{"id":"https://openalex.org/W4400908837","doi":"https://doi.org/10.1109/siu61531.2024.10601157","title":"Machine Learning-based Crop Yield Prediction by Data Augmentation","display_name":"Machine Learning-based Crop Yield Prediction by Data Augmentation","publication_year":2024,"publication_date":"2024-05-15","ids":{"openalex":"https://openalex.org/W4400908837","doi":"https://doi.org/10.1109/siu61531.2024.10601157"},"language":"en","primary_location":{"id":"doi:10.1109/siu61531.2024.10601157","is_oa":false,"landing_page_url":"https://doi.org/10.1109/siu61531.2024.10601157","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 32nd Signal Processing and Communications Applications Conference (SIU)","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/A5104987238","display_name":"Alper Balmumcu","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Alper Balmumcu","raw_affiliation_strings":["Gebze Teknik &#x00DC;niversitesi,Elektronik M&#x00FC;hendisli&#x011F;i B&#x00F6;l&#x00FC;m&#x00FC;"],"affiliations":[{"raw_affiliation_string":"Gebze Teknik &#x00DC;niversitesi,Elektronik M&#x00FC;hendisli&#x011F;i B&#x00F6;l&#x00FC;m&#x00FC;","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054230085","display_name":"Koray Kayabol","orcid":"https://orcid.org/0000-0003-0053-2800"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Koray Kayabol","raw_affiliation_strings":["Gebze Teknik &#x00DC;niversitesi,Elektronik M&#x00FC;hendisli&#x011F;i B&#x00F6;l&#x00FC;m&#x00FC;"],"affiliations":[{"raw_affiliation_string":"Gebze Teknik &#x00DC;niversitesi,Elektronik M&#x00FC;hendisli&#x011F;i B&#x00F6;l&#x00FC;m&#x00FC;","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003388985","display_name":"Esra Erten","orcid":"https://orcid.org/0000-0002-4208-7170"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Esra Erten","raw_affiliation_strings":["Gebze Teknik &#x00DC;niversitesi,Elektronik M&#x00FC;hendisli&#x011F;i B&#x00F6;l&#x00FC;m&#x00FC;"],"affiliations":[{"raw_affiliation_string":"Gebze Teknik &#x00DC;niversitesi,Elektronik M&#x00FC;hendisli&#x011F;i B&#x00F6;l&#x00FC;m&#x00FC;","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5104987238"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.3001,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.83945718,"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":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9036999940872192,"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":0.9036999940872192,"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/yield","display_name":"Yield (engineering)","score":0.6393134593963623},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6225359439849854},{"id":"https://openalex.org/keywords/crop","display_name":"Crop","score":0.5031325221061707},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.49507418274879456},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47195592522621155},{"id":"https://openalex.org/keywords/agricultural-engineering","display_name":"Agricultural engineering","score":0.4607393145561218},{"id":"https://openalex.org/keywords/crop-yield","display_name":"Crop yield","score":0.42152905464172363},{"id":"https://openalex.org/keywords/agronomy","display_name":"Agronomy","score":0.17837205529212952},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1402730643749237},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.09030866622924805}],"concepts":[{"id":"https://openalex.org/C134121241","wikidata":"https://www.wikidata.org/wiki/Q899301","display_name":"Yield (engineering)","level":2,"score":0.6393134593963623},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6225359439849854},{"id":"https://openalex.org/C137580998","wikidata":"https://www.wikidata.org/wiki/Q235352","display_name":"Crop","level":2,"score":0.5031325221061707},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49507418274879456},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47195592522621155},{"id":"https://openalex.org/C88463610","wikidata":"https://www.wikidata.org/wiki/Q194118","display_name":"Agricultural engineering","level":1,"score":0.4607393145561218},{"id":"https://openalex.org/C126343540","wikidata":"https://www.wikidata.org/wiki/Q889514","display_name":"Crop yield","level":2,"score":0.42152905464172363},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.17837205529212952},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1402730643749237},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.09030866622924805},{"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/siu61531.2024.10601157","is_oa":false,"landing_page_url":"https://doi.org/10.1109/siu61531.2024.10601157","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 32nd Signal Processing and Communications Applications Conference (SIU)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/2","display_name":"Zero hunger","score":0.7699999809265137}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2018149064","https://openalex.org/W2002738406","https://openalex.org/W2985080412","https://openalex.org/W3006201793","https://openalex.org/W4378566980","https://openalex.org/W139018289","https://openalex.org/W2065828020","https://openalex.org/W2380001790","https://openalex.org/W2515171211","https://openalex.org/W1979405749"],"abstract_inverted_index":{"In":[0],"this":[1,90],"study,":[2],"the":[3,47,71,78,92,107],"effects":[4],"of":[5,95,104],"dynamic":[6,118,123],"climate":[7,52],"and":[8,11,40,63,87,116,124,133],"biophysical":[9,56],"parameters":[10,14,126],"static":[12,125],"soil":[13,64,114,131],"obtained":[15],"from":[16,51],"earth":[17],"observation":[18],"satellites":[19],"on":[20],"cotton":[21,75,96],"yield":[22,76,97],"estimation":[23],"were":[24,99,127],"examined":[25],"with":[26,77],"four":[27],"different":[28],"machine":[29],"learning":[30],"algorithms;":[31],"multilayer":[32],"perceptrons,":[33],"long":[34],"short":[35],"term":[36],"memory,":[37],"quantile":[38],"regression":[39],"extreme":[41],"gradient":[42],"boosting":[43],"(XGBoost).":[44],"According":[45],"to":[46,89],"feature":[48],"space":[49],"created":[50],"(temperature,":[53],"precipitation,":[54],"etc.),":[55],"(leaf":[57],"area":[58],"index,":[59,61],"vegetation":[60],"etc.)":[62,69],"(sand":[65],"ratio,":[66],"water":[67],"permeability,":[68],"parameters,":[70,115],"XGBoost":[72],"approach":[73],"predicted":[74],"highest":[79],"accuracy.":[80],"By":[81],"applying":[82],"Shapley":[83],"Additive":[84],"Global":[85],"Importance":[86],"SHAP":[88],"model,":[91],"driving":[93],"factors":[94],"prediction":[98],"analyzed.":[100],"As":[101],"a":[102],"result":[103],"these":[105],"analyses,":[106],"model":[108],"explains":[109],"32%":[110],"static,":[111],"that":[112],"is,":[113],"68%":[117],"parameters.":[119],"The":[120],"most":[121],"important":[122],"determined":[128],"as":[129],"surface":[130],"moisture":[132],"clay.":[134]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2025-12-26T23:08:49.675405","created_date":"2025-10-10T00:00:00"}
