{"id":"https://openalex.org/W1982602113","doi":"https://doi.org/10.1109/whispers.2011.6080851","title":"Application of airborne hyperspectral imagery to estimating fruit yield in citrus","display_name":"Application of airborne hyperspectral imagery to estimating fruit yield in citrus","publication_year":2011,"publication_date":"2011-06-01","ids":{"openalex":"https://openalex.org/W1982602113","doi":"https://doi.org/10.1109/whispers.2011.6080851","mag":"1982602113"},"language":"en","primary_location":{"id":"doi:10.1109/whispers.2011.6080851","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2011.6080851","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","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/A5002160639","display_name":"Xujun Ye","orcid":"https://orcid.org/0000-0001-8409-2202"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]},{"id":"https://openalex.org/I92614990","display_name":"Tokyo University of Agriculture and Technology","ror":"https://ror.org/00qg0kr10","country_code":"JP","type":"education","lineage":["https://openalex.org/I92614990"]}],"countries":["CN","JP"],"is_corresponding":true,"raw_author_name":"Xujun Ye","raw_affiliation_strings":["Faculty of Agriculture, Tokyo University of Agriculture and Technology, Tokyo, Japan","School of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Faculty of Agriculture, Tokyo University of Agriculture and Technology, Tokyo, Japan","institution_ids":["https://openalex.org/I92614990"]},{"raw_affiliation_string":"School of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006872706","display_name":"Kenshi Sakai","orcid":"https://orcid.org/0000-0002-7887-1709"},"institutions":[{"id":"https://openalex.org/I92614990","display_name":"Tokyo University of Agriculture and Technology","ror":"https://ror.org/00qg0kr10","country_code":"JP","type":"education","lineage":["https://openalex.org/I92614990"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kenshi Sakai","raw_affiliation_strings":["Faculty of Agriculture, Tokyo University of Agriculture and Technology, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Faculty of Agriculture, Tokyo University of Agriculture and Technology, Tokyo, Japan","institution_ids":["https://openalex.org/I92614990"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5002160639"],"corresponding_institution_ids":["https://openalex.org/I76130692","https://openalex.org/I92614990"],"apc_list":null,"apc_paid":null,"fwci":0.6195,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.7211703,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9987000226974487,"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":0.9987000226974487,"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/T11796","display_name":"Horticultural and Viticultural Research","score":0.9904000163078308,"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/T14365","display_name":"Leaf Properties and Growth Measurement","score":0.989799976348877,"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/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.9518575668334961},{"id":"https://openalex.org/keywords/orchard","display_name":"Orchard","score":0.6982567310333252},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.690801739692688},{"id":"https://openalex.org/keywords/canopy","display_name":"Canopy","score":0.5848497152328491},{"id":"https://openalex.org/keywords/chemometrics","display_name":"Chemometrics","score":0.5586056709289551},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.5492774844169617},{"id":"https://openalex.org/keywords/multispectral-image","display_name":"Multispectral image","score":0.5453057885169983},{"id":"https://openalex.org/keywords/vegetation","display_name":"Vegetation (pathology)","score":0.5451903939247131},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.5364044308662415},{"id":"https://openalex.org/keywords/partial-least-squares-regression","display_name":"Partial least squares regression","score":0.4910392761230469},{"id":"https://openalex.org/keywords/imaging-spectrometer","display_name":"Imaging spectrometer","score":0.48357006907463074},{"id":"https://openalex.org/keywords/yield","display_name":"Yield (engineering)","score":0.4582921266555786},{"id":"https://openalex.org/keywords/tree-canopy","display_name":"Tree canopy","score":0.4348528981208801},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.43149083852767944},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3399476706981659},{"id":"https://openalex.org/keywords/spectrometer","display_name":"Spectrometer","score":0.31643980741500854},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.31038933992385864},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.21683132648468018},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.20590835809707642},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.197590172290802},{"id":"https://openalex.org/keywords/horticulture","display_name":"Horticulture","score":0.1804773211479187},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.09981083869934082},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.0856209397315979}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.9518575668334961},{"id":"https://openalex.org/C2780753983","wikidata":"https://www.wikidata.org/wiki/Q236371","display_name":"Orchard","level":2,"score":0.6982567310333252},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.690801739692688},{"id":"https://openalex.org/C101000010","wikidata":"https://www.wikidata.org/wiki/Q5033434","display_name":"Canopy","level":2,"score":0.5848497152328491},{"id":"https://openalex.org/C151304367","wikidata":"https://www.wikidata.org/wiki/Q910067","display_name":"Chemometrics","level":2,"score":0.5586056709289551},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.5492774844169617},{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.5453057885169983},{"id":"https://openalex.org/C2776133958","wikidata":"https://www.wikidata.org/wiki/Q7918366","display_name":"Vegetation (pathology)","level":2,"score":0.5451903939247131},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.5364044308662415},{"id":"https://openalex.org/C22354355","wikidata":"https://www.wikidata.org/wiki/Q422009","display_name":"Partial least squares regression","level":2,"score":0.4910392761230469},{"id":"https://openalex.org/C183852935","wikidata":"https://www.wikidata.org/wiki/Q6002848","display_name":"Imaging spectrometer","level":3,"score":0.48357006907463074},{"id":"https://openalex.org/C134121241","wikidata":"https://www.wikidata.org/wiki/Q899301","display_name":"Yield (engineering)","level":2,"score":0.4582921266555786},{"id":"https://openalex.org/C39807119","wikidata":"https://www.wikidata.org/wiki/Q1134228","display_name":"Tree canopy","level":3,"score":0.4348528981208801},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.43149083852767944},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3399476706981659},{"id":"https://openalex.org/C33390570","wikidata":"https://www.wikidata.org/wiki/Q188463","display_name":"Spectrometer","level":2,"score":0.31643980741500854},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.31038933992385864},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.21683132648468018},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.20590835809707642},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.197590172290802},{"id":"https://openalex.org/C144027150","wikidata":"https://www.wikidata.org/wiki/Q48803","display_name":"Horticulture","level":1,"score":0.1804773211479187},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.09981083869934082},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0856209397315979},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"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},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/whispers.2011.6080851","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2011.6080851","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land","score":0.5099999904632568}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321106","display_name":"Ministry of Education of the People's Republic of China","ror":"https://ror.org/01mv9t934"},{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W120340242","https://openalex.org/W2006401135","https://openalex.org/W2027249140","https://openalex.org/W2040827997","https://openalex.org/W2051888483","https://openalex.org/W2062057049","https://openalex.org/W2067069641","https://openalex.org/W2122628053","https://openalex.org/W2138695073","https://openalex.org/W2509399225","https://openalex.org/W6604840175","https://openalex.org/W6663825396","https://openalex.org/W6678539466"],"related_works":["https://openalex.org/W2043468524","https://openalex.org/W2360864709","https://openalex.org/W2046566088","https://openalex.org/W2546782759","https://openalex.org/W4389640747","https://openalex.org/W3135916123","https://openalex.org/W23916517","https://openalex.org/W4296822844","https://openalex.org/W2955227278","https://openalex.org/W2237152834"],"abstract_inverted_index":{"This":[0],"study":[1,134],"investigated":[2],"the":[3,10,67,79,98,125,136,144],"applicability":[4],"of":[5,12,52,124,127,138,168],"airborne":[6,139],"hyperspectral":[7,140],"imagery":[8,141],"to":[9,28,142],"estimation":[11],"fruit":[13,145],"yield":[14,80,146],"in":[15,19,37,147],"citrus.":[16,148],"Hyperspectral":[17],"images":[18],"72":[20],"visible":[21],"and":[22,78,106,111,161,173],"near-infrared":[23],"(NIR)":[24],"wavelengths":[25,65],"(from":[26],"407":[27],"898":[29],"nm)":[30],"were":[31,55,76,83],"acquired":[32,68],"over":[33],"a":[34],"citrus":[35],"orchard":[36],"Japan":[38],"by":[39,119],"an":[40],"Airborne":[41],"Imaging":[42],"Spectrometer":[43],"for":[44,155,165],"Applications":[45],"(AISA)":[46],"Eagle":[47],"system.":[48],"The":[49],"canopy":[50],"features":[51],"individual":[53,74],"trees":[54,75],"identified":[56],"using":[57,85],"pixel-based":[58],"average":[59],"spectral":[60],"reflectance":[61],"values":[62],"at":[63],"various":[64],"from":[66],"images.":[69],"Fruit":[70],"yields":[71],"on":[72],"48":[73],"recorded":[77],"prediction":[81,87,128],"models":[82],"developed":[84],"different":[86,131],"variables":[88],"-":[89],"(i)":[90],"several":[91],"commonly":[92],"used":[93],"vegetation":[94,103],"indices":[95],"(VIs),":[96],"(ii)":[97],"newly":[99],"derived":[100],"two":[101],"band":[102],"index":[104],"(TBVI)":[105],"(iii)":[107],"principal":[108],"components":[109],"(PCs)":[110],"partial":[112],"least":[113],"square":[114],"regression":[115],"(PLS)":[116],"factors":[117],"obtained":[118],"chemometrics":[120],"analysis.":[121],"In":[122],"spite":[123],"variations":[126],"accuracies":[129],"among":[130],"models,":[132],"this":[133],"confirmed":[135],"potential":[137],"predict":[143],"Yield":[149],"estimates":[150],"can":[151],"provide":[152],"valuable":[153],"information":[154],"forecasting":[156],"yields,":[157],"planning":[158],"harvest":[159],"schedules":[160],"generating":[162],"prescription":[163],"maps":[164],"tree-specific":[166],"application":[167],"alternate":[169],"bearing":[170],"control":[171],"measures":[172],"other":[174],"management":[175],"practices.":[176]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":1}],"updated_date":"2026-03-25T13:04:00.132906","created_date":"2025-10-10T00:00:00"}