{"id":"https://openalex.org/W4410697909","doi":"https://doi.org/10.1051/shsconf/202521601012","title":"Development of Ai-Based Crop Quality Grading Systems using Image Recognition","display_name":"Development of Ai-Based Crop Quality Grading Systems using Image Recognition","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4410697909","doi":"https://doi.org/10.1051/shsconf/202521601012"},"language":"en","primary_location":{"id":"doi:10.1051/shsconf/202521601012","doi":"https://doi.org/10.1051/shsconf/202521601012","is_oa":true,"landing_page_url":"https://doi.org/10.1051/shsconf/202521601012","pdf_url":"https://www.shs-conferences.org/articles/shsconf/pdf/2025/07/shsconf_iciaites2025_01012.pdf","source":{"id":"https://openalex.org/S4210202797","display_name":"SHS Web of Conferences","issn_l":"2261-2424","issn":["2261-2424","2416-5182"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","jufo-1","norway-1"],"host_organization":"https://openalex.org/P4310319748","host_organization_name":"EDP Sciences","host_organization_lineage":["https://openalex.org/P4310319748","https://openalex.org/P4310319982"],"host_organization_lineage_names":["EDP Sciences","Science Press"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SHS Web of Conferences","raw_type":"journal-article"},"volume":null,"type":"conference-paper","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://www.shs-conferences.org/articles/shsconf/pdf/2025/07/shsconf_iciaites2025_01012.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5116805938","display_name":"Prerna Dusi","orcid":"https://orcid.org/0009-0009-1824-9190","observed_orcids":["https://orcid.org/0009-0009-1824-9190"]},"institutions":[{"id":"https://openalex.org/I2800614057","display_name":"Kalinga University","ror":"https://ror.org/03afg5j45","country_code":"IN","type":"education","lineage":["https://openalex.org/I2800614057"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Prerna Dusi","raw_affiliation_strings":["Department of CS & IT, Kalinga University, Raipur, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of CS & IT, Kalinga University, Raipur, India","institution_ids":["https://openalex.org/I2800614057"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052831895","display_name":"Pooja Sharma","orcid":"https://orcid.org/0000-0001-6380-2455","observed_orcids":["https://orcid.org/0000-0001-6380-2455"]},"institutions":[{"id":"https://openalex.org/I2800614057","display_name":"Kalinga University","ror":"https://ror.org/03afg5j45","country_code":"IN","type":"education","lineage":["https://openalex.org/I2800614057"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Pooja Sharma","raw_affiliation_strings":["Research Scholar, Department of CS & IT, Kalinga University, Raipur, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research Scholar, Department of CS & IT, Kalinga University, Raipur, India","institution_ids":["https://openalex.org/I2800614057"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I2800614057"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.10728622,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"216","issue":null,"first_page":"01012","last_page":"01012"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9932000041007996,"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":0.9932000041007996,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.005799999926239252,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.0006000000284984708,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"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/automated-grading","display_name":"automated grading","score":0.6890000104904175},{"id":"https://openalex.org/keywords/image-recognition","display_name":"image recognition","score":0.8930000066757202},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.8859999775886536},{"id":"https://openalex.org/keywords/transfer-learning","display_name":"transfer learning","score":1.0},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"support vector machine","score":0.968999981880188},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.9950000047683716},{"id":"https://openalex.org/keywords/postharvest-losses","display_name":"post-harvest losses","score":0.7269999980926514},{"id":"https://openalex.org/keywords/agricultural-supply-chain","display_name":"agricultural supply chain","score":0.9430000185966492},{"id":"https://openalex.org/keywords/lighting-conditions","display_name":"lighting conditions","score":0.5839999914169312}],"concepts":[{"id":"https://openalex.org/C2777286243","wikidata":"https://www.wikidata.org/wiki/Q5591926","display_name":"Grading (engineering)","level":2,"score":0.6499998569488525},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.569428563117981},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.551544725894928},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.47305813431739807},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.42699626088142395},{"id":"https://openalex.org/C88463610","wikidata":"https://www.wikidata.org/wiki/Q194118","display_name":"Agricultural engineering","level":1,"score":0.42475658655166626},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42354440689086914},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.27987635135650635},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1770666241645813},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1051/shsconf/202521601012","doi":"https://doi.org/10.1051/shsconf/202521601012","is_oa":true,"landing_page_url":"https://doi.org/10.1051/shsconf/202521601012","pdf_url":"https://www.shs-conferences.org/articles/shsconf/pdf/2025/07/shsconf_iciaites2025_01012.pdf","source":{"id":"https://openalex.org/S4210202797","display_name":"SHS Web of Conferences","issn_l":"2261-2424","issn":["2261-2424","2416-5182"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","jufo-1","norway-1"],"host_organization":"https://openalex.org/P4310319748","host_organization_name":"EDP Sciences","host_organization_lineage":["https://openalex.org/P4310319748","https://openalex.org/P4310319982"],"host_organization_lineage_names":["EDP Sciences","Science Press"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SHS Web of Conferences","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:c87b5706f8b343688f81060531ccdb1b","doi":null,"is_oa":true,"landing_page_url":"https://doaj.org/article/c87b5706f8b343688f81060531ccdb1b","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"SHS Web of Conferences, Vol 216, p 01012 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1051/shsconf/202521601012","doi":"https://doi.org/10.1051/shsconf/202521601012","is_oa":true,"landing_page_url":"https://doi.org/10.1051/shsconf/202521601012","pdf_url":"https://www.shs-conferences.org/articles/shsconf/pdf/2025/07/shsconf_iciaites2025_01012.pdf","source":{"id":"https://openalex.org/S4210202797","display_name":"SHS Web of Conferences","issn_l":"2261-2424","issn":["2261-2424","2416-5182"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","jufo-1","norway-1"],"host_organization":"https://openalex.org/P4310319748","host_organization_name":"EDP Sciences","host_organization_lineage":["https://openalex.org/P4310319748","https://openalex.org/P4310319982"],"host_organization_lineage_names":["EDP Sciences","Science Press"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SHS Web of Conferences","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Zero hunger","score":1.0,"id":"https://metadata.un.org/sdg/2"}],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4410697909.pdf","grobid_xml":"https://content.openalex.org/works/W4410697909.grobid-xml"},"referenced_works_count":7,"referenced_works":["https://openalex.org/W3082823908","https://openalex.org/W3109031550","https://openalex.org/W3168556907","https://openalex.org/W3195326532","https://openalex.org/W3212489200","https://openalex.org/W4386360175","https://openalex.org/W4401164171"],"related_works":["https://openalex.org/W2064165679","https://openalex.org/W1588461101","https://openalex.org/W3208525924","https://openalex.org/W2591833644","https://openalex.org/W2885058781","https://openalex.org/W3127248583","https://openalex.org/W1552490082","https://openalex.org/W4236439135","https://openalex.org/W2189583758","https://openalex.org/W3009904625"],"abstract_inverted_index":{"Crops'":[0],"quality":[1,58,104,203],"assessment":[2],"in":[3],"the":[4,46,63,149,153,159,168,186,210,215],"current":[5],"agricultural":[6,187],"environment":[7],"is":[8,98,118],"still":[9],"a":[10,43,53,175,194,206],"labour":[11],"intensive":[12],"process":[13],"relying":[14],"much":[15],"on":[16,62],"human":[17],"judgment.":[18],"Often":[19],"traditional":[20,198],"crop":[21,57,89,108,202],"grading":[22,35,59,179,204],"methods":[23],"are":[24],"inconsistent":[25],"of":[26,48,65,134,161,209,218],"errors":[27],"and":[28,33,55,83,121,140,145,181,200,213],"tend":[29],"to":[30,45,87,174,197],"be":[31],"inefficient":[32],"suboptimal":[34],"results":[36,165],"which":[37,157],"cause":[38],"costs.":[39],"This":[40],"research":[41],"makes":[42],"contribution":[44],"solution":[47],"these":[49],"challenges":[50],"by":[51],"proposing":[52],"novel":[54],"automated":[56],"system":[60,117,169,183],"based":[61],"use":[64],"advanced":[66],"image":[67],"recognition":[68],"techniques.":[69],"It":[70,190],"also":[71,192],"integrate":[72],"Convolutional":[73],"Neural":[74],"Networks":[75],"(CNN),":[76],"Transfer":[77,127,150],"Learning,":[78],"Support":[79],"Vector":[80],"Machines":[81],"(SVM)":[82],"Random":[84,138],"Forest":[85],"algorithms":[86],"label":[88],"images":[90,101],"into":[91],"pre":[92],"defined":[93],"categories.":[94],"The":[95,116],"data":[96],"given":[97],"10,000":[99],"labeled":[100],"across":[102],"five":[103],"grades":[105],"from":[106],"various":[107],"types":[109],"under":[110],"different":[111],"lighting":[112],"conditions":[113],"for":[114],"robustness.":[115],"then":[119],"evaluated":[120],"doesn't":[122],"perform":[123],"well,":[124],"showing":[125],"that":[126,167],"Learning":[128,151],"outperforms":[129],"other":[130],"baselines":[131],"with":[132,205],"95.8%":[133],"accuracy,":[135],"whereas":[136],"CNN,":[137],"Forest,":[139],"SVM":[141],"get":[142],"92.1%,":[143],"87.4%":[144],"85.9%":[146],"respectively.":[147],"Moreover,":[148],"has":[152,191],"shortest":[154],"training":[155],"time,":[156],"shows":[158],"preference":[160],"this":[162],"approach.":[163],"Such":[164],"indicate":[166],"can":[170],"reduce":[171],"manual":[172],"labor":[173],"large":[176],"extent,":[177],"improve":[178],"precision,":[180],"facilitate":[182],"integration":[184],"within":[185],"supply":[188],"chain.":[189],"offered":[193],"reliable":[195],"alternative":[196],"practices":[199],"standardized":[201],"minimum":[207],"loss":[208],"post":[211],"harvest":[212],"keep":[214],"economic":[216],"outcomes":[217],"stakeholders.":[219]},"counts_by_year":[],"updated_date":"2026-09-21T07:43:02.447757","created_date":"2025-05-24T00:00:00"}