{"id":"https://openalex.org/W4403942018","doi":"https://doi.org/10.3390/electronics13214273","title":"A Temporal\u2013Geospatial Deep Learning Framework for Crop Yield Prediction","display_name":"A Temporal\u2013Geospatial Deep Learning Framework for Crop Yield Prediction","publication_year":2024,"publication_date":"2024-10-31","ids":{"openalex":"https://openalex.org/W4403942018","doi":"https://doi.org/10.3390/electronics13214273"},"language":"en","primary_location":{"id":"doi:10.3390/electronics13214273","doi":"https://doi.org/10.3390/electronics13214273","is_oa":true,"landing_page_url":"https://doi.org/10.3390/electronics13214273","pdf_url":"https://www.mdpi.com/2079-9292/13/21/4273/pdf?version=1730362932","source":{"id":"https://openalex.org/S4210202905","display_name":"Electronics","issn_l":"2079-9292","issn":["2079-9292"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","norway-1","poland-100","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Electronics","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2079-9292/13/21/4273/pdf?version=1730362932","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100435978","display_name":"Lei Wang","orcid":"https://orcid.org/0000-0002-6340-6222","observed_orcids":["https://orcid.org/0000-0002-6340-6222"]},"institutions":[{"id":"https://openalex.org/I1328775524","display_name":"Zhejiang Sci-Tech University","ror":"https://ror.org/03893we55","country_code":"CN","type":"education","lineage":["https://openalex.org/I1328775524"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Wang","raw_affiliation_strings":["School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China","institution_ids":["https://openalex.org/I1328775524"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhengkui Chen","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I1328775524","display_name":"Zhejiang Sci-Tech University","ror":"https://ror.org/03893we55","country_code":"CN","type":"education","lineage":["https://openalex.org/I1328775524"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhengkui Chen","raw_affiliation_strings":["School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China","institution_ids":["https://openalex.org/I1328775524"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023220578","display_name":"Weichun Liu","orcid":"https://orcid.org/0000-0001-6264-1020","observed_orcids":["https://orcid.org/0000-0001-6264-1020"]},"institutions":[{"id":"https://openalex.org/I1328775524","display_name":"Zhejiang Sci-Tech University","ror":"https://ror.org/03893we55","country_code":"CN","type":"education","lineage":["https://openalex.org/I1328775524"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weichun Liu","raw_affiliation_strings":["School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China","institution_ids":["https://openalex.org/I1328775524"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103240674","display_name":"Hai Huang","orcid":"https://orcid.org/0000-0003-3843-9767","observed_orcids":["https://orcid.org/0000-0003-3843-9767"]},"institutions":[{"id":"https://openalex.org/I1328775524","display_name":"Zhejiang Sci-Tech University","ror":"https://ror.org/03893we55","country_code":"CN","type":"education","lineage":["https://openalex.org/I1328775524"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hai Huang","raw_affiliation_strings":["School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China","institution_ids":["https://openalex.org/I1328775524"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5103240674"],"corresponding_institution_ids":["https://openalex.org/I1328775524"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2728},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2728},"fwci":4.3562,"has_fulltext":true,"cited_by_count":21,"citation_normalized_percentile":{"value":0.95067278,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"13","issue":"21","first_page":"4273","last_page":"4273"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9980000257492065,"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":0.9980000257492065,"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":0.0008999999845400453,"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/T10439","display_name":"Climate change impacts on agriculture","score":0.0006000000284984708,"subfield":{"id":"https://openalex.org/subfields/1102","display_name":"Agronomy and Crop 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/crop-yield-prediction","display_name":"crop yield prediction","score":1.0},{"id":"https://openalex.org/keywords/graph-attention-networks","display_name":"graph attention networks","score":0.7680000066757202},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.8669999837875366},{"id":"https://openalex.org/keywords/long-short-term-memory","display_name":"long short-term memory","score":0.9750000238418579},{"id":"https://openalex.org/keywords/spatiotemporal-dependencies","display_name":"spatiotemporal dependencies","score":0.7300000190734863},{"id":"https://openalex.org/keywords/soybean-yield","display_name":"soybean yield","score":0.7129999995231628},{"id":"https://openalex.org/keywords/meteorological-observations","display_name":"meteorological observations","score":0.7910000085830688},{"id":"https://openalex.org/keywords/digital-agriculture","display_name":"digital agriculture","score":0.7450000047683716},{"id":"https://openalex.org/keywords/hybrid-deep-learning","display_name":"hybrid deep learning model","score":0.8429999947547913}],"concepts":[{"id":"https://openalex.org/C9770341","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Geospatial analysis","level":2,"score":0.8966118097305298},{"id":"https://openalex.org/C134121241","wikidata":"https://www.wikidata.org/wiki/Q899301","display_name":"Yield (engineering)","level":2,"score":0.6596612930297852},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.599676787853241},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.509681224822998},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48981496691703796},{"id":"https://openalex.org/C137580998","wikidata":"https://www.wikidata.org/wiki/Q235352","display_name":"Crop","level":2,"score":0.4368076026439667},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4210321605205536},{"id":"https://openalex.org/C88463610","wikidata":"https://www.wikidata.org/wiki/Q194118","display_name":"Agricultural engineering","level":1,"score":0.33312326669692993},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3329516649246216},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3205157518386841},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.26696842908859253},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.22178694605827332},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2124459147453308},{"id":"https://openalex.org/C97137747","wikidata":"https://www.wikidata.org/wiki/Q38112","display_name":"Forestry","level":1,"score":0.09923824667930603},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.08823558688163757},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3390/electronics13214273","doi":"https://doi.org/10.3390/electronics13214273","is_oa":true,"landing_page_url":"https://doi.org/10.3390/electronics13214273","pdf_url":"https://www.mdpi.com/2079-9292/13/21/4273/pdf?version=1730362932","source":{"id":"https://openalex.org/S4210202905","display_name":"Electronics","issn_l":"2079-9292","issn":["2079-9292"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","norway-1","poland-100","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Electronics","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.3390/electronics13214273","doi":"https://doi.org/10.3390/electronics13214273","is_oa":true,"landing_page_url":"https://doi.org/10.3390/electronics13214273","pdf_url":"https://www.mdpi.com/2079-9292/13/21/4273/pdf?version=1730362932","source":{"id":"https://openalex.org/S4210202905","display_name":"Electronics","issn_l":"2079-9292","issn":["2079-9292"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","norway-1","poland-100","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Electronics","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/2","score":0.958299994468689,"display_name":"Zero hunger"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4403942018.pdf","grobid_xml":"https://content.openalex.org/works/W4403942018.grobid-xml"},"referenced_works_count":45,"referenced_works":["https://openalex.org/W1501856433","https://openalex.org/W2064675550","https://openalex.org/W2116341502","https://openalex.org/W2147800946","https://openalex.org/W2532936747","https://openalex.org/W2547315340","https://openalex.org/W2604645045","https://openalex.org/W2624431344","https://openalex.org/W2766453196","https://openalex.org/W2884526666","https://openalex.org/W2896457183","https://openalex.org/W2911287026","https://openalex.org/W2945600159","https://openalex.org/W2947902784","https://openalex.org/W2949642792","https://openalex.org/W2966681365","https://openalex.org/W2982418982","https://openalex.org/W2991004173","https://openalex.org/W2997552745","https://openalex.org/W3048168082","https://openalex.org/W3168997536","https://openalex.org/W3171970564","https://openalex.org/W3213239025","https://openalex.org/W4251579854","https://openalex.org/W4283210400","https://openalex.org/W4300818068","https://openalex.org/W4306385517","https://openalex.org/W4310082067","https://openalex.org/W4311079602","https://openalex.org/W4313292882","https://openalex.org/W4313459197","https://openalex.org/W4313584940","https://openalex.org/W4316690637","https://openalex.org/W4322740724","https://openalex.org/W4367850923","https://openalex.org/W4377970673","https://openalex.org/W4378192053","https://openalex.org/W4379386207","https://openalex.org/W4386981095","https://openalex.org/W4387251191","https://openalex.org/W4387260826","https://openalex.org/W4392528582","https://openalex.org/W4397293659","https://openalex.org/W4399126389","https://openalex.org/W4401628765"],"related_works":["https://openalex.org/W7108923604","https://openalex.org/W3213239025","https://openalex.org/W4386261545","https://openalex.org/W4385272276","https://openalex.org/W4408933000","https://openalex.org/W7221056199","https://openalex.org/W2997552745","https://openalex.org/W7127608319","https://openalex.org/W4409994021","https://openalex.org/W7106246066"],"abstract_inverted_index":{"With":[0],"the":[1,7,35,59,160,163,177,184,197,212,219,242,246,254,268,284,292],"rapid":[2],"development":[3],"of":[4,58,221,256,267],"information":[5,91,199],"technology,":[6],"demand":[8],"for":[9,38,82,166],"digital":[10,63],"agriculture":[11],"is":[12,117,156,173,193],"increasing.":[13],"As":[14,42],"an":[15],"important":[16],"agricultural":[17],"production":[18],"topic,":[19],"crop":[20,39,53,70,97],"yield":[21,40,54,71,98,222],"has":[22,33,55,73],"always":[23],"attracted":[24],"much":[25],"attention.":[26],"Currently,":[27],"artificial":[28],"intelligence,":[29],"particularly":[30],"machine":[31,47],"learning,":[32],"become":[34,56],"leading":[36],"approach":[37],"prediction.":[41,223],"a":[43,46,74,100,106,111,128,228],"result,":[44],"developing":[45],"learning":[48,132,207],"method":[49],"that":[50,134,231,283],"accurately":[51],"predicts":[52],"one":[57],"central":[60],"challenges":[61],"in":[62,169,238,241],"agriculture.":[64],"Unlike":[65],"traditional":[66],"regression":[67],"prediction":[68,72,152],"problems,":[69],"significant":[75,288],"time":[76],"correlation.":[77],"For":[78,103],"example,":[79,104],"weather":[80],"data":[81,165,233],"each":[83,167,170],"county":[84,116,168],"show":[85],"strong":[86],"temporal":[87,198,213],"correlations.":[88],"Moreover,":[89],"geographic":[90],"from":[92,162,234,248],"different":[93],"regions":[94],"also":[95],"impacts":[96],"to":[99,119,150,158,175,186,195,250],"certain":[101],"extent.":[102],"if":[105],"county\u2019s":[107],"neighboring":[108,181],"counties":[109,237],"have":[110,120],"good":[112],"harvest,":[113],"then":[114],"this":[115],"likely":[118],"high":[121],"yields":[122],"as":[123],"well.":[124],"This":[125],"paper":[126],"introduces":[127],"novel":[129],"hybrid":[130,205],"deep":[131,206],"framework":[133,208],"combines":[135],"convolutional":[136],"neural":[137],"network":[138,142],"(CNN),":[139],"graph":[140],"attention":[141],"(GAT)":[143],"and":[144,214,275,305],"long":[145],"short-term":[146],"memory":[147],"(LSTM)":[148],"modules":[149],"enhance":[151],"accuracy.":[153],"Specifically,":[154],"CNN":[155],"employed":[157],"extract":[159,196],"features":[161],"input":[164],"year.":[171],"GAT":[172],"introduced":[174],"model":[176,185,260,286],"geographical":[178],"relationships":[179],"between":[180],"counties,":[182],"allowing":[183],"capture":[187],"spatial":[188,215],"dependencies":[189],"more":[190],"effectively.":[191],"LSTM":[192],"used":[194],"within":[200],"many":[201],"years.":[202],"The":[203,279],"proposed":[204,258,285],"CNN-GAT-LSTM":[209,259],"captures":[210],"both":[211],"relationships,":[216],"thereby":[217],"improving":[218],"accuracy":[220],"We":[224,252],"conduct":[225],"experiments":[226],"on":[227,262],"nationwide":[229],"dataset":[230],"includes":[232],"1115":[235],"soybean-producing":[236],"13":[239],"states":[240],"United":[243],"States":[244],"covering":[245],"years":[247],"1980":[249],"2018.":[251],"evaluate":[253],"performance":[255,289],"our":[257],"based":[261],"three":[263],"metrics,":[264],"namely":[265],"root":[266],"mean":[269],"squared":[270],"error":[271],"(RMSE),":[272],"R-squared":[273],"(R2)":[274],"correlation":[276],"coefficient":[277],"(Corr).":[278],"experimental":[280],"results":[281],"demonstrate":[282],"achieves":[287],"improvements":[290],"over":[291],"existing":[293],"state-of-the-art":[294],"model,":[295],"with":[296],"RMSE":[297],"reduced":[298],"by":[299,303,308],"5%,":[300],"R2":[301],"improved":[302],"6%":[304],"Corr":[306],"enhanced":[307],"4%.":[309]},"counts_by_year":[{"year":2026,"cited_by_count":11},{"year":2025,"cited_by_count":10}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2024-11-01T00:00:00"}
