{"id":"https://openalex.org/W3176041514","doi":"https://doi.org/10.1109/i2mtc50364.2021.9460071","title":"Deep Learning for improving the storage process: Accurate and automatic segmentation of spoiled areas on apples","display_name":"Deep Learning for improving the storage process: Accurate and automatic segmentation of spoiled areas on apples","publication_year":2021,"publication_date":"2021-05-17","ids":{"openalex":"https://openalex.org/W3176041514","doi":"https://doi.org/10.1109/i2mtc50364.2021.9460071","mag":"3176041514"},"language":"en","primary_location":{"id":"doi:10.1109/i2mtc50364.2021.9460071","is_oa":false,"landing_page_url":"https://doi.org/10.1109/i2mtc50364.2021.9460071","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","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/A5015331544","display_name":"Nikita Stasenko","orcid":"https://orcid.org/0000-0003-4970-0246"},"institutions":[{"id":"https://openalex.org/I125989756","display_name":"Skolkovo Institute of Science and Technology","ror":"https://ror.org/03f9nc143","country_code":"RU","type":"education","lineage":["https://openalex.org/I125989756"]}],"countries":["RU"],"is_corresponding":true,"raw_author_name":"Nikita Stasenko","raw_affiliation_strings":["Skolkovo Institute of Science and Technology, CDISE, Moscow, Russia"],"affiliations":[{"raw_affiliation_string":"Skolkovo Institute of Science and Technology, CDISE, Moscow, Russia","institution_ids":["https://openalex.org/I125989756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034270154","display_name":"Elizaveta Chernova","orcid":null},"institutions":[{"id":"https://openalex.org/I125989756","display_name":"Skolkovo Institute of Science and Technology","ror":"https://ror.org/03f9nc143","country_code":"RU","type":"education","lineage":["https://openalex.org/I125989756"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Elizaveta Chernova","raw_affiliation_strings":["Skolkovo Institute of Science and Technology, CDISE, Digital Agriculture Lab, Moscow, Russia"],"affiliations":[{"raw_affiliation_string":"Skolkovo Institute of Science and Technology, CDISE, Digital Agriculture Lab, Moscow, Russia","institution_ids":["https://openalex.org/I125989756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024830111","display_name":"Dmitrii Shadrin","orcid":"https://orcid.org/0000-0003-3486-8214"},"institutions":[{"id":"https://openalex.org/I125989756","display_name":"Skolkovo Institute of Science and Technology","ror":"https://ror.org/03f9nc143","country_code":"RU","type":"education","lineage":["https://openalex.org/I125989756"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Dmitrii Shadrin","raw_affiliation_strings":["Skolkovo Institute of Science and Technology, CDISE, Digital Agriculture Lab, Moscow, Russia"],"affiliations":[{"raw_affiliation_string":"Skolkovo Institute of Science and Technology, CDISE, Digital Agriculture Lab, Moscow, Russia","institution_ids":["https://openalex.org/I125989756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044787915","display_name":"George Ovchinnikov","orcid":"https://orcid.org/0000-0002-5522-1834"},"institutions":[{"id":"https://openalex.org/I125989756","display_name":"Skolkovo Institute of Science and Technology","ror":"https://ror.org/03f9nc143","country_code":"RU","type":"education","lineage":["https://openalex.org/I125989756"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"George Ovchinnikov","raw_affiliation_strings":["Skolkovo Institute of Science and Technology, CDISE, Moscow, Russia"],"affiliations":[{"raw_affiliation_string":"Skolkovo Institute of Science and Technology, CDISE, Moscow, Russia","institution_ids":["https://openalex.org/I125989756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054681264","display_name":"\u0418 \u041f \u041a\u0440\u0438\u0432\u043e\u043b\u0430\u043f\u043e\u0432","orcid":null},"institutions":[{"id":"https://openalex.org/I4210152733","display_name":"Michurinsk State Agrarian University","ror":"https://ror.org/0475cfj29","country_code":"RU","type":"education","lineage":["https://openalex.org/I4210152733"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Ivan Krivolapov","raw_affiliation_strings":["Michurinsk State Agrarian University, Michurinsk, Russia"],"affiliations":[{"raw_affiliation_string":"Michurinsk State Agrarian University, Michurinsk, Russia","institution_ids":["https://openalex.org/I4210152733"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067086380","display_name":"Mariia Pukalchik","orcid":"https://orcid.org/0000-0001-7996-642X"},"institutions":[{"id":"https://openalex.org/I125989756","display_name":"Skolkovo Institute of Science and Technology","ror":"https://ror.org/03f9nc143","country_code":"RU","type":"education","lineage":["https://openalex.org/I125989756"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Mariia Pukalchik","raw_affiliation_strings":["Skolkovo Institute of Science and Technology, CDISE, Digital Agriculture Lab, Moscow, Russia"],"affiliations":[{"raw_affiliation_string":"Skolkovo Institute of Science and Technology, CDISE, Digital Agriculture Lab, Moscow, Russia","institution_ids":["https://openalex.org/I125989756"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5015331544"],"corresponding_institution_ids":["https://openalex.org/I125989756"],"apc_list":null,"apc_paid":null,"fwci":2.4073,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.88681105,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"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/T10616","display_name":"Smart Agriculture and AI","score":0.9988999962806702,"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":0.9988999962806702,"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/T12894","display_name":"Date Palm Research Studies","score":0.9934999942779541,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9925000071525574,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/postharvest","display_name":"Postharvest","score":0.7605763077735901},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6891217827796936},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6511794328689575},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.5882492065429688},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5788880586624146},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5364288687705994},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5232928395271301},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.5108951926231384},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4313699007034302},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4190235435962677},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3332904577255249},{"id":"https://openalex.org/keywords/horticulture","display_name":"Horticulture","score":0.11062118411064148},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08797097206115723}],"concepts":[{"id":"https://openalex.org/C157670687","wikidata":"https://www.wikidata.org/wiki/Q426933","display_name":"Postharvest","level":2,"score":0.7605763077735901},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6891217827796936},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6511794328689575},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5882492065429688},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5788880586624146},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5364288687705994},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5232928395271301},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.5108951926231384},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4313699007034302},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4190235435962677},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3332904577255249},{"id":"https://openalex.org/C144027150","wikidata":"https://www.wikidata.org/wiki/Q48803","display_name":"Horticulture","level":1,"score":0.11062118411064148},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08797097206115723},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/i2mtc50364.2021.9460071","is_oa":false,"landing_page_url":"https://doi.org/10.1109/i2mtc50364.2021.9460071","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Zero hunger","score":0.7799999713897705,"id":"https://metadata.un.org/sdg/2"}],"awards":[{"id":"https://openalex.org/G4174635572","display_name":null,"funder_award_id":"19-29-09085 MK","funder_id":"https://openalex.org/F4320321079","funder_display_name":"Russian Foundation for Basic Research"}],"funders":[{"id":"https://openalex.org/F4320321079","display_name":"Russian Foundation for Basic Research","ror":"https://ror.org/02mh1ke95"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2148333466","https://openalex.org/W2247236939","https://openalex.org/W2530179772","https://openalex.org/W2543665758","https://openalex.org/W2600825551","https://openalex.org/W2618530766","https://openalex.org/W2630837129","https://openalex.org/W2742700757","https://openalex.org/W2766066477","https://openalex.org/W2794915299","https://openalex.org/W2898364767","https://openalex.org/W2900324734","https://openalex.org/W2907663452","https://openalex.org/W2920270483","https://openalex.org/W2922623026","https://openalex.org/W2929667367","https://openalex.org/W2934580386","https://openalex.org/W2936718694","https://openalex.org/W2969640942","https://openalex.org/W2972333791","https://openalex.org/W2972955397","https://openalex.org/W2985886590","https://openalex.org/W2996132143","https://openalex.org/W2997032006","https://openalex.org/W3009473123","https://openalex.org/W3024956434","https://openalex.org/W3025686484","https://openalex.org/W3094535040","https://openalex.org/W3109676856","https://openalex.org/W4247292278","https://openalex.org/W4287778984","https://openalex.org/W6639824700","https://openalex.org/W6691158522","https://openalex.org/W6774217238","https://openalex.org/W6786363107"],"related_works":["https://openalex.org/W2359643675","https://openalex.org/W3209922781","https://openalex.org/W4390424254","https://openalex.org/W4243110937","https://openalex.org/W2624055430","https://openalex.org/W3141512175","https://openalex.org/W4247755507","https://openalex.org/W3166583931","https://openalex.org/W2110560018","https://openalex.org/W4237560127"],"abstract_inverted_index":{"Artificial":[0],"Intelligence":[1],"(AI)":[2],"methods":[3],"and":[4,14,35,57,80,91,149,200],"technologies":[5,20],"have":[6],"been":[7],"successfully":[8],"applied":[9],"for":[10,66,145,158,172,186],"recognizing":[11],"objects,":[12],"detecting":[13],"segmenting":[15],"RGB":[16],"images.":[17,165],"Today,":[18],"such":[19,54],"are":[21,42],"widely":[22],"used":[23],"in":[24,48,95,175,192],"precision":[25,193],"agriculture":[26,194],"to":[27,89,183],"estimate":[28],"food":[29,49,70,189],"quality,":[30],"especially":[31],"when":[32],"assessing":[33,67],"plants":[34],"fruits":[36],"at":[37,99,141,154],"various":[38],"harvest":[39],"stages.":[40],"There":[41],"also":[43,167],"several":[44],"processes":[45],"taking":[46],"place":[47],"during":[50],"the":[51,60,68,135,142,146,152,155,159,169,188,197,203],"postharvest":[52,69,96],"stages,":[53],"as":[55],"decay":[56,93,117,173],"moldy.":[58],"However,":[59],"number":[61],"of":[62,113,134,151,202],"AI":[63],"approaches":[64],"allowing":[65],"conditions":[71],"is":[72],"limited.":[73],"In":[74],"this":[75],"work,":[76],"we":[77],"trained":[78,105,162],"U-Net":[79,147],"Deeplab":[81,160],"models":[82,103],"based":[83],"on":[84,106,127,163],"Convolutional":[85],"Neural":[86],"Networks":[87],"(CNNs)":[88],"detect":[90],"predict":[92],"areas":[94,174],"apples":[97,114,176],"stored":[98],"room":[100],"temperatures.":[101],"The":[102],"were":[104,120],"a":[107,123,128],"dataset":[108],"that":[109],"includes":[110],"4440":[111],"images":[112],"with":[115],"segmented":[116],"areas.":[118,205],"Images":[119],"captured":[121],"by":[122,178,195],"digital":[124],"camera":[125],"mounted":[126],"custom-made":[129],"testbed.":[130],"We":[131,166],"achieved":[132],"99.71%":[133],"mean":[136],"Intersection":[137],"over":[138],"Union":[139],"(mIoU)":[140],"testing":[143,156],"stage":[144,157],"model":[148,161],"99.99%":[150],"mIoU":[153],"651":[164],"presented":[168],"first":[170],"masks":[171],"predicted":[177],"U-Net.":[179],"Our":[180],"approach":[181],"seems":[182],"be":[184],"promising":[185],"improving":[187],"storage":[190],"process":[191],"enabling":[196],"automatic":[198],"detection":[199],"quantification":[201],"decayed":[204]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
