{"id":"https://openalex.org/W4310985342","doi":"https://doi.org/10.1109/metroagrifor55389.2022.9964902","title":"Reliable image processing algorithm for sunburn management in green apples","display_name":"Reliable image processing algorithm for sunburn management in green apples","publication_year":2022,"publication_date":"2022-11-03","ids":{"openalex":"https://openalex.org/W4310985342","doi":"https://doi.org/10.1109/metroagrifor55389.2022.9964902"},"language":"en","primary_location":{"id":"doi:10.1109/metroagrifor55389.2022.9964902","doi":"https://doi.org/10.1109/metroagrifor55389.2022.9964902","is_oa":false,"landing_page_url":"https://doi.org/10.1109/metroagrifor55389.2022.9964902","pdf_url":null,"source":{"id":"https://openalex.org/S7407085957","display_name":"IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)","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":"2022 IEEE Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","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/A5083985135","display_name":"Basavaraj R. Amogi","orcid":"https://orcid.org/0000-0002-3975-5764","observed_orcids":["https://orcid.org/0000-0002-3975-5764"]},"institutions":[{"id":"https://openalex.org/I72951846","display_name":"Washington State University","ror":"https://ror.org/05dk0ce17","country_code":"US","type":"education","lineage":["https://openalex.org/I72951846"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Basavaraj R. Amogi","raw_affiliation_strings":["Center for Precision and Automated Agricultural Systems, Washington State University,Department of Biological Systems Engineering,Prosser,WA,United States,99350"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Precision and Automated Agricultural Systems, Washington State University,Department of Biological Systems Engineering,Prosser,WA,United States,99350","institution_ids":["https://openalex.org/I72951846"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042760124","display_name":"Rakesh Ranjan","orcid":"https://orcid.org/0000-0001-9862-6746","observed_orcids":["https://orcid.org/0000-0001-9862-6746"]},"institutions":[{"id":"https://openalex.org/I1311308879","display_name":"The Conservation Fund","ror":"https://ror.org/04mxfzz97","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I1311308879"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rakesh Ranjan","raw_affiliation_strings":["The Conservation Fund, Freshwater Institute,Arlington,VA,United States,22209"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Conservation Fund, Freshwater Institute,Arlington,VA,United States,22209","institution_ids":["https://openalex.org/I1311308879"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032313733","display_name":"Lav Ramchandra Khot","orcid":"https://orcid.org/0000-0002-5018-7825","observed_orcids":["https://orcid.org/0000-0002-5018-7825"]},"institutions":[{"id":"https://openalex.org/I72951846","display_name":"Washington State University","ror":"https://ror.org/05dk0ce17","country_code":"US","type":"education","lineage":["https://openalex.org/I72951846"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lav R. Khot","raw_affiliation_strings":["Center for Precision and Automated Agricultural Systems, Washington State University,Department of Biological Systems Engineering,Prosser,WA,United States,99350"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Precision and Automated Agricultural Systems, Washington State University,Department of Biological Systems Engineering,Prosser,WA,United States,99350","institution_ids":["https://openalex.org/I72951846"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.1653,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.87554629,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"186","last_page":"190"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9502000212669373,"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":0.9502000212669373,"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":0.02419999986886978,"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/T11546","display_name":"Plant Physiology and Cultivation Studies","score":0.00430000014603138,"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/green-apple","display_name":"green apple","score":0.7440000176429749},{"id":"https://openalex.org/keywords/fruit-surface-temperature","display_name":"fruit surface temperature","score":0.9430000185966492},{"id":"https://openalex.org/keywords/heat-stress-monitoring","display_name":"heat stress monitoring","score":0.6389999985694885},{"id":"https://openalex.org/keywords/edge-computing","display_name":"edge computing","score":0.7950000166893005},{"id":"https://openalex.org/keywords/precision-agriculture","display_name":"precision agriculture","score":0.42500001192092896},{"id":"https://openalex.org/keywords/apple-orchards","display_name":"apple orchards","score":0.628000020980835}],"concepts":[{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6022996306419373},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.555645763874054},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.49783945083618164},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46852582693099976},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4366213381290436},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.37497174739837646},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3726224899291992},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.34658169746398926},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0956437885761261}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/metroagrifor55389.2022.9964902","doi":"https://doi.org/10.1109/metroagrifor55389.2022.9964902","is_oa":false,"landing_page_url":"https://doi.org/10.1109/metroagrifor55389.2022.9964902","pdf_url":null,"source":{"id":"https://openalex.org/S7407085957","display_name":"IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)","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":"2022 IEEE Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.9208999872207642,"id":"https://metadata.un.org/sdg/2","display_name":"Zero hunger"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1969391481","https://openalex.org/W2014566210","https://openalex.org/W2021824448","https://openalex.org/W2089135225","https://openalex.org/W2123873447","https://openalex.org/W2905364102","https://openalex.org/W2978605283","https://openalex.org/W3015699616","https://openalex.org/W3034726464"],"related_works":["https://openalex.org/W2978605283","https://openalex.org/W4389280383","https://openalex.org/W6940119233","https://openalex.org/W2794302172","https://openalex.org/W3034726464","https://openalex.org/W3002639688","https://openalex.org/W4399560782","https://openalex.org/W4409356793","https://openalex.org/W4391761363","https://openalex.org/W4412076806"],"abstract_inverted_index":{"To":[0],"tackle":[1],"weather":[2,36],"uncertainties":[3],"and":[4,37,83,104,134,152,155,173,192,222,237],"associated":[5],"heat":[6],"stress":[7,20],"to":[8,72,102,127,135,214],"apple":[9,206],"fruits,":[10],"researchers":[11],"have":[12,25],"been":[13,26],"exploring":[14],"development":[15],"of":[16,92,98,150],"a":[17,106],"real-time":[18],"crop":[19],"monitoring":[21,45],"systems.":[22],"Our":[23],"group":[24],"researching":[27],"one":[28],"such":[29],"in-field":[30,94,198],"sensing":[31,95,199],"system":[32],"that":[33],"uses":[34],"localized":[35],"thermal-RGB":[38,74,238],"imagery":[39,75,119],"proceed":[40],"on":[41,180,196],"the":[42,77,85,90,93,176,184],"edge":[43,67],"for":[44,76,112,138,189],"fruit":[46,87,109,132],"surface":[47],"temperature":[48,124,129],"(FST).":[49],"Such":[50],"solutions":[51],"can":[52],"be":[53],"tied":[54],"with":[55,218],"mitigation":[56],"techniques":[57],"(e.g.,":[58],"water-based":[59],"cooling":[60,154],"methods)":[61],"as":[62],"precision":[63],"management.":[64],"However,":[65],"current":[66],"compute":[68],"algorithms":[69],"are":[70,171],"limited":[71],"segment":[73],"red":[78],"pigmented":[79],"fruits":[80,164,170],"near":[81],"maturity":[82],"lack":[84],"green":[86,172],"segmentation,":[88],"limiting":[89],"usability":[91],"system.":[96],"Aim":[97],"this":[99],"study":[100],"was":[101,125,144,186,224],"develop":[103],"validate":[105],"color":[107],"independent":[108],"segmentation":[110,133],"algorithm":[111,142,161,185],"successful":[113],"FST":[114,137,181,194,232],"monitoring.":[115],"Longwave":[116],"infrared":[117],"(LWIR)":[118],"at":[120],"daily":[121],"peak":[122],"air":[123],"utilized":[126],"achieve":[128],"gradient":[130],"aided":[131],"estimate":[136],"next":[139],"24-h.":[140],"The":[141],"robustness":[143],"field":[145],"evaluated":[146,188],"in":[147,165,204],"Fog-Net":[148],"(combination":[149],"fogging":[151],"netting)":[153],"control":[156],"treatments":[157],"(Year":[158,208],"2021).":[159],"Overall,":[160],"accurately":[162],"detected":[163],"early":[166],"growing":[167],"season":[168],"when":[169],"effectively":[174],"captured":[175],"treatment":[177],"effects":[178],"based":[179,239],"data.":[182],"Additionally,":[183],"also":[187],"computational":[190],"overhead":[191],"estimated":[193,233],"accuracy":[195],"an":[197],"node":[200],"(Control":[201],"treatment)":[202],"deployed":[203],"commercial":[205],"orchard":[207],"2022).":[209],"CPSS":[210],"took":[211],"12":[212],"milliseconds":[213],"process":[215],"LWIR":[216,235],"image":[217,241],"no":[219,225],"CPU":[220],"throttles":[221],"there":[223],"significant":[226],"difference":[227],"between$\\boldsymbol{(\\mathrm{R}^{2}=0.98}$,":[228],"p-value":[229],"=":[230],"0.1607)":[231],"using":[234],"(FSTi)":[236],"(FST_Actual)":[240],"processing":[242],"algorithm.":[243]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2}],"updated_date":"2026-10-29T09:56:32.521200","created_date":"2022-12-22T00:00:00"}