{"id":"https://openalex.org/W7204657652","doi":"https://doi.org/10.5281/zenodo.22154291","title":"QUANTIZED MOBILENETV2 FOR TOMATO LEAF DISEASE CLASSIFICATION TARGETING AN ESP32-CAM MICROCONTROLLER","display_name":"QUANTIZED MOBILENETV2 FOR TOMATO LEAF DISEASE CLASSIFICATION TARGETING AN ESP32-CAM MICROCONTROLLER","publication_year":2026,"publication_date":"2026-08-29","ids":{"openalex":"https://openalex.org/W7204657652","doi":"https://doi.org/10.5281/zenodo.22154291"},"language":"en","primary_location":{"id":"pmh:oai:ojs2.houghtonstreetreview.com:article/1654","doi":null,"is_oa":true,"landing_page_url":"https://houghtonstreetreview.com/index.php/index/article/view/1654","pdf_url":null,"source":{"id":"https://openalex.org/S7407075273","display_name":"HSR (London)","issn_l":"2058-5519","issn":["2058-5519"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2058-5519","raw_type":"info:eu-repo/semantics/article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://houghtonstreetreview.com/index.php/index/article/view/1654","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139509027","display_name":"Reyimberganov Baxrom Rashid o'g'li","orcid":"https://orcid.org/0009-0005-8124-0042","observed_orcids":["https://orcid.org/0009-0005-8124-0042"]},"institutions":[{"id":"https://openalex.org/I14916453","display_name":"Urgench State University","ror":"https://ror.org/0593kfr97","country_code":"UZ","type":"education","lineage":["https://openalex.org/I14916453"]}],"countries":["UZ"],"is_corresponding":false,"raw_author_name":"Baxrom R. Reyimberganov","raw_affiliation_strings":["Department of Computer Science, Urgench State University, Urgench, Uzbekistan *Corresponding author: Baxrom R. Reyimberganov"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Urgench State University, Urgench, Uzbekistan *Corresponding author: Baxrom R. Reyimberganov","institution_ids":["https://openalex.org/I14916453"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5122412260","display_name":"Worldly Knowledge Publishing Centre","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Worldly Knowledge Publishing Centre","raw_affiliation_strings":["Worldly Knowledge Publishing Centre"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Worldly Knowledge Publishing Centre","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9984999895095825,"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":0.9984999895095825,"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":0.0007999999797903001,"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/T10273","display_name":"IoT and Edge/Fog Computing","score":0.00019999999494757503,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/tomato-leaf-disease-classification","display_name":"tomato leaf disease classification","score":0.9819999933242798},{"id":"https://openalex.org/keywords/mobilenetv2","display_name":"MobileNetV2","score":0.9440000057220459},{"id":"https://openalex.org/keywords/post-training-quantization","display_name":"post-training quantization","score":0.9120000004768372},{"id":"https://openalex.org/keywords/int8-quantization","display_name":"INT8 quantization","score":0.8889999985694885},{"id":"https://openalex.org/keywords/esp32-cam","display_name":"ESP32-CAM","score":0.8700000047683716},{"id":"https://openalex.org/keywords/edge-intelligence","display_name":"edge AI","score":0.9319999814033508},{"id":"https://openalex.org/keywords/plant-health-assessment","display_name":"plant health assessment","score":0.8270000219345093},{"id":"https://openalex.org/keywords/greenhouse-monitoring","display_name":"greenhouse monitoring","score":0.828000009059906},{"id":"https://openalex.org/keywords/mqtt","display_name":"MQTT","score":0.49000000953674316},{"id":"https://openalex.org/keywords/plant-disease-detection","display_name":"plant disease detection","score":0.6399999856948853}],"concepts":[{"id":"https://openalex.org/C173018170","wikidata":"https://www.wikidata.org/wiki/Q165678","display_name":"Microcontroller","level":2,"score":0.7365999817848206},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.6013000011444092},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5817999839782715},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5182999968528748},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5131999850273132},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4066999852657318},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.38089999556541443},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.37630000710487366},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.352400004863739},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3334999978542328},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.3312999904155731},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.3262999951839447},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.32600000500679016},{"id":"https://openalex.org/C32198211","wikidata":"https://www.wikidata.org/wiki/Q165044","display_name":"Greenhouse","level":2,"score":0.3197000026702881},{"id":"https://openalex.org/C132868160","wikidata":"https://www.wikidata.org/wiki/Q10322548","display_name":"MQTT","level":3,"score":0.3070000112056732},{"id":"https://openalex.org/C106251023","wikidata":"https://www.wikidata.org/wiki/Q851989","display_name":"Porting","level":3,"score":0.2930999994277954},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.290800005197525},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.28349998593330383},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2775000035762787},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2757999897003174},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.2754000127315521},{"id":"https://openalex.org/C193536780","wikidata":"https://www.wikidata.org/wiki/Q1513153","display_name":"Edge detection","level":4,"score":0.27129998803138733},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2678000032901764},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.26649999618530273},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.2632000148296356},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.26030001044273376},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2526000142097473},{"id":"https://openalex.org/C125907379","wikidata":"https://www.wikidata.org/wiki/Q4851537","display_name":"Ballast","level":2,"score":0.25049999356269836},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.2502000033855438}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:ojs2.houghtonstreetreview.com:article/1654","doi":null,"is_oa":true,"landing_page_url":"https://houghtonstreetreview.com/index.php/index/article/view/1654","pdf_url":null,"source":{"id":"https://openalex.org/S7407075273","display_name":"HSR (London)","issn_l":"2058-5519","issn":["2058-5519"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2058-5519","raw_type":"info:eu-repo/semantics/article"},{"id":"doi:10.5281/zenodo.22154291","doi":"https://doi.org/10.5281/zenodo.22154291","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.22154291","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"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"},{"id":"doi:10.5281/zenodo.22154292","doi":"https://doi.org/10.5281/zenodo.22154292","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.22154292","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"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"}],"best_oa_location":{"id":"pmh:oai:ojs2.houghtonstreetreview.com:article/1654","doi":null,"is_oa":true,"landing_page_url":"https://houghtonstreetreview.com/index.php/index/article/view/1654","pdf_url":null,"source":{"id":"https://openalex.org/S7407075273","display_name":"HSR (London)","issn_l":"2058-5519","issn":["2058-5519"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2058-5519","raw_type":"info:eu-repo/semantics/article"},"sustainable_development_goals":[{"display_name":"Zero hunger","id":"https://metadata.un.org/sdg/2","score":0.8409000039100647},{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.4943999946117401}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7213781816","https://openalex.org/W4417156187","https://openalex.org/W4386883226","https://openalex.org/W7201887364","https://openalex.org/W7223567521","https://openalex.org/W7172340434","https://openalex.org/W3095388995","https://openalex.org/W7163004605","https://openalex.org/W4414630918","https://openalex.org/W7162416973"],"abstract_inverted_index":{"Early":[0],"detection":[1,29],"of":[2,112],"plant":[3,144],"diseases":[4],"is":[5,77,98,219],"critical":[6],"for":[7,26,84,208],"food":[8],"security,":[9],"yet":[10],"most":[11],"deep":[12],"learning":[13],"models":[14],"require":[15],"GPU-class":[16],"hardware.":[17],"This":[18,107],"paper":[19],"presents":[20],"an":[21,32],"end-to-end":[22],"edge":[23],"AI":[24],"system":[25,193,222],"tomato":[27,43],"disease":[28],"deployed":[30,78],"on":[31,41,79,114,231],"ESP32-CAM":[33,80,234],"microcontroller":[34],"($7":[35],"USD).":[36],"We":[37],"train":[38],"MobileNetV2":[39,113],"(\u03b1=0.35)":[40],"six":[42],"leaf":[44],"classes":[45],"from":[46,61],"PlantVillage":[47],"(10,431":[48],"images),":[49],"achieving":[50],"91.57%":[51],"test":[52],"accuracy.":[53],"Full":[54],"INT8":[55],"post-training":[56],"quantization":[57],"compresses":[58],"the":[59,109,115,186,220,232],"model":[60,76],"1,588":[62],"KB":[63,66,89,174,181],"to":[64,140],"614":[65],"(2.6\u00d7":[67],"reduction)":[68],"with":[69,86,131,152,227],"1.41":[70],"percentage-point":[71],"accuracy":[72],"loss.":[73],"The":[74,157,192],"quantized":[75,224],"using":[81],"TensorFlow":[82],"Lite":[83],"Microcontrollers":[85],"a":[87,142,153,168],"220":[88],"tensor":[90,175],"arena":[91,176],"in":[92,177],"PSRAM.":[93],"Measured":[94],"on-device":[95],"inference":[96,202,226],"latency":[97],"approximately":[99,172],"8.7":[100],"seconds":[101,199],"per":[102],"frame":[103],"at":[104],"240":[105],"MHz.":[106],"reflects":[108],"computational":[110],"intensity":[111],"ESP32's":[116],"Xtensa":[117],"LX6":[118],"cores":[119],"without":[120,212],"hardware":[121],"neural":[122],"network":[123],"acceleration.":[124],"A":[125],"late-fusion":[126],"module":[127],"combines":[128],"visual":[129],"classification":[130,196],"environmental":[132,228],"sensor":[133,162,229],"readings":[134],"(temperature,":[135],"humidity,":[136],"soil":[137],"moisture,":[138],"CO\u2082)":[139],"produce":[141],"four-level":[143],"health":[145],"assessment":[146],"(Healthy,":[147],"Environmental":[148],"Stress,":[149],"Disease,":[150],"Critical)":[151],"continuous":[154],"risk":[155],"score.":[156],"complete":[158],"system\u2014camera":[159],"capture,":[160],"inference,":[161],"fusion,":[163],"and":[164,179],"MQTT":[165],"reporting\u2014operates":[166],"as":[167],"FreeRTOS":[169],"task":[170],"consuming":[171],"201":[173],"PSRAM":[178,190],"50":[180],"static":[182],"RAM,":[183],"well":[184],"within":[185],"ESP32-CAM's":[187],"4":[188],"MB":[189],"budget.":[191],"performs":[194],"one":[195],"every":[197],"14":[198],"(8.7":[200],"s":[201,205],"+":[203],"5":[204],"interval),":[206],"suitable":[207],"periodic":[209],"greenhouse":[210],"monitoring":[211],"cloud":[213],"connectivity.":[214],"To":[215],"our":[216],"knowledge,":[217],"this":[218],"first":[221],"combining":[223],"CNN":[225],"fusion":[230],"original":[233],"platform.":[235]},"counts_by_year":[],"updated_date":"2026-10-14T16:39:01.820549","created_date":"2026-08-30T00:00:00"}