{"id":"https://openalex.org/W2966593772","doi":"https://doi.org/10.1109/access.2019.2933062","title":"Deepblueberry: Quantification of Blueberries in the Wild Using Instance Segmentation","display_name":"Deepblueberry: Quantification of Blueberries in the Wild Using Instance Segmentation","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2966593772","doi":"https://doi.org/10.1109/access.2019.2933062","mag":"2966593772"},"language":"en","primary_location":{"id":"doi:10.1109/access.2019.2933062","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2933062","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08787818.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08787818.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103929383","display_name":"Sebastian Gonzalez","orcid":"https://orcid.org/0000-0003-2937-3794"},"institutions":[{"id":"https://openalex.org/I13897259","display_name":"Universidad Andr\u00e9s Bello","ror":"https://ror.org/01qq57711","country_code":"CL","type":"education","lineage":["https://openalex.org/I13897259"]}],"countries":["CL"],"is_corresponding":false,"raw_author_name":"Sebastian Gonzalez","raw_affiliation_strings":["Department of Engineering Sciences, Universidad Andres Bello, Santiago, Chile"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Engineering Sciences, Universidad Andres Bello, Santiago, Chile","institution_ids":["https://openalex.org/I13897259"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028361572","display_name":"Claudia Arellano","orcid":"https://orcid.org/0000-0002-3565-0308"},"institutions":[{"id":"https://openalex.org/I4210155252","display_name":"Instituto Nacional de Capacitaci\u00f3n Profesional","ror":"https://ror.org/05141h569","country_code":"CL","type":"education","lineage":["https://openalex.org/I4210155252"]}],"countries":["CL"],"is_corresponding":false,"raw_author_name":"Claudia Arellano","raw_affiliation_strings":["Research and Development Department, Universidad Tecnologica de Chile\u2014INACAP, Santiago, Chile"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research and Development Department, Universidad Tecnologica de Chile\u2014INACAP, Santiago, Chile","institution_ids":["https://openalex.org/I4210155252"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017736954","display_name":"Juan Tapia","orcid":"https://orcid.org/0000-0001-9159-4075"},"institutions":[{"id":"https://openalex.org/I13897259","display_name":"Universidad Andr\u00e9s Bello","ror":"https://ror.org/01qq57711","country_code":"CL","type":"education","lineage":["https://openalex.org/I13897259"]}],"countries":["CL"],"is_corresponding":false,"raw_author_name":"Juan E. Tapia","raw_affiliation_strings":["Department of Engineering Sciences, Universidad Andres Bello, Santiago, Chile"],"raw_orcid":"https://orcid.org/0000-0001-9159-4075","affiliations":[{"raw_affiliation_string":"Department of Engineering Sciences, Universidad Andres Bello, Santiago, Chile","institution_ids":["https://openalex.org/I13897259"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":8.6956,"has_fulltext":true,"cited_by_count":80,"citation_normalized_percentile":{"value":0.97240137,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"7","issue":null,"first_page":"105776","last_page":"105788"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9987999796867371,"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":0.9987999796867371,"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":0.9544000029563904,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9320999979972839,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7880852222442627},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6818020343780518},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6595717668533325},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.6551703214645386},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6335094571113586},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6239223480224609},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5168285965919495},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5133325457572937},{"id":"https://openalex.org/keywords/sort","display_name":"sort","score":0.4986119270324707},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4919542968273163},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.48914626240730286}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7880852222442627},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6818020343780518},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6595717668533325},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.6551703214645386},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6335094571113586},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6239223480224609},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5168285965919495},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5133325457572937},{"id":"https://openalex.org/C88548561","wikidata":"https://www.wikidata.org/wiki/Q347599","display_name":"sort","level":2,"score":0.4986119270324707},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4919542968273163},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.48914626240730286},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","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/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2019.2933062","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2933062","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08787818.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:6b56bd0aed264b72b3cb4479a7d5a5cd","is_oa":true,"landing_page_url":"https://doaj.org/article/6b56bd0aed264b72b3cb4479a7d5a5cd","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 7, Pp 105776-105788 (2019)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2019.2933062","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2933062","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08787818.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2966593772.pdf","grobid_xml":"https://content.openalex.org/works/W2966593772.grobid-xml"},"referenced_works_count":44,"referenced_works":["https://openalex.org/W809122546","https://openalex.org/W1507506748","https://openalex.org/W1536680647","https://openalex.org/W1686810756","https://openalex.org/W1849277567","https://openalex.org/W1903029394","https://openalex.org/W1926243895","https://openalex.org/W1948751323","https://openalex.org/W1958328135","https://openalex.org/W2080430926","https://openalex.org/W2102605133","https://openalex.org/W2108598243","https://openalex.org/W2160581817","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2216125271","https://openalex.org/W2412782625","https://openalex.org/W2555182955","https://openalex.org/W2609077090","https://openalex.org/W2740520864","https://openalex.org/W2766422654","https://openalex.org/W2790979755","https://openalex.org/W2793444752","https://openalex.org/W2897350321","https://openalex.org/W2898424076","https://openalex.org/W2919091563","https://openalex.org/W2919115771","https://openalex.org/W2921445558","https://openalex.org/W2942265585","https://openalex.org/W2943994693","https://openalex.org/W2950800384","https://openalex.org/W2951631494","https://openalex.org/W2953151638","https://openalex.org/W2963150697","https://openalex.org/W2963353662","https://openalex.org/W2963523428","https://openalex.org/W3100555247","https://openalex.org/W4297775537","https://openalex.org/W6622964371","https://openalex.org/W6630336748","https://openalex.org/W6637373629","https://openalex.org/W6684191040","https://openalex.org/W6714138976","https://openalex.org/W6737664043"],"related_works":["https://openalex.org/W2361805396","https://openalex.org/W2972254340","https://openalex.org/W1805912688","https://openalex.org/W2373973507","https://openalex.org/W2022231341","https://openalex.org/W4250902763","https://openalex.org/W4255476312","https://openalex.org/W2351154965","https://openalex.org/W2348909947","https://openalex.org/W2041503010"],"abstract_inverted_index":{"An":[0],"accurate":[1],"and":[2,47,67,103,114,132,141,160,165,172,187,189,199],"reliable":[3],"image-based":[4],"quantification":[5,64],"system":[6],"for":[7,12,25,100,183,191],"blueberries":[8,32,52,71],"may":[9,19],"be":[10,55],"useful":[11],"the":[13,23,48,63,73,88,121,126,133,151,177,184,192],"automation":[14],"of":[15,31,51,70,90,120,158,163,197],"harvest":[16],"management.":[17],"It":[18],"also":[20],"serve":[21],"as":[22,111],"basis":[24],"controlling":[26],"robotic":[27],"harvesting":[28],"systems.":[29],"Quantification":[30],"from":[33],"images":[34,78,140],"is":[35],"a":[36,81,94,155],"challenging":[37],"task":[38],"due":[39],"to":[40,86],"occlusions,":[41],"differences":[42],"in":[43,57,72],"size,":[44],"illumination":[45],"conditions":[46],"irregular":[49],"amount":[50],"that":[53],"can":[54],"present":[56],"an":[58],"image.":[59],"This":[60],"paper":[61],"proposes":[62],"per":[65,68,92,139,142],"image":[66],"batch":[69],"wild,":[74],"using":[75,80,125,195],"high":[76],"definition":[77],"captured":[79],"mobile":[82],"device.":[83],"In":[84],"order":[85],"quantify":[87],"number":[89],"berries":[91],"image,":[93],"network":[95],"based":[96],"on":[97],"Mask":[98],"R-CNN":[99],"object":[101],"detection":[102,146],"instance":[104,175],"segmentation":[105],"was":[106,123,148],"proposed.":[107],"Several":[108],"backbones":[109],"such":[110],"ResNet101,":[112],"ResNet50":[113,152],"MobileNetV1":[115],"were":[116,181],"tested.":[117],"The":[118,144],"performance":[119],"algorithm":[122],"evaluated":[124],"Intersection":[127],"over":[128],"Union":[129],"Error":[130],"(IoU)":[131],"competitive":[134],"mean":[135],"Average":[136],"Precision":[137],"(mAP)":[138],"batch.":[143],"best":[145,178],"result":[147],"obtained":[149,180],"with":[150],"backbone":[153],"achieving":[154],"mIoU":[156,185],"score":[157],"0.595":[159],"mAP":[161,193],"scores":[162],"0.759":[164],"0.724":[166],"respectively":[167],"(for":[168],"IoU":[169],"thresholds":[170,196],"0.5":[171,198],"0.7).":[173],"For":[174],"segmentation,":[176],"results":[179],"0.726":[182],"metric":[186,194],"0.909":[188],"0.774":[190],"0.7":[200],"respectively.":[201]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":19},{"year":2023,"cited_by_count":14},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":8},{"year":2019,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}