{"id":"https://openalex.org/W2991205842","doi":"https://doi.org/10.1145/3365109.3368766","title":"Estimating Fruit Crop Yield through Deep Learning","display_name":"Estimating Fruit Crop Yield through Deep Learning","publication_year":2019,"publication_date":"2019-11-27","ids":{"openalex":"https://openalex.org/W2991205842","doi":"https://doi.org/10.1145/3365109.3368766","mag":"2991205842"},"language":"en","primary_location":{"id":"doi:10.1145/3365109.3368766","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3365109.3368766","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 6th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies","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/A5026745740","display_name":"Huaqing Yu","orcid":"https://orcid.org/0000-0001-8538-7811"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"The University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Huaqing Yu","raw_affiliation_strings":["University of Melbourne, Melbourne, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Melbourne, Melbourne, Australia","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079431316","display_name":"Shining Song","orcid":null},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"The University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Shining Song","raw_affiliation_strings":["University of Melbourne, Melbourne, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Melbourne, Melbourne, Australia","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019529668","display_name":"Shaoxi Ma","orcid":null},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"The University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Shaoxi Ma","raw_affiliation_strings":["University of Melbourne, Melbourne, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Melbourne, Melbourne, Australia","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039872140","display_name":"Richard Sinnott","orcid":"https://orcid.org/0000-0001-5998-222X"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"The University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Richard O. Sinnott","raw_affiliation_strings":["University of Melbourne, Melbourne, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Melbourne, Melbourne, Australia","institution_ids":["https://openalex.org/I165779595"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I165779595"],"apc_list":null,"apc_paid":null,"fwci":1.9133,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.88114507,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"145","last_page":"148"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9713000059127808,"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/T14365","display_name":"Leaf Properties and Growth Measurement","score":0.9553999900817871,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.782209038734436},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7692236304283142},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7226722836494446},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6827065944671631},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5785821080207825},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5711862444877625},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5092204809188843},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4914137125015259},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4805362820625305},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.4192982017993927},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.4158655107021332},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3939659595489502},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15903684496879578}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.782209038734436},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7692236304283142},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7226722836494446},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6827065944671631},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5785821080207825},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5711862444877625},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5092204809188843},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4914137125015259},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4805362820625305},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.4192982017993927},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4158655107021332},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3939659595489502},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15903684496879578},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"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},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3365109.3368766","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3365109.3368766","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 6th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/2","score":0.5,"display_name":"Zero hunger"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W2046875449","https://openalex.org/W2095654367","https://openalex.org/W2159564241","https://openalex.org/W2555576940","https://openalex.org/W2594258618","https://openalex.org/W2625219738","https://openalex.org/W2772515592","https://openalex.org/W2953106684","https://openalex.org/W3106250896"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W3029198973","https://openalex.org/W2949096641","https://openalex.org/W2970686063","https://openalex.org/W2969228573","https://openalex.org/W3034745255"],"abstract_inverted_index":{"Deep":[0],"learning":[1],"can":[2,24,35,55],"bring":[3],"significant":[4],"improvements":[5],"to":[6,61,121,168],"a":[7,104,109],"range":[8,110],"of":[9,19,49,51,74,96,111,125,140],"research":[10],"areas":[11,22],"and":[12,33,83,94,129,144,152],"application":[13],"domains.":[14],"Computer":[15],"vision":[16],"is":[17],"one":[18],"the":[20,47,75,97,119,123,130,137,149,161],"key":[21],"that":[23,54,132],"benefit":[25,36],"from":[26,37],"deep":[27],"learning.":[28],"In":[29,40,99],"particular,":[30],"object":[31],"detection":[32],"classification":[34],"such":[38],"approaches.":[39],"this":[41,100,115,166],"work":[42,167],"we":[43,69,102,117],"focus":[44],"on":[45,90,157],"counting":[46,155],"number":[48,124],"images":[50,107,128],"individual":[52],"fruit":[53,59,63,112,126,156,175],"be":[56],"used":[57],"by":[58],"growers":[60],"estimate":[62],"crop":[64],"yields.":[65],"To":[66],"achieve":[67],"this,":[68],"apply":[70,118],"two":[71],"different":[72],"state":[73],"art":[76],"convolutional":[77],"neural":[78],"networks":[79],"(CNNs):":[80],"Faster":[81],"R-CNN":[82],"Single":[84],"Shot":[85],"Detection":[86],"(SSD).":[87],"CNNs":[88],"depend":[89],"data":[91],"for":[92,108],"training":[93],"tuning":[95],"models.":[98],"paper":[101],"establish":[103],"dataset":[105],"containing":[106],"types.":[113],"Using":[114],"data,":[116],"models":[120],"identify":[122],"in":[127,164],"challenges":[131,163],"are":[133],"encountered.":[134],"We":[135,159],"present":[136],"experimental":[138],"results":[139],"applying":[141],"these":[142],"approaches":[143],"illustrate":[145],"their":[146],"performance":[147],"including":[148],"accuracy,":[150],"time":[151],"loss":[153],"when":[154],"trees.":[158],"consider":[160],"future":[162],"scaling":[165],"deal":[169],"with":[170],"more":[171],"complex":[172],"issues":[173],"around":[174],"estimation":[176],"at":[177],"scale.":[178]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3}],"updated_date":"2026-07-03T08:13:44.112507","created_date":"2025-10-10T00:00:00"}