{"id":"https://openalex.org/W7133360527","doi":"https://doi.org/10.1016/j.compag.2026.111621","title":"A machine learning-based lettuce fresh weight estimation framework incorporating agronomic traits and image features","display_name":"A machine learning-based lettuce fresh weight estimation framework incorporating agronomic traits and image features","publication_year":2026,"publication_date":"2026-03-03","ids":{"openalex":"https://openalex.org/W7133360527","doi":"https://doi.org/10.1016/j.compag.2026.111621"},"language":"en","primary_location":{"id":"doi:10.1016/j.compag.2026.111621","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.compag.2026.111621","pdf_url":null,"source":{"id":"https://openalex.org/S116775814","display_name":"Computers and Electronics in Agriculture","issn_l":"0168-1699","issn":["0168-1699","1872-7107"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers and Electronics in Agriculture","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1016/j.compag.2026.111621","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5127965319","display_name":"Feng Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210146959","display_name":"Beijing Academy of Agricultural and Forestry Sciences","ror":"https://ror.org/04trzn023","country_code":"CN","type":"funder","lineage":["https://openalex.org/I4210146959"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Yu","raw_affiliation_strings":["Institute of Data Science and Agricultural Economics, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China","School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China"],"affiliations":[{"raw_affiliation_string":"Institute of Data Science and Agricultural Economics, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China","institution_ids":["https://openalex.org/I4210146959"]},{"raw_affiliation_string":"School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128023771","display_name":"Jun Xiao","orcid":null},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Xiao","raw_affiliation_strings":["School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033545356","display_name":"Xining Zhang","orcid":"https://orcid.org/0000-0003-0906-7386"},"institutions":[{"id":"https://openalex.org/I4210146959","display_name":"Beijing Academy of Agricultural and Forestry Sciences","ror":"https://ror.org/04trzn023","country_code":"CN","type":"funder","lineage":["https://openalex.org/I4210146959"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xining Zhang","raw_affiliation_strings":["Institute of Data Science and Agricultural Economics, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"],"affiliations":[{"raw_affiliation_string":"Institute of Data Science and Agricultural Economics, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China","institution_ids":["https://openalex.org/I4210146959"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007196875","display_name":"Jinmeng Zhang","orcid":"https://orcid.org/0009-0005-6666-7646"},"institutions":[{"id":"https://openalex.org/I4210146959","display_name":"Beijing Academy of Agricultural and Forestry Sciences","ror":"https://ror.org/04trzn023","country_code":"CN","type":"funder","lineage":["https://openalex.org/I4210146959"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jinmeng Zhang","raw_affiliation_strings":["Institute of Data Science and Agricultural Economics, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"],"affiliations":[{"raw_affiliation_string":"Institute of Data Science and Agricultural Economics, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China","institution_ids":["https://openalex.org/I4210146959"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127890123","display_name":"Ming Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210146959","display_name":"Beijing Academy of Agricultural and Forestry Sciences","ror":"https://ror.org/04trzn023","country_code":"CN","type":"funder","lineage":["https://openalex.org/I4210146959"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ming Wang","raw_affiliation_strings":["Institute of Data Science and Agricultural Economics, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"],"affiliations":[{"raw_affiliation_string":"Institute of Data Science and Agricultural Economics, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China","institution_ids":["https://openalex.org/I4210146959"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019501903","display_name":"RuPeng Luan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210146959","display_name":"Beijing Academy of Agricultural and Forestry Sciences","ror":"https://ror.org/04trzn023","country_code":"CN","type":"funder","lineage":["https://openalex.org/I4210146959"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rupeng Luan","raw_affiliation_strings":["Institute of Data Science and Agricultural Economics, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"],"affiliations":[{"raw_affiliation_string":"Institute of Data Science and Agricultural Economics, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China","institution_ids":["https://openalex.org/I4210146959"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125055879","display_name":"Yuhan Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210146959","display_name":"Beijing Academy of Agricultural and Forestry Sciences","ror":"https://ror.org/04trzn023","country_code":"CN","type":"funder","lineage":["https://openalex.org/I4210146959"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuhan Wang","raw_affiliation_strings":["Institute of Data Science and Agricultural Economics, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"],"affiliations":[{"raw_affiliation_string":"Institute of Data Science and Agricultural Economics, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China","institution_ids":["https://openalex.org/I4210146959"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127923034","display_name":"Yuntao Ma","orcid":null},"institutions":[{"id":"https://openalex.org/I52158045","display_name":"China Agricultural University","ror":"https://ror.org/04v3ywz14","country_code":"CN","type":"education","lineage":["https://openalex.org/I52158045"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuntao Ma","raw_affiliation_strings":["College of Land Science and Technology, China Agricultural University, Beijing 100193, China"],"affiliations":[{"raw_affiliation_string":"College of Land Science and Technology, China Agricultural University, Beijing 100193, China","institution_ids":["https://openalex.org/I52158045"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5128035718","display_name":"Qian Yu Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210146959","display_name":"Beijing Academy of Agricultural and Forestry Sciences","ror":"https://ror.org/04trzn023","country_code":"CN","type":"funder","lineage":["https://openalex.org/I4210146959"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qian Zhang","raw_affiliation_strings":["Institute of Data Science and Agricultural Economics, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"],"affiliations":[{"raw_affiliation_string":"Institute of Data Science and Agricultural Economics, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China","institution_ids":["https://openalex.org/I4210146959"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5007196875","https://openalex.org/A5033545356","https://openalex.org/A5128035718"],"corresponding_institution_ids":["https://openalex.org/I4210146959"],"apc_list":{"value":3680,"currency":"USD","value_usd":3680},"apc_paid":{"value":3680,"currency":"USD","value_usd":3680},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.92704121,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":"246","issue":null,"first_page":"111621","last_page":"111621"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9176999926567078,"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":0.9176999926567078,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.02239999920129776,"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"}},{"id":"https://openalex.org/T13391","display_name":"Innovations in Aquaponics and Hydroponics Systems","score":0.014800000004470348,"subfield":{"id":"https://openalex.org/subfields/1104","display_name":"Aquatic 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/artificial-neural-network","display_name":"Artificial neural network","score":0.6025000214576721},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.529699981212616},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.5160999894142151},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.42800000309944153},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4268999993801117},{"id":"https://openalex.org/keywords/weight-estimation","display_name":"Weight estimation","score":0.3885999917984009},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.31679999828338623}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6216999888420105},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6129999756813049},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6025000214576721},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.529699981212616},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.5160999894142151},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.45249998569488525},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.42800000309944153},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4268999993801117},{"id":"https://openalex.org/C3019530228","wikidata":"https://www.wikidata.org/wiki/Q620876","display_name":"Weight estimation","level":2,"score":0.3885999917984009},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.31679999828338623},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.30809998512268066},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.30640000104904175},{"id":"https://openalex.org/C5339829","wikidata":"https://www.wikidata.org/wiki/Q1425977","display_name":"Machine vision","level":2,"score":0.29649999737739563},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.289900004863739},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2815000116825104},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.2757999897003174},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.26570001244544983},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2515000104904175}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.compag.2026.111621","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.compag.2026.111621","pdf_url":null,"source":{"id":"https://openalex.org/S116775814","display_name":"Computers and Electronics in Agriculture","issn_l":"0168-1699","issn":["0168-1699","1872-7107"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers and Electronics in Agriculture","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1016/j.compag.2026.111621","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.compag.2026.111621","pdf_url":null,"source":{"id":"https://openalex.org/S116775814","display_name":"Computers and Electronics in Agriculture","issn_l":"0168-1699","issn":["0168-1699","1872-7107"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers and Electronics in Agriculture","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.8171491622924805,"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":43,"referenced_works":["https://openalex.org/W2000374050","https://openalex.org/W2032734097","https://openalex.org/W2033233667","https://openalex.org/W2058164160","https://openalex.org/W2063795323","https://openalex.org/W2073929666","https://openalex.org/W2082353956","https://openalex.org/W2528491735","https://openalex.org/W2591569502","https://openalex.org/W2891950633","https://openalex.org/W2894202761","https://openalex.org/W2912571849","https://openalex.org/W2919115771","https://openalex.org/W2994844732","https://openalex.org/W3008439211","https://openalex.org/W3015117847","https://openalex.org/W3043033734","https://openalex.org/W3046517933","https://openalex.org/W3093134863","https://openalex.org/W3146049777","https://openalex.org/W3155013308","https://openalex.org/W3171520305","https://openalex.org/W3192482461","https://openalex.org/W3211269036","https://openalex.org/W4220694250","https://openalex.org/W4287219550","https://openalex.org/W4288040257","https://openalex.org/W4293089594","https://openalex.org/W4297459889","https://openalex.org/W4307055615","https://openalex.org/W4309740001","https://openalex.org/W4385259923","https://openalex.org/W4385834724","https://openalex.org/W4385949596","https://openalex.org/W4386844030","https://openalex.org/W4387204687","https://openalex.org/W4387638497","https://openalex.org/W4389189949","https://openalex.org/W4389675256","https://openalex.org/W4400314777","https://openalex.org/W4401945350","https://openalex.org/W4402495595","https://openalex.org/W4403391534"],"related_works":[],"abstract_inverted_index":{"\u2022":[0,10,23,36],"Constructing":[1],"a":[2,83,95,147],"machine":[3,25,79,105,120,148,179],"learning-based":[4,149],"lettuce":[5,88,112,152],"fresh":[6,47,89,113,153,194],"weight":[7,48,51,114,154],"estimation":[8,155,246],"framework.":[9,22],"Agronomic":[11],"traits":[12,158,241],"and":[13,30,69,159,202,208,252],"image":[14,160,250],"features":[15,161,232],"jointly":[16],"serve":[17],"as":[18,78,162],"inputs":[19,138],"to":[20,142,225,244,249],"the":[21,33,42,58,101,109,117,127,130,177,183,189,198,206,219,228,234,245,253,258,268,272,284,287],"7":[24],"learning":[26,106,121,180],"models":[27,107,122,133,181,212],"are":[28,123,139],"compared,":[29],"DNN":[31,207],"exhibits":[32,188],"highest":[34,190],"performance.":[35],"The":[37,63,236,262],"number":[38,254],"of":[39,61,65,97,103,111,151,176,230,255,266,286],"leaves":[40,256],"is":[41,52,93,257,264],"most":[43,259],"dominant":[44,260,269],"factor":[45],"affecting":[46],"estimation.":[49,115],"Fresh":[50],"an":[53],"important":[54],"indicator":[55],"for":[56,86],"quantifying":[57],"growth":[59],"state":[60],"lettuce.":[62],"utilization":[64],"raw":[66],"RGB":[67],"images":[68],"multidimensional":[70],"features,":[71,251],"in":[72,108,192],"conjunction":[73],"with":[74,197,213],"advanced":[75],"techniques":[76],"such":[77],"learning,":[80],"has":[81],"become":[82],"popular":[84],"trend":[85],"estimating":[87,193],"weight.":[90],"However,":[91],"there":[92],"currently":[94],"lack":[96],"comprehensive":[98],"studies":[99],"comparing":[100],"performance":[102],"various":[104],"task":[110],"Moreover,":[116],"fact":[118],"that":[119,126,239],"data-driven":[124],"means":[125],"mechanisms":[128],"behind":[129],"decisions":[131],"these":[132],"make":[134],"based":[135],"on":[136,233],"multi-source":[137],"not":[140,276],"easy":[141],"explain.":[143],"In":[144],"this":[145],"study,":[146],"framework":[150,263],"taking":[156],"agronomic":[157,240],"input":[163,231],"was":[164],"developed":[165],"using":[166],"two":[167],"self-made":[168],"datasets.":[169],"Our":[170],"comparative":[171],"study":[172],"reveals":[173],"that,":[174],"out":[175],"seven":[178],"evaluated,":[182],"deep":[184],"neural":[185],"network":[186],"(DNN)":[187],"accuracy":[191],"weight,":[195],"coupled":[196],"strongest":[199],"model":[200],"stability":[201],"cross-variety":[203],"applicability.":[204],"For":[205],"extra":[209],"trees":[210],"(ET)":[211],"relatively":[214],"superior":[215],"performance,":[216],"we":[217],"implement":[218],"Shapley":[220],"additive":[221],"explanations":[222],"(SHAP)":[223],"algorithms":[224],"quantitatively":[226],"analyze":[227],"impact":[229],"models.":[235],"results":[237,247],"indicate":[238],"contribute":[242],"more":[243],"compared":[248],"factor.":[261],"capable":[265],"identifying":[267],"factors":[270],"influencing":[271],"model\u2019s":[273,288],"output,":[274],"providing":[275],"only":[277],"highly":[278],"accurate":[279],"estimations":[280],"but":[281],"also":[282],"enhancing":[283],"understanding":[285],"decision":[289],"making.":[290]},"counts_by_year":[],"updated_date":"2026-03-05T07:30:30.508283","created_date":"2026-03-04T00:00:00"}
