org/W4396879087","doi":"https://doi.org/10.1145/3647444.3647833","title":"Evaluation of an Ensemble Technique for Prediction of Crop Yield","display_name":"Evaluation of an Ensemble Technique for Prediction of Crop Yield","publication_year":2023,"publication_date":"2023-11-23","ids":{"openalex":"https://openalex.org/W4396879087","doi":"https://doi.org/10.1145/3647444.3647833"},"language":"en","primary_location":{"id":"doi:10.1145/3647444.3647833","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3647444.3647833","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th International Conference on Information Management &amp; Machine Intelligence","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/A5007110786","display_name":"K. Sankareswari","orcid":null},"institutions":[{"id":"https://openalex.org/I13268429","display_name":"Madurai Kamaraj University","ror":"https://ror.org/04c8e9019","country_code":"IN","type":"education","lineage":["https://openalex.org/I13268429"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Sankareswari K","raw_affiliation_strings":["PG and Research Department of Computer Science, Sri Meenakshi Govt. Arts College for Women(A), Affiliated to Madurai Kamaraj University, India and \rDepartment of Computer Science, The American College, India"],"affiliations":[{"raw_affiliation_string":"PG and Research Department of Computer Science, Sri Meenakshi Govt. Arts College for Women(A), Affiliated to Madurai Kamaraj University, India and \rDepartment of Computer Science, The American College, India","institution_ids":["https://openalex.org/I13268429"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026811448","display_name":"G. Sujatha","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sujatha G","raw_affiliation_strings":["PG and Research Department of Computer Science, Sri Meenakshi Government Arts College for Women (A), India"],"affiliations":[{"raw_affiliation_string":"PG and Research Department of Computer Science, Sri Meenakshi Government Arts College for Women (A), India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5007110786"],"corresponding_institution_ids":["https://openalex.org/I13268429"],"apc_list":null,"apc_paid":null,"fwci":0.6312,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.72170057,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13220","display_name":"Plant and Fungal Interactions Research","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/1310","display_name":"Endocrinology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T13220","display_name":"Plant and Fungal Interactions Research","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/1310","display_name":"Endocrinology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10494","display_name":"Plant Virus Research Studies","score":0.9509000182151794,"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/T13069","display_name":"Cocoa and Sweet Potato Agronomy","score":0.9003999829292297,"subfield":{"id":"https://openalex.org/subfields/1108","display_name":"Horticulture"},"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/yield","display_name":"Yield (engineering)","score":0.6093391180038452},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5412265062332153},{"id":"https://openalex.org/keywords/crop","display_name":"Crop","score":0.4159046709537506},{"id":"https://openalex.org/keywords/forestry","display_name":"Forestry","score":0.13554957509040833},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.08887585997581482},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.0769558846950531}],"concepts":[{"id":"https://openalex.org/C134121241","wikidata":"https://www.wikidata.org/wiki/Q899301","display_name":"Yield (engineering)","level":2,"score":0.6093391180038452},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5412265062332153},{"id":"https://openalex.org/C137580998","wikidata":"https://www.wikidata.org/wiki/Q235352","display_name":"Crop","level":2,"score":0.4159046709537506},{"id":"https://openalex.org/C97137747","wikidata":"https://www.wikidata.org/wiki/Q38112","display_name":"Forestry","level":1,"score":0.13554957509040833},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.08887585997581482},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0769558846950531},{"id":"https://openalex.org/C191897082","wikidata":"https://www.wikidata.org/wiki/Q11467","display_name":"Metallurgy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3647444.3647833","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3647444.3647833","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th International Conference on Information Management &amp; Machine Intelligence","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":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":2,"referenced_works":["https://openalex.org/W2107561290","https://openalex.org/W4206412188"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278","https://openalex.org/W4395014643"],"abstract_inverted_index":{"Crop":[0],"yield":[1,17,77,94,131,213,332],"prediction":[2,18,78,95,315,319],"plays":[3],"a":[4,58,124,163],"crucial":[5],"role":[6],"in":[7,22,119,298],"agricultural":[8,36,53,63,341],"management":[9,81],"and":[10,26,32,47,79,103,135,146,159,191,205,226,240,256,282,317,337,343],"decision-making":[11],"processes.":[12],"Traditional":[13],"approaches":[14],"to":[15,29,44,67,73,91,109,196,259],"crop":[16,76,80,93,130,176,212,331],"often":[19],"face":[20],"limitations":[21],"terms":[23],"of":[24,35,128,170,272],"accuracy":[25,281,316],"robustness":[27],"due":[28],"the":[30,62,168,230,245,261,287,299,306],"complex":[31],"dynamic":[33],"nature":[34],"systems.":[37],"Machine":[38,55],"learning":[39,56],"is":[40,57,219],"an":[41,151,171],"emerging":[42],"technology":[43],"understand":[45],"practical":[46],"real":[48],"world":[49],"use":[50],"cases":[51],"for":[52,61,174,211,264,329,340],"production.":[54],"supporting":[59],"tool":[60],"production":[64],"which":[65,114],"helps":[66],"make":[68],"decisions":[69],"on":[70,244],"what":[71],"crops":[72],"be":[74,181],"cultivated,":[75],"practices.":[82],"In":[83],"this":[84],"research":[85],"paper,":[86],"we":[87],"propose":[88],"ensemble":[89,152,172,186,271,307,325],"techniques":[90,326],"improve":[92,110,260],"accuracy.":[96],"Ensemble":[97],"methods":[98],"such":[99,183,199],"as":[100,184,200],"bagging,":[101],"boosting,":[102],"stacking":[104],"that":[105,221,270,305,324],"combine":[106],"multiple":[107],"models":[108,158],"their":[111],"predictive":[112],"power":[113],"have":[115],"shown":[116],"promising":[117,328],"results":[118,303],"various":[120],"domains.":[121],"We":[122,149],"collected":[123],"comprehensive":[125],"dataset":[126],"consisting":[127],"historical":[129],"data,":[132,185],"weather":[133],"information,":[134],"soil":[136,203],"characteristics.":[137],"The":[138,234,302],"data":[139],"underwent":[140],"pre-processing":[141],"steps,":[142],"including":[143],"cleaning,":[144],"normalization,":[145],"feature":[147],"engineering.":[148],"developed":[150],"model":[153],"architecture,":[154],"selecting":[155],"appropriate":[156],"base":[157],"training":[160],"them":[161],"using":[162],"validation":[164],"process.":[165],"To":[166],"evaluate":[167],"effectiveness":[169],"technique":[173,308],"predicting":[175],"yield,":[177],"several":[178],"factors":[179],"should":[180],"considered":[182],"composition,":[187],"evaluation":[188],"metrics,":[189],"generalizability":[190],"interpretability.":[192],"Important":[193],"parameters":[194],"related":[195],"climatic":[197],"conditions":[198],"rainfall,":[201],"humidity,":[202],"type":[204],"temperature":[206],"were":[207,296],"taken":[208],"into":[209],"consideration":[210],"prediction.":[214],"From":[215],"literature":[216],"review,":[217],"it":[218],"understanding":[220],"Decision":[222],"Tree,":[223],"Random":[224,238,273],"Forest":[225,239,274],"Neural":[227],"Networks":[228],"are":[229,327],"algorithms":[231,242],"mostly":[232],"used.":[233],"proposed":[235,300],"work":[236],"compared":[237],"Boosting":[241,277],"based":[243],"score":[246,258],"like":[247],"Mean":[248,252,290,293],"Squared":[249,291],"Error":[250,254],"(MSE),":[251],"Absolute":[253,294],"(MAE)":[255],"R2":[257],"weak":[262],"learner":[263],"most":[265,283],"expected":[266,284],"outcome.":[267,285],"Finally":[268],"concluded":[269],"with":[275],"Gradient":[276],"Regressor":[278],"achieved":[279],"more":[280,335],"At":[286],"same":[288],"time,":[289],"Error(MSE),":[292],"Error(MAE)":[295],"smaller":[297],"work.":[301],"demonstrated":[304],"consistently":[309],"outperformed":[310],"individual":[311],"models,":[312],"achieving":[313],"higher":[314],"reducing":[318],"errors.":[320],"Our":[321],"findings":[322],"suggest":[323],"improving":[330],"prediction,":[333],"offering":[334],"robust":[336],"accurate":[338],"insights":[339],"planning":[342],"decision-making.":[344]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
