{"id":"https://openalex.org/W4407737736","doi":"https://doi.org/10.1109/whispers65427.2024.10876506","title":"An Interpretable Neural Network for Vegetation Phenotyping with Visualization of Trait-Based Spectral Features","display_name":"An Interpretable Neural Network for Vegetation Phenotyping with Visualization of Trait-Based Spectral Features","publication_year":2024,"publication_date":"2024-12-09","ids":{"openalex":"https://openalex.org/W4407737736","doi":"https://doi.org/10.1109/whispers65427.2024.10876506"},"language":"en","primary_location":{"id":"doi:10.1109/whispers65427.2024.10876506","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers65427.2024.10876506","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 14th Workshop on Hyperspectral Imaging 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/A5066041701","display_name":"William Basener","orcid":"https://orcid.org/0000-0002-8593-2362"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"William Basener","raw_affiliation_strings":["School of Data Science, University of Virginia"],"affiliations":[{"raw_affiliation_string":"School of Data Science, University of Virginia","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049265211","display_name":"Abigail Basener","orcid":"https://orcid.org/0000-0001-8705-7955"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Abigail Basener","raw_affiliation_strings":["College of Computer, Mathematical, and Natural Sciences, University of Marlyand"],"affiliations":[{"raw_affiliation_string":"College of Computer, Mathematical, and Natural Sciences, University of Marlyand","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032281705","display_name":"Michael Luegering","orcid":null},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Michael Luegering","raw_affiliation_strings":["School of Architecture, University of Virginia"],"affiliations":[{"raw_affiliation_string":"School of Architecture, University of Virginia","institution_ids":["https://openalex.org/I51556381"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5066041701"],"corresponding_institution_ids":["https://openalex.org/I51556381"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.2411833,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9247000217437744,"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":0.9247000217437744,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.7588216066360474},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6309183239936829},{"id":"https://openalex.org/keywords/trait","display_name":"Trait","score":0.6175411939620972},{"id":"https://openalex.org/keywords/vegetation","display_name":"Vegetation (pathology)","score":0.5889964699745178},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5888589024543762},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5808656811714172},{"id":"https://openalex.org/keywords/data-visualization","display_name":"Data visualization","score":0.4556139409542084},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4131055474281311},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34596794843673706},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32886528968811035},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.058265119791030884}],"concepts":[{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.7588216066360474},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6309183239936829},{"id":"https://openalex.org/C106934330","wikidata":"https://www.wikidata.org/wiki/Q1971873","display_name":"Trait","level":2,"score":0.6175411939620972},{"id":"https://openalex.org/C2776133958","wikidata":"https://www.wikidata.org/wiki/Q7918366","display_name":"Vegetation (pathology)","level":2,"score":0.5889964699745178},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5888589024543762},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5808656811714172},{"id":"https://openalex.org/C172367668","wikidata":"https://www.wikidata.org/wiki/Q6504956","display_name":"Data visualization","level":3,"score":0.4556139409542084},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4131055474281311},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34596794843673706},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32886528968811035},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.058265119791030884},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/whispers65427.2024.10876506","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers65427.2024.10876506","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4000000059604645,"id":"https://metadata.un.org/sdg/13","display_name":"Climate action"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W2054054246","https://openalex.org/W2086330580","https://openalex.org/W4285815274","https://openalex.org/W4390600657","https://openalex.org/W4391392820","https://openalex.org/W4391761368","https://openalex.org/W4391970145","https://openalex.org/W4398206351"],"related_works":["https://openalex.org/W2013728941","https://openalex.org/W4225274103","https://openalex.org/W2154046714","https://openalex.org/W2189613078","https://openalex.org/W2579659702","https://openalex.org/W2923661510","https://openalex.org/W1574055964","https://openalex.org/W1965329638","https://openalex.org/W2542318691","https://openalex.org/W3160708108"],"abstract_inverted_index":{"Plant":[0],"phenotyping":[1],"is":[2,12,172,236],"the":[3,13,17,35,70,73,86,98,143,149,153,185,192],"assessment":[4],"of":[5,15,85,155,158],"a":[6,109,208,223],"plant's":[7],"traits":[8,82,94,147],"and":[9,22,54,62,80,89,133,180,212,221],"plant":[10,204],"identification":[11,102],"process":[14],"determining":[16],"category":[18],"such":[19],"as":[20,118,164,166,184],"genus":[21],"species.":[23,66],"In":[24],"this":[25,195],"paper":[26,196],"we":[27,90,181],"present":[28,160],"an":[29,104],"interpretable":[30],"neural":[31,113,214,227],"network":[32,74,87,99],"trained":[33],"on":[34,108,216],"UPWINS":[36,186],"spectral":[37,76,187,217],"library":[38,188],"which":[39,161],"contains":[40],"spectra":[41],"with":[42,103,229],"rich":[43],"metadata":[44],"across":[45],"variation":[46],"in":[47,72,129,177,194,202,219],"species,":[48],"health,":[49],"growth":[50],"stage,":[51],"annual":[52],"variation,":[53],"environmental":[55],"conditions":[56],"for":[57,78,100,151,210,225,232],"13":[58],"selected":[59],"indicator":[60],"species":[61,101,163],"natural":[63],"common":[64],"background":[65],"We":[67,140],"show":[68,91,141],"that":[69,125,142,183,235],"neurons":[71,144],"learn":[75,145],"indicators":[77],"chemical":[79],"physiological":[81],"through":[83],"visualization":[84],"weights,":[88],"how":[92],"these":[93],"are":[95,115],"combined":[96],"by":[97],"accuracy":[105],"around":[106],"90%":[107],"test":[110],"set.":[111],"While":[112],"networks":[114,215,228],"often":[116],"perceived":[117],"\u2018black":[119],"box\u2019":[120],"classifiers,":[121],"our":[122,178],"work":[123],"shows":[124],"they":[126],"can":[127],"be":[128],"fact":[130],"more":[131],"explainable":[132],"informative":[134],"than":[135],"other":[136,239],"machine":[137],"learning":[138],"methods.":[139],"fundamental":[146],"about":[148],"vegetation,":[150],"example":[152],"composition":[154],"different":[156],"types":[157],"chlorophyll":[159],"indicates":[162],"well":[165],"response":[167],"to":[168,190,238],"illumination":[169],"conditions.":[170],"There":[171],"clear":[173],"excess":[174],"training":[175],"capacity":[176],"network,":[179],"expect":[182],"continues":[189],"grow":[191],"approach":[193],"will":[197],"provide":[198],"further":[199],"foundational":[200],"insights":[201],"understanding":[203,233],"traits.":[205],"This":[206],"provides":[207,222],"methodology":[209],"designing":[211],"interpreting":[213],"data":[218],"general,":[220],"framework":[224],"using":[226],"hyperspectral":[230],"imagery":[231],"vegetation":[234],"extendable":[237],"domains.":[240]},"counts_by_year":[],"updated_date":"2025-12-22T23:10:17.713674","created_date":"2025-10-10T00:00:00"}