{"id":"https://openalex.org/W4414878640","doi":"https://doi.org/10.48550/arxiv.2507.17219","title":"A Low-Cost Machine Learning Approach for Timber Diameter Estimation","display_name":"A Low-Cost Machine Learning Approach for Timber Diameter Estimation","publication_year":2025,"publication_date":"2025-07-23","ids":{"openalex":"https://openalex.org/W4414878640","doi":"https://doi.org/10.48550/arxiv.2507.17219"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2507.17219","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.17219","pdf_url":"https://arxiv.org/pdf/2507.17219","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2507.17219","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114602061","display_name":"Fatemeh Hasanzadeh Fard","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Fard, Fatemeh Hasanzadeh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005416314","display_name":"Sanaz Hasanzadeh Fard","orcid":"https://orcid.org/0009-0007-0807-1339"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fard, Sanaz Hasanzadeh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5006259052","display_name":"Mehdi Jonoobi","orcid":"https://orcid.org/0000-0003-3590-175X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jonoobi, Mehdi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5114602061"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10484","display_name":"Wood Treatment and Properties","score":0.9815999865531921,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10484","display_name":"Wood Treatment and Properties","score":0.9815999865531921,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11595","display_name":"Textile materials and evaluations","score":0.9517999887466431,"subfield":{"id":"https://openalex.org/subfields/2507","display_name":"Polymers and Plastics"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12971","display_name":"Material Properties and Processing","score":0.9483000040054321,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5393999814987183},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.49399998784065247},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.4595000147819519},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.39879998564720154},{"id":"https://openalex.org/keywords/wood-processing","display_name":"Wood processing","score":0.38449999690055847},{"id":"https://openalex.org/keywords/machine-vision","display_name":"Machine vision","score":0.38089999556541443},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3792000114917755},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.36959999799728394}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5885000228881836},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5609999895095825},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5393999814987183},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5385000109672546},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.49399998784065247},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.4595000147819519},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.39879998564720154},{"id":"https://openalex.org/C2776752069","wikidata":"https://www.wikidata.org/wiki/Q1609891","display_name":"Wood processing","level":2,"score":0.38449999690055847},{"id":"https://openalex.org/C5339829","wikidata":"https://www.wikidata.org/wiki/Q1425977","display_name":"Machine vision","level":2,"score":0.38089999556541443},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3792000114917755},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.36959999799728394},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.3693999946117401},{"id":"https://openalex.org/C2778348673","wikidata":"https://www.wikidata.org/wiki/Q739302","display_name":"Production (economics)","level":2,"score":0.3458000123500824},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.3377000093460083},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33649998903274536},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.2937000095844269},{"id":"https://openalex.org/C58328972","wikidata":"https://www.wikidata.org/wiki/Q184609","display_name":"Expert system","level":2,"score":0.28929999470710754},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2892000079154968},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.287200003862381},{"id":"https://openalex.org/C138827492","wikidata":"https://www.wikidata.org/wiki/Q6661985","display_name":"Data processing","level":2,"score":0.27459999918937683},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.26409998536109924},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2612999975681305},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.25529998540878296}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2507.17219","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.17219","pdf_url":"https://arxiv.org/pdf/2507.17219","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2507.17219","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2507.17219","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2507.17219","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.17219","pdf_url":"https://arxiv.org/pdf/2507.17219","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"wood":[1],"processing":[2,45],"industry,":[3],"particularly":[4,166],"in":[5,117,167],"facilities":[6],"such":[7],"as":[8],"sawmills":[9],"and":[10,16,21,39,53,94,163,169],"MDF":[11],"production":[12],"lines,":[13],"requires":[14],"accurate":[15],"efficient":[17],"identification":[18],"of":[19,23,62,136],"species":[20],"thickness":[22,96],"the":[24,60,77,128],"wood.":[25],"Although":[26],"traditional":[27],"methods":[28,102],"rely":[29],"heavily":[30],"on":[31,51,83,114],"expert":[32],"human":[33],"labor,":[34],"they":[35],"are":[36],"slow,":[37],"inconsistent,":[38],"prone":[40],"to":[41,89],"error,":[42],"especially":[43],"when":[44],"large":[46],"volumes.":[47],"This":[48,147],"study":[49],"focuses":[50],"practical":[52,154],"cost-effective":[54],"machine":[55],"learning":[56],"frameworks":[57],"that":[58,103,127],"automate":[59],"estimation":[61],"timber":[63,92,122],"log":[64,140],"diameter":[65],"using":[66],"standard":[67],"RGB":[68],"images":[69,115],"captured":[70],"under":[71],"real-world":[72],"working":[73],"conditions.":[74],"We":[75],"employ":[76],"YOLOv5":[78],"object":[79],"detection":[80,141],"algorithm,":[81],"fine-tuned":[82],"a":[84,131],"public":[85],"dataset":[86],"(TimberSeg":[87],"1.0),":[88],"detect":[90],"individual":[91],"logs":[93],"estimate":[95],"through":[97],"bounding-box":[98],"dimensions.":[99],"Unlike":[100],"previous":[101],"require":[104],"expensive":[105],"sensors":[106],"or":[107],"controlled":[108],"environments,":[109],"this":[110],"model":[111,129],"is":[112],"trained":[113],"taken":[116],"typical":[118],"industrial":[119],"sheds":[120],"during":[121],"delivery.":[123],"Experimental":[124],"results":[125],"show":[126],"achieves":[130],"mean":[132],"Average":[133],"Precision":[134],"(mAP@0.5)":[135],"0.64,":[137],"demonstrating":[138],"reliable":[139],"even":[142],"with":[143],"modest":[144],"computing":[145],"resources.":[146],"lightweight,":[148],"scalable":[149],"solution":[150],"holds":[151],"promise":[152],"for":[153],"integration":[155],"into":[156],"existing":[157],"workflows,":[158],"including":[159],"on-site":[160],"inventory":[161],"management":[162],"preliminary":[164],"sorting,":[165],"small":[168],"medium-sized":[170],"operations.":[171]},"counts_by_year":[],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2025-10-10T00:00:00"}
