{"id":"https://openalex.org/W7204875953","doi":"https://doi.org/10.66669/cineforum.v66is4.1352","title":"Synergizing End-to-End Spatio-Temporal Feature Extraction with FRPGW-Optimized HALSTM: A Unified Evolutionary Deep Learning Framework for Holistic Precision Agriculture.","display_name":"Synergizing End-to-End Spatio-Temporal Feature Extraction with FRPGW-Optimized HALSTM: A Unified Evolutionary Deep Learning Framework for Holistic Precision Agriculture.","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7204875953","doi":"https://doi.org/10.66669/cineforum.v66is4.1352"},"language":"en","primary_location":{"id":"doi:10.66669/cineforum.v66is4.1352","doi":"https://doi.org/10.66669/cineforum.v66is4.1352","is_oa":true,"landing_page_url":"https://doi.org/10.66669/cineforum.v66is4.1352","pdf_url":"https://revistadecineforum.com/index.php/cf/article/download/1352/946","source":null,"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":null,"raw_type":"edited-book"},"volume":null,"type":"book","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://revistadecineforum.com/index.php/cf/article/download/1352/946","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5116108059","display_name":"Anju Markose","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I860813019","display_name":"Bharath University","ror":"https://ror.org/04yazpn06","country_code":"IN","type":"education","lineage":["https://openalex.org/I860813019"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Anju Markose","raw_affiliation_strings":["Department of Computer Science and Engineering , Bharath Institute of Higher Education and Research , Chennai , Tamil Nadu , India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering , Bharath Institute of Higher Education and Research , Chennai , Tamil Nadu , India","institution_ids":["https://openalex.org/I860813019"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5138770048","display_name":"K. Baalaji","orcid":"https://orcid.org/0009-0002-6363-0184","observed_orcids":["https://orcid.org/0009-0002-6363-0184"]},"institutions":[{"id":"https://openalex.org/I860813019","display_name":"Bharath University","ror":"https://ror.org/04yazpn06","country_code":"IN","type":"education","lineage":["https://openalex.org/I860813019"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"K. Baalaji K. Baalaji","raw_affiliation_strings":["Department of Computer Science and Engineering , Bharath Institute of Higher Education and Research , Chennai , Tamil Nadu , India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering , Bharath Institute of Higher Education and Research , Chennai , Tamil Nadu , India","institution_ids":["https://openalex.org/I860813019"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I860813019"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"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":0.9994999766349792,"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":0.9994999766349792,"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.00019999999494757503,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.0,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/plant-disease-detection","display_name":"plant disease detection","score":0.7630000114440918},{"id":"https://openalex.org/keywords/pest-detection","display_name":"pest detection","score":0.7160000205039978},{"id":"https://openalex.org/keywords/spatiotemporal-feature-extraction","display_name":"spatiotemporal feature extraction","score":0.8619999885559082},{"id":"https://openalex.org/keywords/hierarchical-attention","display_name":"hierarchical attention","score":0.5789999961853027},{"id":"https://openalex.org/keywords/fuzzy-rough-sets","display_name":"fuzzy rough sets","score":0.7960000038146973},{"id":"https://openalex.org/keywords/grey-wolf-optimizer","display_name":"grey wolf optimizer","score":0.9200000166893005},{"id":"https://openalex.org/keywords/hyperparameter-optimization","display_name":"hyperparameter optimization","score":0.6890000104904175},{"id":"https://openalex.org/keywords/hyperspectral-imagery","display_name":"hyperspectral imagery","score":0.6980000138282776},{"id":"https://openalex.org/keywords/multimodal-fusion","display_name":"multimodal fusion","score":0.7170000076293945},{"id":"https://openalex.org/keywords/yield-prediction","display_name":"yield prediction","score":0.7749999761581421},{"id":"https://openalex.org/keywords/phenological-models","display_name":"phenological models","score":0.5690000057220459},{"id":"https://openalex.org/keywords/evolutionary-deep-learning","display_name":"evolutionary deep learning","score":0.5149999856948853}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7541000247001648},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6974999904632568},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5006999969482422},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4765999913215637},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.43549999594688416},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.400299996137619},{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.3522999882698059},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3400999903678894},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.3100999891757965},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.30390000343322754},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.29739999771118164},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.28790000081062317},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.2766999900341034},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.26510000228881836},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.2639000117778778},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2572000026702881},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.25540000200271606},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2524000108242035}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.66669/cineforum.v66is4.1352","doi":"https://doi.org/10.66669/cineforum.v66is4.1352","is_oa":true,"landing_page_url":"https://doi.org/10.66669/cineforum.v66is4.1352","pdf_url":"https://revistadecineforum.com/index.php/cf/article/download/1352/946","source":null,"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":null,"raw_type":"edited-book"}],"best_oa_location":{"id":"doi:10.66669/cineforum.v66is4.1352","doi":"https://doi.org/10.66669/cineforum.v66is4.1352","is_oa":true,"landing_page_url":"https://doi.org/10.66669/cineforum.v66is4.1352","pdf_url":"https://revistadecineforum.com/index.php/cf/article/download/1352/946","source":null,"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":null,"raw_type":"edited-book"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/2","score":0.9208999872207642,"display_name":"Zero hunger"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7204875953.pdf","grobid_xml":"https://content.openalex.org/works/W7204875953.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4414570848","https://openalex.org/W7154448983","https://openalex.org/W7131639361","https://openalex.org/W2980946635","https://openalex.org/W4414914068","https://openalex.org/W7162084531","https://openalex.org/W7159994918","https://openalex.org/W4412425805","https://openalex.org/W7220464943","https://openalex.org/W4412346040"],"abstract_inverted_index":{"Precision":[0],"agriculture":[1],"has":[2],"rapidly":[3],"evolved":[4],"from":[5],"simple":[6],"monitoring":[7],"and":[8,21,37,40,51,77,114,166,198,209,239,255,286,305,325,337,354],"rudimentary":[9],"sensor":[10,266],"deployments":[11],"to":[12,61,104,138,204,227],"complex,":[13],"data-driven":[14],"decision-making":[15],"systems":[16],"operating":[17],"at":[18],"the":[19,63,107,160,177,207,314,317],"edge":[20],"cloud":[22],"levels.":[23],"However,":[24],"existing":[25],"methodologies":[26],"often":[27],"treat":[28],"spatial":[29,111,155,230],"tasks":[30,42],"(such":[31,43],"as":[32,44],"visual":[33],"plant":[34],"disease":[35,112,275],"detection":[36,282],"pest":[38,281],"classification)":[39],"temporal":[41,116,141],"long-term":[45],"yield":[46,288],"prediction,":[47],"growth":[48,117],"stage":[49],"monitoring,":[50],"micro-climatic":[52,234,265],"forecasting)":[53],"in":[54,67,169,320,351],"absolute":[55],"isolation.":[56],"This":[57,71,297,329],"modal":[58],"isolation":[59],"fails":[60],"capture":[62,139],"holistic":[64],"dependencies":[65],"inherent":[66],"complex":[68],"agro-ecosystems.":[69],"Objective:":[70],"paper":[72],"proposes":[73],"a":[74,120,128,147,184,199,214,223,245,280,287,334],"novel,":[75],"unified,":[76,330],"comprehensive":[78],"deep":[79],"learning":[80],"framework:":[81],"Synergizing":[82],"End-to-End":[83],"Spatio-Temporal":[84],"Feature":[85],"Extraction":[86],"with":[87,146,232,260],"Fuzzy-Rough":[88,178],"Parallel":[89,179],"Grey":[90,180],"Wolf":[91,181],"(FRPGW)":[92],"Optimized":[93],"Hierarchical":[94,130],"Attention":[95,131],"Long":[96,132],"Short-Term":[97,133],"Memory":[98,134],"(HALSTM).":[99],"The":[100,219,268,309],"primary":[101],"goal":[102],"is":[103],"seamlessly":[105],"bridge":[106],"gap":[108],"between":[109],"static":[110],"diagnosis":[113],"dynamic":[115],"modeling":[118],"within":[119],"single":[121],"end-to-end":[122,331],"differentiable":[123],"pipeline.":[124],"Methods:":[125],"We":[126],"introduce":[127],"sophisticated":[129],"(HALSTM)":[135],"network":[136],"designed":[137],"multi-scale":[140],"phenological":[142],"dependencies,":[143],"coupled":[144],"inextricably":[145],"customized,":[148],"attention-augmented":[149],"ResNet-based":[150],"backbone":[151],"(SE-ResNet)":[152],"for":[153,195,341],"high-fidelity":[154],"feature":[156],"extraction.":[157],"To":[158],"address":[159],"profound":[161],"challenge":[162],"of":[163,263,278,284,295,302,316],"hyperparameter":[164],"selection":[165],"architectural":[167],"tuning":[168],"this":[170],"heavily":[171],"parameterized":[172],"hybrid":[173,306],"architecture,":[174],"we":[175],"develop":[176],"Optimizer":[182],"(FRPGW),":[183],"highly":[185,215,322],"advanced":[186],"bio-inspired":[187],"evolutionary":[188],"algorithm.":[189],"FRPGW":[190],"incorporates":[191],"fuzzy-rough":[192],"set":[193],"theory":[194],"uncertainty-aware":[196],"initialization":[197],"parallel":[200],"pack":[201],"communication":[202],"strategy":[203,226],"adaptively":[205],"tune":[206],"model":[208],"guarantee":[210],"optimal":[211],"convergence":[212],"across":[213],"non-convex":[216],"loss":[217],"landscape.":[218],"framework":[220,271],"further":[221],"utilizes":[222],"multimodal":[224],"gating":[225],"fuse":[228],"high-dimensional":[229],"embeddings":[231],"sequential":[233],"data":[235],"representations.":[236],"Results:":[237],"Extensive":[238],"rigorous":[240],"experiments":[241],"were":[242],"conducted":[243],"on":[244],"massive,":[246],"composite,":[247],"multi-modal":[248],"dataset":[249],"comprising":[250],"over":[251],"50,000":[252],"high-resolution":[253],"hyperspectral":[254],"RGB":[256],"images,":[257],"temporally":[258],"aligned":[259],"5":[261],"years":[262],"continuous":[264],"data.":[267],"proposed":[269],"FRPGW-HALSTM":[270,318],"achieved":[272],"an":[273,300],"unprecedented":[274],"classification":[276],"accuracy":[277],"98.2%,":[279],"precision":[283],"97.8%,":[285],"prediction":[289],"Root":[290],"Mean":[291],"Square":[292],"Error":[293],"(RMSE)":[294],"0.12.":[296],"significantly":[298],"outperforms":[299],"array":[301],"state-of-the-art":[303],"base":[304],"models.":[307],"Conclusion:":[308],"empirical":[310],"results":[311],"definitively":[312],"demonstrate":[313],"efficacy":[315],"synergy":[319],"delivering":[321],"robust,":[323],"low-latency,":[324],"high-precision":[326],"agricultural":[327],"diagnostics.":[328],"approach":[332],"provides":[333],"fundamentally":[335],"scalable":[336],"deeply":[338],"optimized":[339],"solution":[340],"modern":[342],"smart":[343],"farming":[344],"paradigms,":[345],"directly":[346],"addressing":[347],"critical":[348],"global":[349],"challenges":[350],"sustainable":[352],"intensification":[353],"food":[355],"security.":[356]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-09-02T00:00:00"}
