{"id":"https://openalex.org/W4401018262","doi":"https://doi.org/10.1109/ur61395.2024.10597519","title":"Semantic Segmentation for Robotic Apple Harvesting: A Deep Learning Approach Leveraging U-Net, Synthetic Data, and Domain Adaptation","display_name":"Semantic Segmentation for Robotic Apple Harvesting: A Deep Learning Approach Leveraging U-Net, Synthetic Data, and Domain Adaptation","publication_year":2024,"publication_date":"2024-06-24","ids":{"openalex":"https://openalex.org/W4401018262","doi":"https://doi.org/10.1109/ur61395.2024.10597519"},"language":"en","primary_location":{"id":"doi:10.1109/ur61395.2024.10597519","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ur61395.2024.10597519","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 21st International Conference on Ubiquitous Robots (UR)","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/A5113306317","display_name":"Ghokulji Selvaraj","orcid":null},"institutions":[{"id":"https://openalex.org/I107077323","display_name":"Worcester Polytechnic Institute","ror":"https://ror.org/05ejpqr48","country_code":"US","type":"education","lineage":["https://openalex.org/I107077323"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ghokulji Selvaraj","raw_affiliation_strings":["Worcester Polytechnic Institute,Robotics Engineering Department,Worcester,MA,USA,01609"],"affiliations":[{"raw_affiliation_string":"Worcester Polytechnic Institute,Robotics Engineering Department,Worcester,MA,USA,01609","institution_ids":["https://openalex.org/I107077323"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084689056","display_name":"Siavash Farzan","orcid":"https://orcid.org/0000-0001-6415-5560"},"institutions":[{"id":"https://openalex.org/I149919469","display_name":"California Polytechnic State University","ror":"https://ror.org/001gpfp45","country_code":"US","type":"education","lineage":["https://openalex.org/I149919469"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Siavash Farzan","raw_affiliation_strings":["California Polytechnic State University,Electrical Engineering Department,San Luis Obispo,CA,USA,93407"],"affiliations":[{"raw_affiliation_string":"California Polytechnic State University,Electrical Engineering Department,San Luis Obispo,CA,USA,93407","institution_ids":["https://openalex.org/I149919469"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5113306317"],"corresponding_institution_ids":["https://openalex.org/I107077323"],"apc_list":null,"apc_paid":null,"fwci":0.6028,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.77387422,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"611","last_page":"618"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.8438000082969666,"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":0.8438000082969666,"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/domain-adaptation","display_name":"Domain adaptation","score":0.8585665225982666},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.79319828748703},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6604316234588623},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.6441265344619751},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6343360543251038},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5485381484031677},{"id":"https://openalex.org/keywords/net","display_name":"Net (polyhedron)","score":0.5201883316040039},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.47769978642463684},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.41490858793258667},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3839753270149231}],"concepts":[{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.8585665225982666},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.79319828748703},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6604316234588623},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.6441265344619751},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6343360543251038},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5485381484031677},{"id":"https://openalex.org/C14166107","wikidata":"https://www.wikidata.org/wiki/Q253829","display_name":"Net (polyhedron)","level":2,"score":0.5201883316040039},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.47769978642463684},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.41490858793258667},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3839753270149231},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ur61395.2024.10597519","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ur61395.2024.10597519","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 21st International Conference on Ubiquitous Robots (UR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1525717033","https://openalex.org/W1861492603","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2031489346","https://openalex.org/W2098185782","https://openalex.org/W2108598243","https://openalex.org/W2119765017","https://openalex.org/W2194775991","https://openalex.org/W2501369945","https://openalex.org/W2555576940","https://openalex.org/W2876225115","https://openalex.org/W2883090707","https://openalex.org/W2889985731","https://openalex.org/W2963523428","https://openalex.org/W2981708487","https://openalex.org/W2984249216","https://openalex.org/W2999206534","https://openalex.org/W3011638763","https://openalex.org/W3040547430","https://openalex.org/W3091091831","https://openalex.org/W3099319035","https://openalex.org/W3103795537","https://openalex.org/W4391615510","https://openalex.org/W6631591799","https://openalex.org/W6635232323"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2912321008","https://openalex.org/W1998607122","https://openalex.org/W2324368075","https://openalex.org/W2972124131","https://openalex.org/W338149487","https://openalex.org/W2972032537","https://openalex.org/W150363521","https://openalex.org/W3154107650","https://openalex.org/W1522196789"],"abstract_inverted_index":{"This":[0,45],"paper":[1],"introduces":[2],"a":[3,19,71,81,95,168],"deep":[4,163],"learning-based":[5,164],"semantic":[6,165],"segmentation":[7],"framework":[8],"tailored":[9],"for":[10,86],"robotic":[11,72],"apple":[12,22,38],"harvesting,":[13],"leveraging":[14],"synthetic":[15,64,107,160],"data":[16,118,161,175],"generated":[17],"within":[18],"3D":[20],"simulated":[21],"orchard.":[23],"The":[24,63,154],"proposed":[25],"simulation":[26],"environment":[27],"replicates":[28],"real-world":[29,56,109,142,174],"scenarios,":[30],"encompassing":[31],"challenges":[32],"such":[33,114],"as":[34,115],"occlusion,":[35],"variety":[36],"in":[37,42,59,75,127,135,140,162],"types,":[39],"and":[40,51,108,120,137,149,170],"changes":[41],"lighting":[43],"conditions.":[44],"approach":[46],"eliminates":[47],"the":[48,76,121,128,157],"extensive":[49],"costs":[50],"complexities":[52],"associated":[53],"with":[54,70],"collecting":[55],"datasets,":[57],"particularly":[58],"unpredictable":[60],"agricultural":[61],"settings.":[62],"dataset,":[65],"rendered":[66],"from":[67],"perspectives":[68],"consistent":[69],"harvester's":[73],"camera":[74],"Gazebo":[77],"physics":[78],"engine,":[79],"provides":[80],"comprehensive":[82],"range":[83],"of":[84,123,159],"scenarios":[85],"robust":[87],"model":[88],"training.":[89],"For":[90],"validation,":[91],"we":[92,131],"deploy":[93],"U-Net,":[94],"fully":[96],"convolutional":[97],"neural":[98],"network,":[99],"emphasizing":[100],"its":[101],"adaptability":[102],"to":[103,145,180],"domain":[104,116],"shifts":[105],"between":[106],"data.":[110],"By":[111],"integrating":[112],"strategies":[113],"adaptation,":[117],"augmentation,":[119],"inclusion":[122],"pre-trained":[124],"ResNet-50":[125],"encoders":[126],"U-Net":[129,147],"framework,":[130],"demonstrate":[132],"superior":[133],"performance":[134],"detecting":[136],"segmenting":[138],"apples":[139],"diverse":[141],"conditions":[143],"compared":[144],"standard":[146],"models":[148],"traditional":[150],"computer":[151],"vision":[152],"techniques.":[153],"results":[155],"highlight":[156],"potential":[158],"segmentation,":[166],"offering":[167],"cost-effective":[169],"scalable":[171],"solution":[172],"when":[173],"is":[176],"limited":[177],"or":[178],"hard":[179],"collect.":[181]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-25T13:04:00.132906","created_date":"2025-10-10T00:00:00"}