{"id":"https://openalex.org/W2998971272","doi":"https://doi.org/10.1109/intellect47034.2019.8954985","title":"Convolutional Neural Network based Regression for Leaf Water Content Estimation","display_name":"Convolutional Neural Network based Regression for Leaf Water Content Estimation","publication_year":2019,"publication_date":"2019-11-01","ids":{"openalex":"https://openalex.org/W2998971272","doi":"https://doi.org/10.1109/intellect47034.2019.8954985","mag":"2998971272"},"language":"en","primary_location":{"id":"doi:10.1109/intellect47034.2019.8954985","is_oa":false,"landing_page_url":"https://doi.org/10.1109/intellect47034.2019.8954985","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 Second International Conference on Latest trends in Electrical Engineering and Computing Technologies (INTELLECT)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5035765842","display_name":"Rida Nasir","orcid":null},"institutions":[{"id":"https://openalex.org/I197827452","display_name":"Institute of Space Technology","ror":"https://ror.org/01tmmzv45","country_code":"PK","type":"education","lineage":["https://openalex.org/I197827452"]}],"countries":["PK"],"is_corresponding":true,"raw_author_name":"Rida Nasir","raw_affiliation_strings":["Department of Electrical Engineering, Artificial Intelligence and Computer Vision (iVision) Lab, Institute of Space Technology, Islamabad"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Artificial Intelligence and Computer Vision (iVision) Lab, Institute of Space Technology, Islamabad","institution_ids":["https://openalex.org/I197827452"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037898063","display_name":"Muhammad Jaleed Khan","orcid":"https://orcid.org/0000-0003-4727-4722"},"institutions":[{"id":"https://openalex.org/I197827452","display_name":"Institute of Space Technology","ror":"https://ror.org/01tmmzv45","country_code":"PK","type":"education","lineage":["https://openalex.org/I197827452"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Muhammad Jaleed Khan","raw_affiliation_strings":["Department of Electrical Engineering, Artificial Intelligence and Computer Vision (iVision) Lab, Institute of Space Technology, Islamabad"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Artificial Intelligence and Computer Vision (iVision) Lab, Institute of Space Technology, Islamabad","institution_ids":["https://openalex.org/I197827452"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010226537","display_name":"Muhammad Sufyan Arshad","orcid":"https://orcid.org/0000-0003-1666-9414"},"institutions":[{"id":"https://openalex.org/I197827452","display_name":"Institute of Space Technology","ror":"https://ror.org/01tmmzv45","country_code":"PK","type":"education","lineage":["https://openalex.org/I197827452"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Muhammad Arshad","raw_affiliation_strings":["Office of Research Innovation and Commercialization, Institute of Space Technology, Islamabad"],"affiliations":[{"raw_affiliation_string":"Office of Research Innovation and Commercialization, Institute of Space Technology, Islamabad","institution_ids":["https://openalex.org/I197827452"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103113851","display_name":"Khurram Khurshid","orcid":"https://orcid.org/0000-0002-5632-7331"},"institutions":[{"id":"https://openalex.org/I197827452","display_name":"Institute of Space Technology","ror":"https://ror.org/01tmmzv45","country_code":"PK","type":"education","lineage":["https://openalex.org/I197827452"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Khurram Khurshid","raw_affiliation_strings":["Department of Electrical Engineering, Artificial Intelligence and Computer Vision (iVision) Lab, Institute of Space Technology, Islamabad"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Artificial Intelligence and Computer Vision (iVision) Lab, Institute of Space Technology, Islamabad","institution_ids":["https://openalex.org/I197827452"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5035765842"],"corresponding_institution_ids":["https://openalex.org/I197827452"],"apc_list":null,"apc_paid":null,"fwci":0.3406,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.66905646,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9994999766349792,"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"}},"topics":[{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9994999766349792,"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"}},{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9988999962806702,"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/T14365","display_name":"Leaf Properties and Growth Measurement","score":0.9980000257492065,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.657984733581543},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.6565367579460144},{"id":"https://openalex.org/keywords/vegetation","display_name":"Vegetation (pathology)","score":0.5717073082923889},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5697277784347534},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4736919403076172},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.45568206906318665},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4480934739112854},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4285871982574463},{"id":"https://openalex.org/keywords/water-content","display_name":"Water content","score":0.42092567682266235},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.40093719959259033},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38572439551353455},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20365944504737854},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.20143264532089233},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.11956986784934998},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10526028275489807}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.657984733581543},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.6565367579460144},{"id":"https://openalex.org/C2776133958","wikidata":"https://www.wikidata.org/wiki/Q7918366","display_name":"Vegetation (pathology)","level":2,"score":0.5717073082923889},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5697277784347534},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4736919403076172},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.45568206906318665},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4480934739112854},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4285871982574463},{"id":"https://openalex.org/C24939127","wikidata":"https://www.wikidata.org/wiki/Q373499","display_name":"Water content","level":2,"score":0.42092567682266235},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.40093719959259033},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38572439551353455},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20365944504737854},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.20143264532089233},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.11956986784934998},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10526028275489807},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C187320778","wikidata":"https://www.wikidata.org/wiki/Q1349130","display_name":"Geotechnical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/intellect47034.2019.8954985","is_oa":false,"landing_page_url":"https://doi.org/10.1109/intellect47034.2019.8954985","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 Second International Conference on Latest trends in Electrical Engineering and Computing Technologies (INTELLECT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Clean water and sanitation","id":"https://metadata.un.org/sdg/6","score":0.6600000262260437}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W194524482","https://openalex.org/W268331225","https://openalex.org/W831658426","https://openalex.org/W1597222998","https://openalex.org/W1966035399","https://openalex.org/W1974110440","https://openalex.org/W1976948919","https://openalex.org/W2005125125","https://openalex.org/W2010797000","https://openalex.org/W2037236246","https://openalex.org/W2042436931","https://openalex.org/W2047508432","https://openalex.org/W2055289219","https://openalex.org/W2084555690","https://openalex.org/W2085880802","https://openalex.org/W2086708373","https://openalex.org/W2095705004","https://openalex.org/W2106677999","https://openalex.org/W2113325037","https://openalex.org/W2116187918","https://openalex.org/W2116743998","https://openalex.org/W2118877769","https://openalex.org/W2127431974","https://openalex.org/W2127632654","https://openalex.org/W2150140969","https://openalex.org/W2165282790","https://openalex.org/W2224070395","https://openalex.org/W2267740595","https://openalex.org/W2510725918","https://openalex.org/W2782659011","https://openalex.org/W2793272303","https://openalex.org/W2890455271","https://openalex.org/W2896018297","https://openalex.org/W2909214391","https://openalex.org/W3003930371","https://openalex.org/W6623340389","https://openalex.org/W6659717275","https://openalex.org/W6662335928","https://openalex.org/W6684243969","https://openalex.org/W6725236325","https://openalex.org/W6772950877","https://openalex.org/W6999369388","https://openalex.org/W6999728311","https://openalex.org/W7064968723"],"related_works":["https://openalex.org/W3164827840","https://openalex.org/W3163755067","https://openalex.org/W3212705600","https://openalex.org/W3129768331","https://openalex.org/W3109305176","https://openalex.org/W2089724914","https://openalex.org/W2151181340","https://openalex.org/W2904453310","https://openalex.org/W2483888092","https://openalex.org/W3005415419","https://openalex.org/W3130060595","https://openalex.org/W3135366015","https://openalex.org/W3176449035","https://openalex.org/W2134070145","https://openalex.org/W3129727445","https://openalex.org/W1186600836","https://openalex.org/W2139454280","https://openalex.org/W2186573075","https://openalex.org/W3174896073","https://openalex.org/W1986812364"],"abstract_inverted_index":{"With":[0],"the":[1,90,124,202,219,232],"advancements":[2],"in":[3,149,183,200,210],"precision":[4],"farming,":[5],"crop":[6,13],"sensing":[7],"is":[8,20,88,105,121],"gaining":[9],"importance":[10],"for":[11,136,157,206,238],"timely":[12],"health":[14,26],"management.":[15],"Leaf":[16],"water":[17,208,225],"content":[18,226],"(LWC)":[19],"key":[21],"component":[22],"to":[23,57,62,72,146,223],"determine":[24],"vegetation":[25,240],"and":[27,46,66,69,97,113,172,185,193],"nourishment.":[28],"Timely":[29],"estimation":[30],"of":[31,48,53,76,94,108,126,129,160,170,178,191,195,214,227,234],"LWC":[32,54,148],"could":[33],"save":[34],"us":[35],"from":[36,55,117,152],"hazardous":[37],"damage":[38],"by":[39,133],"pre-planning:":[40],"drought":[41],"stress":[42],"on":[43,123,165,231],"plants,":[44],"irrigation":[45],"prediction":[47],"woodland":[49],"fire.":[50],"The":[51,100,138,212],"retrieval":[52],"visible":[56],"shortwave":[58],"infrared":[59],"(VSWIR:":[60],"0.39":[61],"2.5":[63],"\u03bcm)":[64,74],"mid-":[65],"thermal-infrared":[67],"(MIR":[68],"TIR:":[70],"2.50":[71],"14.0":[73],"windows":[75],"electromagnetic":[77],"spectrum":[78],"has":[79],"been":[80],"investigated":[81],"using":[82,134],"different":[83,127,130,228],"statistical":[84],"algorithms.":[85],"Deep":[86],"learning":[87,96,110],"modernizing":[89],"fast":[91],"growing":[92],"field":[93],"machine":[95],"image":[98],"processing.":[99],"convolutional":[101],"neural":[102],"network":[103],"(CNN)":[104],"ultramodern":[106],"technique":[107],"deep":[109],"that":[111],"learns":[112],"extracts":[114],"features":[115,128,145],"directly":[116],"data.":[118],"This":[119,197],"research":[120],"focused":[122],"extraction":[125],"plant":[131,150,229],"species":[132,151,230],"CNN":[135,140,181],"Regression.":[137],"modeled":[139],"architecture":[141],"automatically":[142],"detects":[143],"prominent":[144],"estimate":[147],"its":[153],"reflectance":[154],"spectra,":[155],"recorded":[156],"varying":[158],"amount":[159],"LWC.":[161],"Previous":[162],"methods":[163],"applied":[164],"same":[166],"dataset":[167],"yielded":[168],"accuracy":[169,190],"93%":[171],"Root":[173],"Mean":[174],"Square":[175],"Error":[176],"(RMSE)":[177],"7.1,":[179],"however,":[180],"resulted":[182],"better":[184],"swift":[186],"results":[187],"with":[188],"an":[189],"98.4%":[192],"RMSE":[194],"4.183.":[196],"study":[198,216],"helps":[199],"identifying":[201],"important":[203],"spectral":[204,236],"regions":[205],"quantifying":[207],"stresses":[209],"vegetation.":[211],"outcomes":[213],"this":[215],"can":[217],"enable":[218],"future":[220],"space":[221],"missions":[222],"foresee":[224],"basis":[233],"their":[235],"signatures":[237],"illustrating":[239],"stresses.":[241]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}