{"id":"https://openalex.org/W6888532040","doi":"https://doi.org/10.21227/hq18-pm58","title":"\"Image-Based Dataset for Identification of Common Diseases in Indian Tea Plants\"","display_name":"\"Image-Based Dataset for Identification of Common Diseases in Indian Tea Plants\"","publication_year":2025,"publication_date":"2025-06-21","ids":{"openalex":"https://openalex.org/W6888532040","doi":"https://doi.org/10.21227/hq18-pm58"},"language":"en","primary_location":{"id":"doi:10.21227/hq18-pm58","is_oa":true,"landing_page_url":"https://doi.org/10.21227/hq18-pm58","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"dataset"},"type":"dataset","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.21227/hq18-pm58","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Punit Kumar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Punit Kumar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Vishnu Sharma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vishnu Sharma","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Dinesh Goyal","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dinesh Goyal","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":null,"topics":[],"keywords":[{"id":"https://openalex.org/keywords/thresholding","display_name":"Thresholding","score":0.618399977684021},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.6068000197410583},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5891000032424927},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5565000176429749},{"id":"https://openalex.org/keywords/histogram-equalization","display_name":"Histogram equalization","score":0.532800018787384},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5325999855995178},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4999000132083893},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.46459999680519104},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.448199987411499}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7991999983787537},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.618399977684021},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6161999702453613},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.6068000197410583},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5891000032424927},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5565000176429749},{"id":"https://openalex.org/C136943445","wikidata":"https://www.wikidata.org/wiki/Q1970240","display_name":"Histogram equalization","level":4,"score":0.532800018787384},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5325999855995178},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4999000132083893},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.46459999680519104},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.448199987411499},{"id":"https://openalex.org/C78201319","wikidata":"https://www.wikidata.org/wiki/Q685727","display_name":"Grayscale","level":3,"score":0.4334999918937683},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.4077000021934509},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3971000015735626},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3515999913215637},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3230000138282776},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3100999891757965},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.28769999742507935},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.27559998631477356},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2712000012397766},{"id":"https://openalex.org/C42781572","wikidata":"https://www.wikidata.org/wiki/Q1250322","display_name":"Digital image","level":4,"score":0.2662999927997589},{"id":"https://openalex.org/C193536780","wikidata":"https://www.wikidata.org/wiki/Q1513153","display_name":"Edge detection","level":4,"score":0.2653999924659729},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.26460000872612},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.2581999897956848},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21227/hq18-pm58","is_oa":true,"landing_page_url":"https://doi.org/10.21227/hq18-pm58","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"dataset"}],"best_oa_location":{"id":"doi:10.21227/hq18-pm58","is_oa":true,"landing_page_url":"https://doi.org/10.21227/hq18-pm58","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"dataset"},"sustainable_development_goals":[{"score":0.7954967617988586,"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":[],"abstract_inverted_index":{"\"Current":[0],"dataset":[1,38,165,181],"comprises":[2],"images":[3,50,77,173],"of":[4,160,176],"Indian":[5],"tea":[6,11,28],"leaves":[7],"collected":[8],"directly":[9],"from":[10],"farms":[12],"(Talap":[13],"Town,":[14],"Tinsukia":[15],"District,":[16],"India),":[17],"intended":[18],"to":[19,80,138,167,192],"support":[20],"machine":[21],"learning":[22,25,93],"and":[23,33,53,65,90,124,128,156,170,189,194,200],"deep":[24,92],"applications":[26],"in":[27,174,197],"plant":[29,201]," leaf":[30],"health":[31],"monitoring":[32],"disease":[34],"detection.":[35],"The":[36,75,130],"current":[37],"is":[39],"organized":[40],"into":[41,103],"three":[42],"distinct":[43],"folders:":[44],"(i)":[45],"01-Tea_Leaf_Dataset":[46],"containing":[47],"raw,":[48],"clean":[49],"(197":[51],"healthy":[52,169],"219":[54],"diseased),":[55],"(ii)":[56],"02-Pre":[57],"processed":[58,69],"Images":[59],"comprising":[60],"seven":[61,104],"classical":[62],"preprocessing":[63],"techniques,":[64],"(iii)":[66],"03-Adv":[67],"Pre":[68],"with":[70],"six":[71,134],"advanced":[72],"augmentation":[73,125],"techniques.":[74],"preprocessed":[76],"were":[78],"designed":[79,137],"optimize":[81],"performance":[82],"for":[83,187],"real-time":[84],"visual":[85],"models":[86,196],"such":[87],"as":[88,183],"YOLO":[89],"other":[91],"frameworks.In":[94],"the":[95,107,164,177],"second":[96],"folder,":[97],"each":[98,175],"original":[99],"image":[100,110,119],"was":[101],"expanded":[102,166],"variants":[105],"using":[106,115,203],"following":[108],"transformations:":[109],"resizing":[111],"(416\\u00d7416),":[112],"histogram":[113],"equalization":[114],"CLAHE,":[116],"Gaussian":[117],"blur,":[118],"normalization,":[120],"edge":[121],"detection":[122],"(Canny),":[123],"via":[126],"flipping":[127],"rotation.":[129],"third":[131],"folder":[132],"includes":[133],"additional":[135],"transformations":[136],"further":[139],"enhance":[140],"model":[141],"robustness:":[142],"grayscale":[143],"conversion,":[144],"black-and-white":[145],"thresholding":[146],"(binary),":[147],"HSV-based":[148],"random":[149,154,157],"color":[150],"jitter,":[151],"gamma":[152],"correction,":[153],"zooming,":[155],"angle":[158],"rotations":[159],"\\u00b130\\u00b0.":[161],"After":[162],"preprocessing,":[163],"1,379":[168],"1,533":[171],"diseased":[172],"enhanced":[178],"folders.":[179],"Current":[180],"serves":[182],"a":[184],"valuable":[185],"resource":[186],"researchers":[188],"practitioners":[190],"aiming":[191],"develop":[193],"benchmark":[195],"precision":[198],"agriculture":[199],"pathology":[202],"computer":[204],"vision.\"":[205]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}