{"id":"https://openalex.org/W4417169027","doi":"https://doi.org/10.1109/access.2025.3641954","title":"FeaFusion-PomoNet: A Feature Fusion Driven Regression Framework for Non-Destruction Weight Estimation of Pomegranates","display_name":"FeaFusion-PomoNet: A Feature Fusion Driven Regression Framework for Non-Destruction Weight Estimation of Pomegranates","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4417169027","doi":"https://doi.org/10.1109/access.2025.3641954"},"language":null,"primary_location":{"id":"doi:10.1109/access.2025.3641954","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3641954","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3641954","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5087826135","display_name":"K R Bhavya","orcid":"https://orcid.org/0000-0001-8113-2045"},"institutions":[{"id":"https://openalex.org/I885392262","display_name":"GITAM University","ror":"https://ror.org/0440p1d37","country_code":"IN","type":"education","lineage":["https://openalex.org/I885392262"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"K. R. Bhavya","raw_affiliation_strings":["Department of Computer Science and Engineering, MURTI Research Centre, Smart Agriculture Laboratory, GITAM School of Technology, GITAM (Deemed to be University), Bengaluru, India","Department of Computer Science and Engineering, MURTI Research Centre, Smart Agriculture Labs, GITAM School of Technology, GITAM (Deemed to be University), Bengaluru, India"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, MURTI Research Centre, Smart Agriculture Laboratory, GITAM School of Technology, GITAM (Deemed to be University), Bengaluru, India","institution_ids":["https://openalex.org/I885392262"]},{"raw_affiliation_string":"Department of Computer Science and Engineering, MURTI Research Centre, Smart Agriculture Labs, GITAM School of Technology, GITAM (Deemed to be University), Bengaluru, India","institution_ids":["https://openalex.org/I885392262"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019091201","display_name":"Raghavendra M Devadas","orcid":"https://orcid.org/0000-0002-6625-2986"},"institutions":[{"id":"https://openalex.org/I164861460","display_name":"Manipal Academy of Higher Education","ror":"https://ror.org/02xzytt36","country_code":"IN","type":"education","lineage":["https://openalex.org/I164861460"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Raghavendra M. Devadas","raw_affiliation_strings":["Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India"],"affiliations":[{"raw_affiliation_string":"Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India","institution_ids":["https://openalex.org/I164861460"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010229047","display_name":"N. Shobha Rani","orcid":"https://orcid.org/0000-0003-4882-1919"},"institutions":[{"id":"https://openalex.org/I885392262","display_name":"GITAM University","ror":"https://ror.org/0440p1d37","country_code":"IN","type":"education","lineage":["https://openalex.org/I885392262"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"N. Shobha Rani","raw_affiliation_strings":["Department of Artificial Intelligence and Data Science, MURTI Research Centre, Smart Agriculture Laboratory, GITAM School of Technology, GITAM (Deemed to be University), Bengaluru, India","Smart Agriculture Lab Department of Artificial Intelligence and Data Science, MURTI Research Centre, GITAM School of Technology, GITAM (Deemed to be University), Bengaluru, India"],"affiliations":[{"raw_affiliation_string":"Department of Artificial Intelligence and Data Science, MURTI Research Centre, Smart Agriculture Laboratory, GITAM School of Technology, GITAM (Deemed to be University), Bengaluru, India","institution_ids":["https://openalex.org/I885392262"]},{"raw_affiliation_string":"Smart Agriculture Lab Department of Artificial Intelligence and Data Science, MURTI Research Centre, GITAM School of Technology, GITAM (Deemed to be University), Bengaluru, India","institution_ids":["https://openalex.org/I885392262"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032846023","display_name":"Manjunath Varchagall","orcid":"https://orcid.org/0000-0001-8617-8472"},"institutions":[{"id":"https://openalex.org/I48018076","display_name":"Christ University","ror":"https://ror.org/022tv9y30","country_code":"IN","type":"education","lineage":["https://openalex.org/I48018076"]},{"id":"https://openalex.org/I106826634","display_name":"Jain University","ror":"https://ror.org/01cnqpt53","country_code":"IN","type":"education","lineage":["https://openalex.org/I106826634"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Manjunath Varchagall","raw_affiliation_strings":["SVYASA Deemed to be University, School of Engineering and Technology, Bangalore, India","School of Computer Science and Engineering, RV University, Bengaluru, India"],"affiliations":[{"raw_affiliation_string":"SVYASA Deemed to be University, School of Engineering and Technology, Bangalore, India","institution_ids":["https://openalex.org/I106826634","https://openalex.org/I48018076"]},{"raw_affiliation_string":"School of Computer Science and Engineering, RV University, Bengaluru, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103129598","display_name":"Akshatha Prabhu","orcid":"https://orcid.org/0009-0008-4432-9875"},"institutions":[{"id":"https://openalex.org/I81556334","display_name":"Amrita Vishwa Vidyapeetham","ror":"https://ror.org/03am10p12","country_code":"IN","type":"education","lineage":["https://openalex.org/I81556334"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Akshatha Prabhu","raw_affiliation_strings":["Department of Computer Science, School of Computing, Amrita Vishwa Vidyapeetham, Mysuru, India"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, School of Computing, Amrita Vishwa Vidyapeetham, Mysuru, India","institution_ids":["https://openalex.org/I81556334"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111001950","display_name":"Renuka R Patil","orcid":null},"institutions":[{"id":"https://openalex.org/I3195475088","display_name":"Rashtreeya Sikshana Samithi Trust","ror":"https://ror.org/03abjmr84","country_code":"IN","type":"education","lineage":["https://openalex.org/I3195475088"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Renuka R. Herakal Patil","raw_affiliation_strings":["Department of CSE, GST, Bengaluru, India"],"affiliations":[{"raw_affiliation_string":"Department of CSE, GST, Bengaluru, India","institution_ids":["https://openalex.org/I3195475088"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5087826135"],"corresponding_institution_ids":["https://openalex.org/I885392262"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.29777904,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":null,"first_page":"213578","last_page":"213599"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.8357999920845032,"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":0.8357999920845032,"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":0.06930000334978104,"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.007799999788403511,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7680000066757202},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.6395000219345093},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.6061999797821045},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5727999806404114},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.5655999779701233},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5601000189781189},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.49000000953674316},{"id":"https://openalex.org/keywords/mean-absolute-error","display_name":"Mean absolute error","score":0.45190000534057617},{"id":"https://openalex.org/keywords/correlation-coefficient","display_name":"Correlation coefficient","score":0.4230000078678131}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7680000066757202},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.6395000219345093},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.6061999797821045},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5727999806404114},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.5655999779701233},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5612000226974487},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5601000189781189},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.49000000953674316},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48559999465942383},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.47749999165534973},{"id":"https://openalex.org/C188154048","wikidata":"https://www.wikidata.org/wiki/Q6803609","display_name":"Mean absolute error","level":3,"score":0.45190000534057617},{"id":"https://openalex.org/C2780092901","wikidata":"https://www.wikidata.org/wiki/Q3433612","display_name":"Correlation coefficient","level":2,"score":0.4230000078678131},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.41819998621940613},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.39309999346733093},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.37540000677108765},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.34630000591278076},{"id":"https://openalex.org/C70259352","wikidata":"https://www.wikidata.org/wiki/Q1847839","display_name":"Robust regression","level":3,"score":0.3400999903678894},{"id":"https://openalex.org/C122383733","wikidata":"https://www.wikidata.org/wiki/Q865920","display_name":"Approximation error","level":2,"score":0.32839998602867126},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.3280999958515167},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.32010000944137573},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.314300000667572},{"id":"https://openalex.org/C55078378","wikidata":"https://www.wikidata.org/wiki/Q1136628","display_name":"Pearson product-moment correlation coefficient","level":2,"score":0.3125},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.31220000982284546},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.29350000619888306},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.2849999964237213},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27059999108314514},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.2687999904155731},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2669999897480011},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.2667999863624573},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.26420000195503235},{"id":"https://openalex.org/C60316415","wikidata":"https://www.wikidata.org/wiki/Q6664520","display_name":"Local regression","level":4,"score":0.25999999046325684}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/access.2025.3641954","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3641954","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3641954","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3641954","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W339535247","https://openalex.org/W816175869","https://openalex.org/W1996379420","https://openalex.org/W2003269168","https://openalex.org/W2006730773","https://openalex.org/W2090803973","https://openalex.org/W2800675764","https://openalex.org/W2888043900","https://openalex.org/W2896649574","https://openalex.org/W2948976553","https://openalex.org/W2961933561","https://openalex.org/W2993079219","https://openalex.org/W2997468095","https://openalex.org/W3085475957","https://openalex.org/W3103831163","https://openalex.org/W3162227527","https://openalex.org/W4220996389","https://openalex.org/W4226049419","https://openalex.org/W4304759223","https://openalex.org/W4313731782","https://openalex.org/W4319345239","https://openalex.org/W4394627410","https://openalex.org/W4401417355","https://openalex.org/W4403308855","https://openalex.org/W4406052237","https://openalex.org/W4406261968","https://openalex.org/W4407281142","https://openalex.org/W4409249386"],"related_works":[],"abstract_inverted_index":{"Non-destructive":[0],"fruit":[1],"weight":[2,32],"estimation":[3],"plays":[4],"a":[5,22,36],"critical":[6],"role":[7],"in":[8],"precision":[9],"agriculture,":[10],"particularly":[11],"for":[12,29],"yield":[13],"forecasting,":[14],"grading,":[15],"and":[16,52,56,83,87,98,111,154],"logistics":[17],"optimization.":[18],"<italic":[19],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[20],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">FeaFusion-PomoNet</i>,":[21],"feature":[23,61],"fusion-based":[24],"regression":[25,65],"framework,":[26],"is":[27],"proposed":[28],"estimating":[30],"the":[31,59,75,124,127,149],"of":[33,81,109,121,126],"fruits":[34],"using":[35],"self-collected":[37],"image":[38,134],"dataset":[39],"comprising":[40],"samples":[41],"captured":[42],"from":[43],"multiple":[44],"orientations.":[45],"Handcrafted":[46],"features\u2014including":[47],"texture,":[48],"shape,":[49],"geometric":[50],"attributes,":[51],"pixel":[53],"density\u2014are":[54],"extracted":[55],"optimized":[57],"via":[58],"Boruta":[60],"selection":[62],"algorithm.":[63],"Multiple":[64,70],"models":[66],"are":[67],"evaluated,":[68],"with":[69],"Linear":[71],"Regression":[72],"(MLR)":[73],"achieving":[74],"best":[76],"performance,":[77],"yielding":[78],"R\u00b2":[79],"scores":[80],"0.97":[82],"0.92":[84],"on":[85,147],"80-20":[86],"70-30":[88],"train-test":[89],"splits,":[90],"respectively.":[91],"Validation":[92],"through":[93],"Mean":[94,100],"Absolute":[95],"Error":[96,102],"(MAE)":[97],"Root":[99],"Squared":[101],"(RMSE)":[103],"produces":[104],"average":[105],"relative":[106],"error":[107],"rates":[108],"7.6%":[110],"8.3%,":[112],"indicating":[113],"high":[114],"predictive":[115],"accuracy.":[116],"A":[117],"Pearson":[118],"correlation":[119],"coefficient":[120],"0.99":[122],"confirms":[123],"robustness":[125,156],"model.":[128],"The":[129],"method":[130],"also":[131],"supports":[132],"orientation-independent":[133],"acquisition,":[135],"making":[136],"it":[137],"adaptable":[138],"to":[139,161],"irregularly":[140],"shaped":[141],"fruits.":[142],"Future":[143],"work":[144],"will":[145],"focus":[146],"scaling":[148],"dataset,":[150],"incorporating":[151],"field-captured":[152],"images,":[153],"improving":[155],"under":[157],"real-world":[158],"conditions,":[159],"contributing":[160],"automated,":[162],"cost-effective":[163],"post-harvest":[164],"systems.":[165]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-12-09T00:00:00"}
