{"id":"https://openalex.org/W2751292364","doi":"https://doi.org/10.3233/978-1-61499-796-2-59","title":"Automatic Rice Yield Estimation Using Image Processing Technique","display_name":"Automatic Rice Yield Estimation Using Image Processing Technique","publication_year":2017,"publication_date":"2017-01-01","ids":{"openalex":"https://openalex.org/W2751292364","doi":"https://doi.org/10.3233/978-1-61499-796-2-59","mag":"2751292364"},"language":"en","primary_location":{"id":"doi:10.3233/978-1-61499-796-2-59","is_oa":true,"landing_page_url":"https://doi.org/10.3233/978-1-61499-796-2-59","pdf_url":null,"source":{"id":"https://openalex.org/S4210168452","display_name":"Ambient intelligence and smart environments","issn_l":"1875-4163","issn":["1875-4163","1875-4171"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"book series"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Ambient Intelligence and Smart Environments","raw_type":"book-chapter"},"type":"book-chapter","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.3233/978-1-61499-796-2-59","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Reza Md Nasim","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Reza Md Nasim","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016894750","display_name":"In Seop Na","orcid":"https://orcid.org/0000-0001-6471-043X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Na In Seop","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5116306507","display_name":"Baek Sun Wook","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Baek Sun Wook","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5116306508","display_name":"Lee Kyeong-Hwan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee Kyeong-Hwan","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6018,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.67975996,"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9577999711036682,"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":0.9577999711036682,"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/yield","display_name":"Yield (engineering)","score":0.6061565279960632},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.5728625655174255},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4522762894630432},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4403761923313141},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.39197516441345215},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.14057642221450806},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.08231130242347717},{"id":"https://openalex.org/keywords/systems-engineering","display_name":"Systems engineering","score":0.031142055988311768}],"concepts":[{"id":"https://openalex.org/C134121241","wikidata":"https://www.wikidata.org/wiki/Q899301","display_name":"Yield (engineering)","level":2,"score":0.6061565279960632},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.5728625655174255},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4522762894630432},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4403761923313141},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.39197516441345215},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14057642221450806},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.08231130242347717},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.031142055988311768},{"id":"https://openalex.org/C191897082","wikidata":"https://www.wikidata.org/wiki/Q11467","display_name":"Metallurgy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/978-1-61499-796-2-59","is_oa":true,"landing_page_url":"https://doi.org/10.3233/978-1-61499-796-2-59","pdf_url":null,"source":{"id":"https://openalex.org/S4210168452","display_name":"Ambient intelligence and smart environments","issn_l":"1875-4163","issn":["1875-4163","1875-4171"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"book series"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Ambient Intelligence and Smart Environments","raw_type":"book-chapter"}],"best_oa_location":{"id":"doi:10.3233/978-1-61499-796-2-59","is_oa":true,"landing_page_url":"https://doi.org/10.3233/978-1-61499-796-2-59","pdf_url":null,"source":{"id":"https://openalex.org/S4210168452","display_name":"Ambient intelligence and smart environments","issn_l":"1875-4163","issn":["1875-4163","1875-4171"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"book series"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Ambient Intelligence and Smart Environments","raw_type":"book-chapter"},"sustainable_development_goals":[{"score":0.6399999856948853,"display_name":"Zero hunger","id":"https://metadata.un.org/sdg/2"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2772917594","https://openalex.org/W2036807459","https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"Precision":[0],"agriculture":[1],"enables":[2],"the":[3,18,22,33,90,100,118,153,156,181,219],"dynamic":[4],"use":[5],"of":[6,87,164],"technologies":[7],"to":[8,31,36,80,107,114,121,130,176,235],"boost":[9],"up":[10],"crop":[11,14,39],"production,":[12],"increase":[13],"yield":[15,52],"and":[16,27,76,111,117,138,149,162,180,197,203,223,231,239],"at":[17],"same":[19],"time":[20],"decrease":[21],"input":[23],"variables.":[24],"Yield":[25],"monitoring":[26],"estimation":[28,53],"are":[29],"considered":[30],"be":[32,59,195,206,236],"major":[34],"steps":[35],"implement":[37],"site-specific":[38],"management":[40],"or":[41],"precision":[42],"farming.":[43],"Unmanned":[44],"aerial":[45],"vehicle":[46],"(UAV)":[47],"imaging":[48],"based":[49,145],"automatic":[50],"rice":[51,82,192,199,227],"in":[54,96,135],"different":[55],"growing":[56],"stages,":[57],"could":[58],"a":[60,67,103,137,177],"solution.":[61],"In":[62,99,152],"this":[63],"work,":[64],"we":[65],"proposed":[66,188,224],"color-based":[68],"segmentation":[69],"algorithm":[70],"that":[71,191,198],"used":[72],"Lab":[73,122],"color":[74,123,148,185],"space":[75],"k-mean":[77],"clustering":[78,127,172],"techniques":[79],"detect":[81],"grain":[83,88,193],"panicles":[84,201,229],"area.":[85],"Segmentation":[86],"from":[89,208],"image":[91,119],"background":[92],"was":[93,105,128,160,166,174],"carried":[94],"out":[95],"two":[97],"steps.":[98],"first":[101],"step,":[102,155],"filter":[104],"applied":[106,129],"RGB":[108],"(red,":[109],"green":[110],"blue)":[112],"images":[113,182],"remove":[115],"noise":[116],"converted":[120],"space.":[124],"The":[125,141,171,187,211,216],"k-means":[126],"organize":[131],"all":[132],"colors":[133,159],"contained":[134],"both":[136],"b":[139],"layers.":[140],"pixels":[142,165],"were":[143,233],"clustered":[144],"on":[146],"their":[147],"special":[150],"features.":[151],"second":[154],"variation":[157],"between":[158,218],"measured":[161],"labelling":[163],"completed":[167],"by":[168],"cluster":[169],"index.":[170],"index":[173],"joined":[175],"specific":[178],"region":[179],"segmented":[183,196],"using":[184],"information.":[186],"method":[189,225],"showed":[190],"can":[194,205],"grains":[200,228],"numbers":[202],"area":[204,232],"estimated":[207],"UAV":[209],"images.":[210],"comparison":[212],"provided":[213],"significant":[214],"results.":[215],"correlation":[217],"ground":[220],"truth":[221],"measure":[222],"for":[226],"number":[230],"found":[234],"around":[237],"0.931":[238],"0.842":[240],"respectively.":[241]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2018,"cited_by_count":1}],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-10-10T00:00:00"}