{"id":"https://openalex.org/W4318147476","doi":"https://doi.org/10.1109/bigdata55660.2022.10020917","title":"A Generalized Multimodal Deep Learning Model for Early Crop Yield Prediction","display_name":"A Generalized Multimodal Deep Learning Model for Early Crop Yield Prediction","publication_year":2022,"publication_date":"2022-12-17","ids":{"openalex":"https://openalex.org/W4318147476","doi":"https://doi.org/10.1109/bigdata55660.2022.10020917"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata55660.2022.10020917","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10020917","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","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/A5040077450","display_name":"Arshveer Kaur","orcid":null},"institutions":[{"id":"https://openalex.org/I74796645","display_name":"Birla Institute of Technology and Science, Pilani","ror":"https://ror.org/001p3jz28","country_code":"IN","type":"education","lineage":["https://openalex.org/I74796645"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Arshveer Kaur","raw_affiliation_strings":["BITS Pilani, Pilani Campus,Department of Computer Science & Information Systems,Rajasthan,India"],"affiliations":[{"raw_affiliation_string":"BITS Pilani, Pilani Campus,Department of Computer Science & Information Systems,Rajasthan,India","institution_ids":["https://openalex.org/I74796645"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027120172","display_name":"Poonam Goyal","orcid":"https://orcid.org/0000-0003-1556-9905"},"institutions":[{"id":"https://openalex.org/I74796645","display_name":"Birla Institute of Technology and Science, Pilani","ror":"https://ror.org/001p3jz28","country_code":"IN","type":"education","lineage":["https://openalex.org/I74796645"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Poonam Goyal","raw_affiliation_strings":["BITS Pilani, Pilani Campus,Department of Computer Science & Information Systems,Rajasthan,India"],"affiliations":[{"raw_affiliation_string":"BITS Pilani, Pilani Campus,Department of Computer Science & Information Systems,Rajasthan,India","institution_ids":["https://openalex.org/I74796645"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064438195","display_name":"Kartik Sharma","orcid":"https://orcid.org/0000-0003-2033-9135"},"institutions":[{"id":"https://openalex.org/I74796645","display_name":"Birla Institute of Technology and Science, Pilani","ror":"https://ror.org/001p3jz28","country_code":"IN","type":"education","lineage":["https://openalex.org/I74796645"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Kartik Sharma","raw_affiliation_strings":["BITS Pilani, Pilani Campus,Department of Computer Science & Information Systems,Rajasthan,India"],"affiliations":[{"raw_affiliation_string":"BITS Pilani, Pilani Campus,Department of Computer Science & Information Systems,Rajasthan,India","institution_ids":["https://openalex.org/I74796645"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076951754","display_name":"Lakshay Sharma","orcid":"https://orcid.org/0000-0002-2803-1170"},"institutions":[{"id":"https://openalex.org/I74796645","display_name":"Birla Institute of Technology and Science, Pilani","ror":"https://ror.org/001p3jz28","country_code":"IN","type":"education","lineage":["https://openalex.org/I74796645"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Lakshay Sharma","raw_affiliation_strings":["BITS Pilani, Pilani Campus,Department of Computer Science & Information Systems,Rajasthan,India"],"affiliations":[{"raw_affiliation_string":"BITS Pilani, Pilani Campus,Department of Computer Science & Information Systems,Rajasthan,India","institution_ids":["https://openalex.org/I74796645"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053105431","display_name":"Navneet Goyal","orcid":"https://orcid.org/0000-0002-0006-8211"},"institutions":[{"id":"https://openalex.org/I74796645","display_name":"Birla Institute of Technology and Science, Pilani","ror":"https://ror.org/001p3jz28","country_code":"IN","type":"education","lineage":["https://openalex.org/I74796645"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Navneet Goyal","raw_affiliation_strings":["BITS Pilani, Pilani Campus,Department of Computer Science & Information Systems,Rajasthan,India"],"affiliations":[{"raw_affiliation_string":"BITS Pilani, Pilani Campus,Department of Computer Science & Information Systems,Rajasthan,India","institution_ids":["https://openalex.org/I74796645"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5040077450"],"corresponding_institution_ids":["https://openalex.org/I74796645"],"apc_list":null,"apc_paid":null,"fwci":2.2503,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.89547782,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1272","last_page":"1279"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9977999925613403,"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":0.9977999925613403,"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":0.9657999873161316,"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"}},{"id":"https://openalex.org/T12093","display_name":"Greenhouse Technology and Climate Control","score":0.957099974155426,"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/deep-learning","display_name":"Deep learning","score":0.5858674645423889},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5127928853034973},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5123863816261292},{"id":"https://openalex.org/keywords/satellite","display_name":"Satellite","score":0.5103816390037537},{"id":"https://openalex.org/keywords/crop-yield","display_name":"Crop yield","score":0.4883110523223877},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.47218218445777893},{"id":"https://openalex.org/keywords/yield","display_name":"Yield (engineering)","score":0.46581271290779114},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45596569776535034},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.43279507756233215},{"id":"https://openalex.org/keywords/agricultural-engineering","display_name":"Agricultural engineering","score":0.4226624071598053},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.18969354033470154},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.1233091950416565},{"id":"https://openalex.org/keywords/agronomy","display_name":"Agronomy","score":0.11272141337394714},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1066516637802124}],"concepts":[{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5858674645423889},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5127928853034973},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5123863816261292},{"id":"https://openalex.org/C19269812","wikidata":"https://www.wikidata.org/wiki/Q26540","display_name":"Satellite","level":2,"score":0.5103816390037537},{"id":"https://openalex.org/C126343540","wikidata":"https://www.wikidata.org/wiki/Q889514","display_name":"Crop yield","level":2,"score":0.4883110523223877},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.47218218445777893},{"id":"https://openalex.org/C134121241","wikidata":"https://www.wikidata.org/wiki/Q899301","display_name":"Yield (engineering)","level":2,"score":0.46581271290779114},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45596569776535034},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.43279507756233215},{"id":"https://openalex.org/C88463610","wikidata":"https://www.wikidata.org/wiki/Q194118","display_name":"Agricultural engineering","level":1,"score":0.4226624071598053},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.18969354033470154},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.1233091950416565},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.11272141337394714},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1066516637802124},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"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:10.1109/bigdata55660.2022.10020917","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10020917","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/2","score":0.8100000023841858,"display_name":"Zero hunger"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W2483888092","https://openalex.org/W2604645045","https://openalex.org/W2891765392","https://openalex.org/W2899043197","https://openalex.org/W2899663614","https://openalex.org/W2949642792","https://openalex.org/W2953686964","https://openalex.org/W2979666105","https://openalex.org/W2987713069","https://openalex.org/W2997051686","https://openalex.org/W3008872739","https://openalex.org/W3081407997","https://openalex.org/W3108655343","https://openalex.org/W3109603638","https://openalex.org/W3135523315","https://openalex.org/W3164809178","https://openalex.org/W3200764579","https://openalex.org/W3205479187","https://openalex.org/W4206136329","https://openalex.org/W4214493726","https://openalex.org/W4220679429","https://openalex.org/W4287812705","https://openalex.org/W6755977528","https://openalex.org/W6776700526","https://openalex.org/W6787312032"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W2731899572","https://openalex.org/W4304166257","https://openalex.org/W4230611425","https://openalex.org/W4294635752","https://openalex.org/W4383066092","https://openalex.org/W3215138031","https://openalex.org/W4327774331","https://openalex.org/W2804383999"],"abstract_inverted_index":{"Early":[0],"crop":[1,37,46,59,107,171],"yield":[2,47,60,108,172],"prediction":[3,61,173],"is":[4,62,103,175],"crucial":[5],"in":[6,127,193,197],"agriculture":[7],"for":[8,35,58,142,152,185],"making":[9],"administrative":[10],"plans":[11],"to":[12,89,104,135,167],"ensure":[13],"food":[14],"security,":[15],"post":[16],"harvest":[17],"management":[18],"and":[19,50,70,76,121,131,189,195,204,230],"distribution":[20],"of":[21,66,100,158],"a":[22,36,128,148],"crop.":[23],"Remote":[24],"sensing":[25],"data":[26,53,88,112,132,140,200],"captured":[27],"using":[28,109,199],"various":[29],"satellites":[30,117],"provide":[31],"reliable":[32],"phenological":[33],"information":[34,157],"through":[38],"surface":[39,71],"reflectance":[40,72],"bands.":[41],"Other":[42],"important":[43],"factors,":[44],"affecting":[45],"include":[48],"meteorological":[49,68],"soil.":[51],"The":[52,98],"which":[54,174],"we":[55],"have":[56,162],"used":[57,124],"multimodal.":[63],"It":[64],"consists":[65],"spatiotemporal":[67],"(numeric)":[69],"bands":[73],"(satellite":[74,80],"image),":[75],"temporally":[77],"static":[78],"soil":[79,159],"image)":[81],"data.":[82,160],"We":[83,123,161,181],"effectively":[84,153],"utilize":[85],"this":[86],"multimodal":[87,93],"develop":[90],"the":[91,101,137,155],"proposed":[92],"deep":[94,144],"learning":[95,126,145],"model,":[96],"CropYieldNet.":[97],"objective":[99],"paper":[102],"accurately":[105],"predict":[106],"high":[110],"resolution":[111],"obtained":[113],"from":[114,201],"recently":[115],"launched":[116],"such":[118],"as":[119,176,178],"Landsat8":[120],"Sentinel-2.":[122],"contrastive":[125],"supervised":[129],"setting":[130],"augmentation":[133],"techniques":[134],"overcome":[136],"limited":[138],"historical":[139],"available":[141],"training":[143],"models.We":[146],"introduce":[147],"depth-level":[149],"selection":[150],"module":[151],"modelling":[154],"depth-variant":[156],"also":[163,219],"modified":[164],"our":[165,183,212],"model":[166,184,213],"perform":[168],"in-season":[169],"(early)":[170],"accurate":[177],"end-season":[179],"prediction.":[180],"evaluate":[182],"two":[186],"crops,":[187],"corn":[188],"soybean,":[190],"on":[191,227],"counties":[192],"US":[194],"districts":[196],"India":[198],"MODIS,":[202],"Landsat8,":[203],"Sentinel-2":[205],"satellites.":[206],"Our":[207,217],"extensive":[208],"experimentation":[209],"show":[210,220],"that":[211,221],"outperforms":[214],"competing":[215],"models.":[216],"experiments":[218],"CropYieldNet":[222],"generalizes":[223],"well":[224],"when":[225],"applied":[226],"different":[228],"crops":[229],"geographies.":[231]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}