{"id":"https://openalex.org/W7140690482","doi":"https://doi.org/10.1109/agers67633.2025.11446369","title":"Predicting Rice Leaf Area Index Using Hyperspectral Data, Principal Component Analysis, and Machine Learning","display_name":"Predicting Rice Leaf Area Index Using Hyperspectral Data, Principal Component Analysis, and Machine Learning","publication_year":2025,"publication_date":"2025-12-17","ids":{"openalex":"https://openalex.org/W7140690482","doi":"https://doi.org/10.1109/agers67633.2025.11446369"},"language":"en","primary_location":{"id":"doi:10.1109/agers67633.2025.11446369","doi":"https://doi.org/10.1109/agers67633.2025.11446369","is_oa":false,"landing_page_url":"https://doi.org/10.1109/agers67633.2025.11446369","pdf_url":null,"source":{"id":"https://openalex.org/S7407086493","display_name":"IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"2025 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5057959695","display_name":"Loreniel E. A\u00f1onuevo","orcid":"https://orcid.org/0000-0001-5472-6366","observed_orcids":["https://orcid.org/0000-0001-5472-6366"]},"institutions":[{"id":"https://openalex.org/I210563421","display_name":"Caraga State University","ror":"https://ror.org/00erb5q72","country_code":"PH","type":"education","lineage":["https://openalex.org/I210563421"]}],"countries":["PH"],"is_corresponding":false,"raw_author_name":"Loreniel E. Anonuevo","raw_affiliation_strings":["Caraga State University,Mathematical and Statistical Computing Center,Butuan City,Philippines"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Caraga State University,Mathematical and Statistical Computing Center,Butuan City,Philippines","institution_ids":["https://openalex.org/I210563421"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092614236","display_name":"John Riz V. Bagnol","orcid":"https://orcid.org/0009-0004-9952-4310","observed_orcids":["https://orcid.org/0009-0004-9952-4310"]},"institutions":[{"id":"https://openalex.org/I102516824","display_name":"Universit\u00e9 de Technologie de Compi\u00e8gne","ror":"https://ror.org/04y5kwa70","country_code":"FR","type":"education","lineage":["https://openalex.org/I102516824"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"John Riz V. Bagnol","raw_affiliation_strings":["Compi\u00e8gne Universit\u00e9 de Technologie de Compi\u00e8gne,Laboratoire de Math\u00e9matiques Appliqu\u00e9es de,Compi\u00e8gne,France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Compi\u00e8gne Universit\u00e9 de Technologie de Compi\u00e8gne,Laboratoire de Math\u00e9matiques Appliqu\u00e9es de,Compi\u00e8gne,France","institution_ids":["https://openalex.org/I102516824"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121195067","display_name":"Jay Melvin B. Segales","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I210563421","display_name":"Caraga State University","ror":"https://ror.org/00erb5q72","country_code":"PH","type":"education","lineage":["https://openalex.org/I210563421"]}],"countries":["PH"],"is_corresponding":false,"raw_author_name":"Jay Melvin B. Segales","raw_affiliation_strings":["Caraga State University,Caraga Center for Geo informatics,Butuan City,Philippines"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Caraga State University,Caraga Center for Geo informatics,Butuan City,Philippines","institution_ids":["https://openalex.org/I210563421"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004868437","display_name":"Arnaldo C. Gagula","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I210563421","display_name":"Caraga State University","ror":"https://ror.org/00erb5q72","country_code":"PH","type":"education","lineage":["https://openalex.org/I210563421"]}],"countries":["PH"],"is_corresponding":false,"raw_author_name":"Arnaldo C. Gagula","raw_affiliation_strings":["Caraga State University,Caraga Center for Geo informatics,Butuan City,Philippines"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Caraga State University,Caraga Center for Geo informatics,Butuan City,Philippines","institution_ids":["https://openalex.org/I210563421"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009116664","display_name":"Roger T. Sarmiento","orcid":"https://orcid.org/0000-0002-1856-0773","observed_orcids":["https://orcid.org/0000-0002-1856-0773"]},"institutions":[{"id":"https://openalex.org/I210563421","display_name":"Caraga State University","ror":"https://ror.org/00erb5q72","country_code":"PH","type":"education","lineage":["https://openalex.org/I210563421"]}],"countries":["PH"],"is_corresponding":false,"raw_author_name":"Roger T. Sarmiento","raw_affiliation_strings":["Caraga State University,College of Forestry and Environmental Science,Butuan City,Philippines"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Caraga State University,College of Forestry and Environmental Science,Butuan City,Philippines","institution_ids":["https://openalex.org/I210563421"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040227795","display_name":"Jayrold P. Arcede","orcid":"https://orcid.org/0000-0003-0528-7832","observed_orcids":["https://orcid.org/0000-0003-0528-7832"]},"institutions":[{"id":"https://openalex.org/I210563421","display_name":"Caraga State University","ror":"https://ror.org/00erb5q72","country_code":"PH","type":"education","lineage":["https://openalex.org/I210563421"]}],"countries":["PH"],"is_corresponding":false,"raw_author_name":"Jayrold P. Arcede","raw_affiliation_strings":["Caraga State University,College of Mathematics and Natural Sciences,Butuan City,Philippines"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Caraga State University,College of Mathematics and Natural Sciences,Butuan City,Philippines","institution_ids":["https://openalex.org/I210563421"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"78","last_page":"84"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9434000253677368,"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"}},"topics":[{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9434000253677368,"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/T14365","display_name":"Leaf Properties and Growth Measurement","score":0.02070000022649765,"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/T10616","display_name":"Smart Agriculture and AI","score":0.015399999916553497,"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/leaf-area-index","display_name":"leaf area index","score":0.8220000267028809},{"id":"https://openalex.org/keywords/rice","display_name":"rice","score":0.9079999923706055},{"id":"https://openalex.org/keywords/hyperspectral-remote-sensing","display_name":"hyperspectral remote sensing","score":0.8270000219345093},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"principal component analysis","score":0.8320000171661377},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"dimensionality reduction","score":0.9919999837875366},{"id":"https://openalex.org/keywords/xgboost","display_name":"XGBoost","score":0.8640000224113464},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.7639999985694885},{"id":"https://openalex.org/keywords/support-vector-regression","display_name":"support vector regression","score":0.9959999918937683},{"id":"https://openalex.org/keywords/neural-network","display_name":"artificial neural networks","score":0.984000027179718},{"id":"https://openalex.org/keywords/non-destructive-crop-monitoring","display_name":"non-destructive crop monitoring","score":0.8019999861717224},{"id":"https://openalex.org/keywords/paddy-rice","display_name":"paddy rice","score":0.921999990940094}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8044999837875366},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.76910001039505},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6092000007629395},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5449000000953674},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.48420000076293945},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.4478999972343445},{"id":"https://openalex.org/C2777382242","wikidata":"https://www.wikidata.org/wiki/Q6017816","display_name":"Index (typography)","level":2,"score":0.4009999930858612},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.39250001311302185},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37770000100135803},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3379000127315521},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.302700012922287},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2856999933719635},{"id":"https://openalex.org/C2780376076","wikidata":"https://www.wikidata.org/wiki/Q1499458","display_name":"Vegetation Index","level":4,"score":0.2662999927997589},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.26030001044273376},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2515999972820282}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/agers67633.2025.11446369","doi":"https://doi.org/10.1109/agers67633.2025.11446369","is_oa":false,"landing_page_url":"https://doi.org/10.1109/agers67633.2025.11446369","pdf_url":null,"source":{"id":"https://openalex.org/S7407086493","display_name":"IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"2025 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/2","score":0.8409000039100647,"display_name":"Zero hunger"}],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320337504","display_name":"Research and Development","ror":"https://ror.org/027s68j25"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1970664916","https://openalex.org/W2063907334","https://openalex.org/W2089468765","https://openalex.org/W2145669224","https://openalex.org/W2295598076","https://openalex.org/W2983376237","https://openalex.org/W3096801664","https://openalex.org/W3117450682","https://openalex.org/W3177303668","https://openalex.org/W4292548578","https://openalex.org/W4400762160","https://openalex.org/W4404809312","https://openalex.org/W4410492275"],"related_works":["https://openalex.org/W4414623824","https://openalex.org/W4410845191","https://openalex.org/W2902773593","https://openalex.org/W4282552411","https://openalex.org/W7161642596","https://openalex.org/W2092678346","https://openalex.org/W4405737905","https://openalex.org/W4416074308","https://openalex.org/W2008190217","https://openalex.org/W4414217753"],"abstract_inverted_index":{"Accurate":[0],"and":[1,17,30,89,102,127,149],"timely":[2],"estimation":[3],"of":[4,51,64,155,166],"Leaf":[5],"Area":[6],"Index":[7],"(LAI)":[8],"is":[9],"crucial":[10],"for":[11,56,69,135,158,174],"monitoring":[12],"crop":[13],"growth,":[14],"predicting":[15,92],"yield,":[16],"optimizing":[18],"agricultural":[19,177],"management":[20],"practices.":[21],"Traditional":[22],"LAI":[23,94,136,159],"measurement":[24],"methods":[25],"are":[26],"often":[27],"destructive,":[28],"time-consuming,":[29],"labor-intensive.":[31],"Remote":[32],"sensing,":[33],"particularly":[34],"hyperspectral":[35,52,96],"data,":[36],"offers":[37],"a":[38,105],"promising":[39],"alternative":[40],"due":[41],"to":[42],"its":[43],"rich":[44],"spectral":[45],"information.":[46],"However,":[47],"the":[48,62,142,153,164],"high":[49],"dimensionality":[50,70,122,168],"data":[53,121],"poses":[54],"challenges":[55],"model":[57],"development.":[58],"This":[59,161],"study":[60],"investigates":[61],"effectiveness":[63],"Principal":[65],"Component":[66],"Analysis":[67],"(PCA)":[68],"reduction":[71,169],"combined":[72],"with":[73,145,170],"various":[74],"machine":[75,172],"learning":[76,173],"(ML)":[77],"algorithms-Artificial":[78],"Neural":[79],"Networks":[80],"(ANN),":[81],"Support":[82],"Vector":[83],"Regressor":[84,140],"(SVR),":[85],"Random":[86],"Forest":[87],"(RF),":[88],"XGBoost":[90,139],"Regressor-for":[91],"rice":[93],"from":[95,108],"data.":[97],"The":[98,138],"models":[99,131],"were":[100],"trained":[101],"evaluated":[103],"using":[104],"dataset":[106],"derived":[107],"multi-year":[109],"experiments":[110],"on":[111],"paddy":[112],"rice.":[113],"Our":[114],"results":[115],"demonstrate":[116],"that":[117],"PCA":[118],"effectively":[119],"reduces":[120],"while":[123],"retaining":[124],"essential":[125],"information,":[126],"all":[128],"tested":[129],"ML":[130],"show":[132],"strong":[133],"potential":[134],"prediction.":[137],"achieved":[141],"best":[143],"performance":[144],"$\\mathrm{R}^{2}=0.7326$,":[146],"MSE":[147],"$=0.5629$,":[148],"RMSE":[150],"$=0.7503$,":[151],"highlighting":[152],"robustness":[154],"ensemble-based":[156],"approaches":[157],"estimation.":[160],"research":[162],"underscores":[163],"value":[165],"integrating":[167],"advanced":[171],"accurate,":[175],"non-destructive":[176],"monitoring.":[178]},"counts_by_year":[],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2026-03-27T00:00:00"}
