{"id":10617,"date":"2024-07-13T20:50:45","date_gmt":"2024-07-13T18:50:45","guid":{"rendered":"https:\/\/geopard.tech\/?p=10617"},"modified":"2024-07-13T20:50:51","modified_gmt":"2024-07-13T18:50:51","slug":"5g-leido-mokytis-realiuoju-laiku-tvaraus-ukininkavimo-srityje-tyrimas-apie-cukriniu-runkeliu-auginima","status":"publish","type":"post","link":"https:\/\/geopard.tech\/lt\/blog\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\/","title":{"rendered":"5G tinklas leid\u017eiantis mokytis realiu laiku tvariajame \u016bkininkavime: cukrini\u0173 runkeli\u0173 tyrimas"},"content":{"rendered":"<p class=\"wp-block-paragraph\">D\u017eiaugiam\u0117s gal\u0117dami prane\u0161ti apie s\u0117kming\u0105 projekto \u201c5G tinklai kaip realaus laiko mokymosi tvarioje \u017eemdirbyst\u0117je \u012fgalintojai\u201d u\u017ebaigim\u0105, kuriam dalinai finansavim\u0105 skyr\u0117 \u0160iaur\u0117s Rein menyje-Vestfalijos \u017eem\u0117s \u016akio, pramon\u0117s, klimato apsaugos ir energetikos ministerija.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><img decoding=\"async\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2022\/08\/image-2.png?w=1620&amp;ssl=1\" alt=\"\"\/><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u0160i iniciatyva \u017eymi reik\u0161ming\u0105 \u017eingsn\u012f siekiant i\u0161tirti 5G technologijos transformacin\u012f potencial\u0105 \u017eem\u0117s \u016bkyje, ypa\u010d siekiant pagerinti cukrini\u0173 runkeli\u0173 auginimo ekologinius, ekonominius ir tvarumo aspektus.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Ji pasinaudojo \u017eemu 5G atsilikimu, kad realiuoju laiku integruot\u0173 pa\u017eangias informacines technologijas, leid\u017eian\u010dias nedelsiant reaguoti \u012f jutikli\u0173 ir pad\u0117ties duomenis per nustatytus laiko intervalus.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full is-resized\"><img data-recalc-dims=\"1\" fetchpriority=\"high\" decoding=\"async\" width=\"624\" height=\"386\" data-attachment-id=\"10624\" data-permalink=\"https:\/\/geopard.tech\/lt\/blog\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\/picture-from-the-final-event-of-the-project-presentation-at-hochschule-hamm-lippstadt-hshl\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Picture-from-the-final-event-of-the-project-presentation-at-Hochschule-Hamm-Lippstadt-HSHL.png?fit=624%2C386&amp;ssl=1\" data-orig-size=\"624,386\" data-comments-opened=\"1\" data-image-meta=\"{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;0&quot;}\" data-image-title=\"Picture from the final event of the project presentation at Hochschule Hamm-Lippstadt (HSHL)\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Picture-from-the-final-event-of-the-project-presentation-at-Hochschule-Hamm-Lippstadt-HSHL.png?fit=624%2C386&amp;ssl=1\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Picture-from-the-final-event-of-the-project-presentation-at-Hochschule-Hamm-Lippstadt-HSHL.png?resize=624%2C386&#038;ssl=1\" alt=\"Nuotrauka i\u0161 galutinio projekto pristatymo renginio Hamm-Lippstadt universitete (HSHL)\" class=\"wp-image-10624\" style=\"width:836px;height:auto\" srcset=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Picture-from-the-final-event-of-the-project-presentation-at-Hochschule-Hamm-Lippstadt-HSHL.png?w=624&amp;ssl=1 624w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Picture-from-the-final-event-of-the-project-presentation-at-Hochschule-Hamm-Lippstadt-HSHL.png?resize=300%2C186&amp;ssl=1 300w\" sizes=\"(max-width: 624px) 100vw, 624px\" \/><figcaption class=\"wp-element-caption\">Nuotrauka i\u0161 galutinio projekto pristatymo renginio Hamm-Lippstadt universitete (HSHL)<\/figcaption><\/figure>\n<\/div>\n\n\n<h2 class=\"wp-block-heading\" id=\"h-project-focus-and-partnership\">Projekto tikslas ir partneryst\u0117<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Bendradarbiaujant su HSHL partneriais ir \u201ePfeifer &amp; Langen\u201c parama, projektas sutelk\u0117 d\u0117mes\u012f \u012f cukrini\u0173 runkeli\u0173 auginimo vis\u0105 gyvavimo cikl\u0105 partneriams priklausan\u010diuose laukuose. Juo siekta parodyti, kaip 5G gali tapti pagrindiniu technologijos katalizatoriumi \u0160iaur\u0117s Reinso-Vestfalijos \u017eem\u0117s \u016bkio sektoriuje, demonstruojant jo potencial\u0105 skatinant inovacijas ir efektyvum\u0105.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-role-of-geopard-agriculture\">GeoPard \u017eem\u0117s \u016bkis<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">\u201eGeoPard Agriculture\u201c atliko svarb\u0173 vaidmen\u012f nustatant ir \u012fgyvendinant pagrindinius projekto aspektus, \u012fskaitant augal\u0173 aptikimo, steb\u0117jimo ir produkcijos prognozavimo scenarijus. Suk\u016br\u0117me bandom\u0105j\u0105 DI sistem\u0105, pritaikyt\u0105 5G \u017eem\u0117s \u016bkio aplinkai, modelius vykd\u0117me debes\u0173 infrastrukt\u016broje ir suk\u016br\u0117me mobili\u0105j\u0105 program\u0117l\u0119 realiu laiku bendrauti su debes\u0173 modeliais.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-technological-integration\">Technologin\u0117 integracija<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Dirbtinio intelekto (DI) metodai buvo naudojami per tvirt\u0105 debes\u0173 infrastrukt\u016br\u0105, pasi\u017eymin\u010di\u0105 didel\u0117mis skai\u010diavimo galimyb\u0117mis. DI algoritmai kiekvieno kry\u017eminimo metu realiu laiku kategorizavo augalus ir steb\u0117jo j\u0173 augim\u0105 vis\u0105 gyvavimo cikl\u0105, taip atsisakant nereikaling\u0173 lauko ap\u017ei\u016br\u0173 vien tik duomen\u0173 rinkimo tikslu.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u0160is prover\u017eis leido tiksliai paskirstyti tr\u0105\u0161as ir augal\u0173 apsaugos produktus, dinami\u0161kai reguliuojant paskirstymo normas ma\u0161ininio mokymosi algoritmais, kertant laukus.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-deployment-of-unmanned-vehicles\">Belaid\u017ei\u0173 transporto priemoni\u0173 dislokavimas<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Be to, projekte buvo pasinaudota suma\u017einta 5G delsa, siekiant dislokuoti nepilotuojamas transporto priemones augal\u0173 steb\u0117jimui ir duomen\u0173 rinkimui. \u0160ios transporto priemon\u0117s suvaidino itin svarb\u0173 vaidmen\u012f renkant realaus laiko \u012f\u017evalgas ir toliau optimizuojant \u017eem\u0117s \u016bkio praktikas.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-project-outcomes-enhancing-sugar-beet-production-with-5g-technology\">Projekto rezultatai: Cukrini\u0173 runkeli\u0173 gamybos gerinimas naudojant 5G technologij\u0105<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Projektas parod\u0117, kaip 5G technologija gal\u0117t\u0173 tapti transformuojan\u010diu veiksniu \u0160iaur\u0117s Reino-Vestfalijos \u017eem\u0117s \u016bkio sektoriuje, analizuojant vis\u0105 cukrini\u0173 runkeli\u0173 auginimo gyvavimo cikl\u0105 ir pabr\u0117\u017eiant reik\u0161mingus 5G technologijos suteikiamus patobulinimus. Ta\u010diau, norint efektyviai pademonstruoti projekto rezultatus, mokslininkai naudojo darbo paketus, kuriuose buvo skirtingi scenarijai ir infrastrukt\u016bros.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img data-recalc-dims=\"1\" decoding=\"async\" width=\"810\" height=\"610\" data-attachment-id=\"10625\" data-permalink=\"https:\/\/geopard.tech\/lt\/blog\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\/sugar-beet-test-field\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Sugar-beet-test-field.png?fit=2048%2C1542&amp;ssl=1\" data-orig-size=\"2048,1542\" data-comments-opened=\"1\" data-image-meta=\"{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;0&quot;}\" data-image-title=\"Sugar beet test field\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Sugar-beet-test-field.png?fit=1024%2C771&amp;ssl=1\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Sugar-beet-test-field.png?resize=810%2C610&#038;ssl=1\" alt=\"Cukrini\u0173 runkeli\u0173 bandym\u0173 laukas \" class=\"wp-image-10625\" srcset=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Sugar-beet-test-field.png?w=2048&amp;ssl=1 2048w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Sugar-beet-test-field.png?resize=300%2C226&amp;ssl=1 300w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Sugar-beet-test-field.png?resize=1024%2C771&amp;ssl=1 1024w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Sugar-beet-test-field.png?resize=768%2C578&amp;ssl=1 768w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Sugar-beet-test-field.png?resize=1536%2C1157&amp;ssl=1 1536w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Sugar-beet-test-field.png?resize=400%2C300&amp;ssl=1 400w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Sugar-beet-test-field.png?resize=200%2C150&amp;ssl=1 200w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Sugar-beet-test-field.png?resize=1200%2C904&amp;ssl=1 1200w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Sugar-beet-test-field.png?w=1620&amp;ssl=1 1620w\" sizes=\"(max-width: 810px) 100vw, 810px\" \/><figcaption class=\"wp-element-caption\">Cukrini\u0173 runkeli\u0173 bandym\u0173 laukas<\/figcaption><\/figure>\n<\/div>\n\n\n<h3 class=\"wp-block-heading\" id=\"h-scenario-definition-considering-existing-geodata-and-ml-infrastructure\">Scenarijaus apibr\u0117\u017eimas, atsi\u017evelgiant \u012f esamus geografinius duomenis ir ML infrastrukt\u016br\u0105<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Projekte parodoma, kaip 5G technologijos integravimas gali pagerinti tradicinius cukrini\u0173 runkeli\u0173 gamybos ciklo procesus. Pagrindiniai tikslai ap\u0117m\u0117:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Parengti paruo\u0161ti \u012fgyvendinimui scenarijai augal\u0173 atpa\u017einimui, steb\u0117jimui ir gamybos prognozavimui.<\/li>\n\n\n\n<li>Nustatyti techniniai reikalavimai, reikalingi s\u0117kmingam \u0161i\u0173 scenarij\u0173 \u012fgyvendinimui.<\/li>\n\n\n\n<li>Nustatyti ir \u012fvertinti atitinkami ekologiniai ir ekonominiai rodikliai, siekiant \u012fvertinti prid\u0117tin\u0119 vert\u0119, kuri\u0105 suteikia 5G tinklas.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">\u0160is etapas pabr\u0117\u017e\u0117 projekto \u012fsipareigojim\u0105 integruoti pa\u017eangiausias technologijas su esamomis agrarin\u0117mis praktikomis. \u0160i architekt\u016bra pasinaudojo didelio grei\u010dio 5G tinklo ry\u0161iu, kad palengvint\u0173 duomen\u0173 rinkim\u0105 ir apdorojim\u0105 realiuoju laiku tarp kra\u0161tini\u0173 \u012frengini\u0173 ir debesies. Debes\u0173 infrastrukt\u016bra suteik\u0117 esmini\u0173 i\u0161tekli\u0173 didelio masto dirbtinio intelekto modeli\u0173 mokymui ir diegimui, o dirbtinio intelekto platforma pasi\u016bl\u0117 tvirtus \u012frankius modeli\u0173 k\u016brimui ir diegimui. Taikom\u0173j\u0173 program\u0173 sluoksnis pateik\u0117 galutiniams vartotojams veiksm\u0173 rekomendacijas, gautas i\u0161 dirbtinio intelekto modeli\u0173, pagerindamas sprendim\u0173 pri\u0117mimo galimybes.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-machine-learning-and-ai-in-the-context-of-5g\">Ma\u0161ininis mokymasis ir dirbtinis intelektas 5G kontekste<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">\u0160ios dalies tikslas buvo pritaikyti esamas ma\u0161ininio mokymosi ir dirbtinio intelekto sistemas, kad jos atitikt\u0173 pirmiau apra\u0161ytus scenarijus, atitinkamai jas optimizuojant. Pagrindiniai tikslai ap\u0117m\u0117:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Apibr\u0117\u017ekite sistemos tikslus ir sukurkite sistemos architekt\u016br\u0105<\/li>\n\n\n\n<li>Surinkti duomenys i\u0161 realaus pasaulio modeliui mokyti ir tikrinti.<\/li>\n\n\n\n<li>Sukurta ir anotaciuota tinkama duomen\u0173 baz\u0117, skirta augal\u0173 identifikavimui ir steb\u0117jimui.<\/li>\n\n\n\n<li>Integruoti dirbtinio intelekto modeliai skland\u017eiai \u012f 5G tinklo infrastrukt\u016br\u0105.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">\u0160iame etape svarb\u0173 vaidmen\u012f atliko kra\u0161to \u012frenginiai su 5G technologij\u0105 naudojan\u010diomis SIM kortel\u0117mis. Buvo atid\u017eiai stebimi pagrindiniai na\u0161umo rodikliai (KPI), tokie kaip delsos arba galutin\u0117s (E2E) delsos. Matavimai ap\u0117m\u0117 gaut\u0173 duomen\u0173 paket\u0173 patikimumo ir prieinamumo \u012fvertinim\u0105, taip pat vartotojo duomen\u0173 perdavimo spartos ir did\u017eiausios duomen\u0173 perdavimo spartos analiz\u0119.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Be to, prielaidos buvo daromos remiantis UHD rai\u0161kos vaizdo \u012fra\u0161o MP4 formatu, perduodamo Transmission Control Protocol (TCP) protokolu, transliacija. Galimi sprendimai ap\u0117m\u0117 optimizavim\u0105 naudojant atskirus vaizdus vietoj nuolatini\u0173 vaizdo transliacij\u0173, pagrindini\u0173 optimizacij\u0173 atlikim\u0105 tiesiogiai briaunos (edge) \u012frenginiuose ir modeli\u0173 kvantizavimo technologij\u0173 taikym\u0105 efektyvumui pagerinti.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-cloud-infrastructure-and-aws-services\">Debes\u0173 infrastrukt\u016bra ir AWS paslaugos<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Projektas stipriai r\u0117m\u0117si debes\u0173 infrastrukt\u016bra, naudojant AWS paslaugas, tokias kaip Lambda, SageMaker, S3, CloudWatch ir RDS, kurios atliko svarb\u0173 vaidmen\u012f teikiant reikiamus i\u0161teklius dirbtinio intelekto modeli\u0173 mokymui ir diegimui.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">AWS Lambda buvo naudojama efektyviam instancij\u0173 valdymui ir pritaikym\u0173 pateikimui, o AWS SageMaker palengvino tvirt\u0173 ma\u0161ininio mokymosi proces\u0173 k\u016brim\u0105. Saugyklos sprendimai, tokie kaip S3, CloudWatch ir RDS, buvo b\u016btini duomen\u0173 rinkiniams ir \u017eurnalams saugoti, kurie yra gyvybi\u0161kai svarb\u016bs ma\u0161ininio mokymosi modeli\u0173 ir neuronini\u0173 tinkl\u0173 veikimui.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img data-recalc-dims=\"1\" decoding=\"async\" width=\"800\" height=\"490\" data-attachment-id=\"10627\" data-permalink=\"https:\/\/geopard.tech\/lt\/blog\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\/aws-cloud-infrastructure\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/AWS-cloud-infrastructure.png?fit=800%2C490&amp;ssl=1\" data-orig-size=\"800,490\" data-comments-opened=\"1\" data-image-meta=\"{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;0&quot;}\" data-image-title=\"AWS cloud infrastructure\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/AWS-cloud-infrastructure.png?fit=800%2C490&amp;ssl=1\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/AWS-cloud-infrastructure.png?resize=800%2C490&#038;ssl=1\" alt=\"AWS debes\u0173 infrastrukt\u016bra\" class=\"wp-image-10627\" srcset=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/AWS-cloud-infrastructure.png?w=800&amp;ssl=1 800w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/AWS-cloud-infrastructure.png?resize=300%2C184&amp;ssl=1 300w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/AWS-cloud-infrastructure.png?resize=768%2C470&amp;ssl=1 768w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/><figcaption class=\"wp-element-caption\">AWS debes\u0173 infrastrukt\u016bra<\/figcaption><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\">D\u0117l to \u0161i infrastrukt\u016bra palaik\u0117 5G tinklo u\u017etikrinamas realaus laiko duomen\u0173 apdorojimo galimybes.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-5g-network-latency\">5G tinklo v\u0117lavimas<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">5G tinklai buvo sukurti siekiant pasiekti itin ma\u017e\u0105 dels\u0105, paprastai nuo 1 iki 10 milisekund\u017ei\u0173. \u0160i delsa atspind\u0117jo laik\u0105, reikaling\u0105 duomenims keliauti tarp mobili\u0173j\u0173 \u012frengini\u0173 ir AWS serveri\u0173 per 5G tinkl\u0105. \u012erengini\u0173 specifin\u0117s apdorojimo galimyb\u0117s, pavyzd\u017eiui, greitis, su kuriuo i\u0161manieji telefonai su didelio na\u0161umo procesoriais fiksuoja ir apdoroja nuotraukas, taip pat tur\u0117jo \u012ftakos delsai.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">5G tinklo duomen\u0173 \u012fk\u0117limo spartos ir nuotraukos dydis tur\u0117jo \u012ftakos duomen\u0173 perdavimo laikui \u012f AWS. AWS dar labiau prisid\u0117jo prie v\u0117lavimo d\u0117l apdorojimo laik\u0173, toki\u0173 kaip neuronini\u0173 tinkl\u0173 pagrindu atliekamas aptikimas ir segmentavimas, kurie skyr\u0117si priklausomai nuo algoritmo sud\u0117tingumo ir AWS paslaugos efektyvumo. Po apdorojimo rezultatai buvo atsisiun\u010diami atgal \u012f mobiliuosius \u012frenginius, tai priklaus\u0117 nuo 5G atsisiuntimo spartos ir rezultat\u0173 duomen\u0173 dyd\u017eio.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-plant-recognition-using-ai\">Augal\u0173 atpa\u017einimas naudojant DI<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Augal\u0173 atpa\u017einimo srityje dirbtinio intelekto valdomi procesai ap\u0117m\u0117 i\u0161samios augal\u0173 vaizd\u0173 duomen\u0173 baz\u0117s k\u016brim\u0105, skirtos mokyti algoritmus, paremtus neuronini\u0173 tinkl\u0173 principais. \u0160ie algoritmai buvo mokomi atskirti cukrini\u0173 runkeli\u0173 r\u016b\u0161is nuo kit\u0173 augal\u0173, atpa\u017e\u012fstant konkre\u010diam augalo tipui b\u016bdingus po\u017eymius, tokius kaip lap\u0173 formos, \u017eied\u0173 spalvos ir kt.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"810\" height=\"264\" data-attachment-id=\"10628\" data-permalink=\"https:\/\/geopard.tech\/lt\/blog\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\/phenological-development-of-sugar-beet-plants\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Phenological-development-of-sugar-beet-plants.jpg?fit=2048%2C667&amp;ssl=1\" data-orig-size=\"2048,667\" data-comments-opened=\"1\" data-image-meta=\"{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;0&quot;}\" data-image-title=\"Phenological development of sugar beet plants\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Phenological-development-of-sugar-beet-plants.jpg?fit=1024%2C334&amp;ssl=1\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Phenological-development-of-sugar-beet-plants.jpg?resize=810%2C264&#038;ssl=1\" alt=\"Cukrini\u0173 runkeli\u0173 augal\u0173 fenologin\u0117 raida\" class=\"wp-image-10628\" srcset=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Phenological-development-of-sugar-beet-plants.jpg?w=2048&amp;ssl=1 2048w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Phenological-development-of-sugar-beet-plants.jpg?resize=300%2C98&amp;ssl=1 300w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Phenological-development-of-sugar-beet-plants.jpg?resize=1024%2C334&amp;ssl=1 1024w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Phenological-development-of-sugar-beet-plants.jpg?resize=768%2C250&amp;ssl=1 768w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Phenological-development-of-sugar-beet-plants.jpg?resize=1536%2C500&amp;ssl=1 1536w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Phenological-development-of-sugar-beet-plants.jpg?resize=1200%2C391&amp;ssl=1 1200w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Phenological-development-of-sugar-beet-plants.jpg?w=1620&amp;ssl=1 1620w\" sizes=\"(max-width: 810px) 100vw, 810px\" \/><figcaption class=\"wp-element-caption\">Cukrini\u0173 runkeli\u0173 augal\u0173 fenologin\u0117 raida. \u0160altinis: <a href=\"https:\/\/www.mdpi.com\/2073-4395\/11\/7\/1277\" rel=\"nofollow\">https:\/\/www.mdpi.com\/2073-4395\/11\/7\/1277<\/a><\/figcaption><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\">\u010cia augal\u0173 atpa\u017einimu laikome pikt\u017eoli\u0173 aptikimo ir cukrini\u0173 runkeli\u0173 augal\u0173 segmentavimo u\u017eduot\u012f.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Pikt\u017eoli\u0173 aptikimas<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Veiklos nustatymui projekte naudotas \"MobileNet-v3\" modelis, apmokytas naudojant pla\u010dias duomen\u0173 augmentacijas ir svertin\u012f parinkim\u0105. \u0160is modelis pasiek\u0117 \u012fsp\u016bding\u0105 0,984 tikslum\u0105 ir 0,998 AUC.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Cukrini\u0173 runkeli\u0173 segmentavimas<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Segmentavimo u\u017eduotims atlikti buvo naudojami tokie modeliai kaip YOLACT, ResNeSt, SOLO ir U-net, siekiant tiksliai nustatyti atskirus cukrini\u0173 runkeli\u0173 pavyzd\u017eius vaizduose. Tada efektyviausias modelis buvo pasirinktas pagal skirtingus kriterijus: greit\u012f, inferencijos laik\u0105 ir kt. Segmentavimo duomenys buvo gauti i\u0161 orlaivi\u0173 fiksuot\u0173 RGB vaizd\u0173, kurie buvo pakeisto dyd\u017eio ir anotacij\u0173 apmokymui bei validavimui.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Segmentavimo u\u017eduotys ap\u0117m\u0117 kauki\u0173 k\u016brim\u0105, tiksliai apibr\u0117\u017eiant augal\u0173 ribas. \u0160is metodas suma\u017eino \u017emogaus anotavimo pastangas ir optimizavo efektyvum\u0105. Pirmenyb\u0119 teikiant sud\u0117ting\u0173 pavyzd\u017ei\u0173 \u017eym\u0117jimui, modelio na\u0161umas buvo \u017eymiai patobulintas. Iteratyvinis pakartotinis apmokymas ir netikrumo m\u0117gini\u0173 \u0117mimo strategijos pasirod\u0117 es\u0105s veiksmingas, pasiekiant didesn\u012f nei 98% segmentavimo tikslum\u0105 \u012fvairiais augimo etapais.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full is-resized\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"355\" height=\"146\" data-attachment-id=\"10629\" data-permalink=\"https:\/\/geopard.tech\/lt\/blog\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\/example-of-input-output-of-segmentation\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Example-of-input-output-of-segmentation.png?fit=355%2C146&amp;ssl=1\" data-orig-size=\"355,146\" data-comments-opened=\"1\" data-image-meta=\"{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;0&quot;}\" data-image-title=\"Example of input-output of segmentation\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Example-of-input-output-of-segmentation.png?fit=355%2C146&amp;ssl=1\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Example-of-input-output-of-segmentation.png?resize=355%2C146&#038;ssl=1\" alt=\"Pavyzdys, kaip atliekamas segmentavimas (\u012fvestis ir i\u0161vestis)\" class=\"wp-image-10629\" style=\"width:767px;height:auto\" srcset=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Example-of-input-output-of-segmentation.png?w=355&amp;ssl=1 355w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Example-of-input-output-of-segmentation.png?resize=300%2C123&amp;ssl=1 300w\" sizes=\"(max-width: 355px) 100vw, 355px\" \/><figcaption class=\"wp-element-caption\">Pavyzdys, kaip atliekamas segmentavimas (\u012fvestis ir i\u0161vestis)<\/figcaption><\/figure>\n<\/div>\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Modelio vertinimas<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Modelis buvo apmokytas naudojant grie\u017etus duomen\u0173 padidinimo metodus. Modelis buvo vertinamas naudojant \u012fvairius metrikus, \u012fskaitant sankirt\u0105 per s\u0105jung\u0105 (IoU). Sukurtam modeliui atlikta i\u0161vad\u0173 analiz\u0117, remiantis dalimi \u2018plant seedlings v2\u2019 duomen\u0173 rinkinio, parod\u0117 81%tikslum\u0105. I\u0161vad\u0173 laikas u\u017etruko ma\u017edaug 320 milisekund\u017ei\u0173, po 7 sekund\u017ei\u0173 inicijavimo laikotarpio, kuris reikalingas tik kart\u0105 per sesij\u0105.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Augal\u0173 steb\u0117jime, pagr\u012fstame dirbtiniu intelektu (DI), kameros ir jutikliai rinko gyvybi\u0161kai svarbius augal\u0173 duomenis, kuriuos analizavo ma\u0161ininio mokymosi ir DI algoritmai. \u0160i analiz\u0117 atliko itin svarb\u0173 vaidmen\u012f vertinant augal\u0173 sveikat\u0105, nustatant stres\u0105, ligas ar kitus augim\u0105 veikian\u010dius veiksnius.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Programos, apiman\u010dios nuo \u017eem\u0117s \u016bkio produktyvumo optimizavimo iki gamtosaugos ekosistem\u0173, toki\u0173 kaip mi\u0161kai, steb\u0117jimo, gamtosaugos pastang\u0173 palaikymo ir poveikio aplinkai supratimo gerinimo.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-object-detection-in-plant-monitoring\">Objekt\u0173 aptikimas augal\u0173 steb\u0117senoje<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Kitas cukrini\u0173 runkeli\u0173 augal\u0173 segmentavimo etapas yra objekt\u0173 aptikimas, siekiant suprasti kiekvieno augalo ypatybes pagal sveikat\u0105, augim\u0105 ir kitus veiksnius. Augal\u0173 steb\u0117jimui objektams aptikti buvo naudojami pa\u017eang\u016bs modeliai, tokie kaip YOLOv4, MobileNetV2 ir VGG-19 su atidumo mechanizmais. \u0160ie modeliai analizavo segmentuotus cukrini\u0173 runkeli\u0173 vaizdus, kad aptikt\u0173 specifines streso ir lig\u0173 vietas, leid\u017eian\u010dius atlikti tikslesnius ir kryptingesnius veiksmus.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Projektas pasiek\u0117 reik\u0161ming\u0173 etap\u0173 lig\u0173 aptikimo srityje, apmokant \u201eResNet-18\u201c ir \u201eResNet-34\u201c modelius, i\u0161 anksto apmokytus ant \u201eImageNet\u201c. \u0160ie modeliai pademonstravo \u012fsp\u016bding\u0105 0,88 tikslum\u0105 nustatant cukrini\u0173 runkeli\u0173 augalus naikinusias ligas, o ROC kreiv\u0117s plotas (AUC) siek\u0117 0,898. Modeliai pasi\u017eym\u0117jo dideliu prognozavimo pasitik\u0117jimu, tiksliai atskirdami sergan\u010dius ir sveikus augalus.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full is-resized\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"414\" height=\"194\" data-attachment-id=\"10630\" data-permalink=\"https:\/\/geopard.tech\/lt\/blog\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\/example-of-input-output-of-object-detection\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Example-of-input-output-of-object-detection.png?fit=414%2C194&amp;ssl=1\" data-orig-size=\"414,194\" data-comments-opened=\"1\" data-image-meta=\"{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;0&quot;}\" data-image-title=\"Example of input-output of object detection\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Example-of-input-output-of-object-detection.png?fit=414%2C194&amp;ssl=1\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Example-of-input-output-of-object-detection.png?resize=414%2C194&#038;ssl=1\" alt=\"Pavyzdys \u012f\u0117jimo-i\u0161\u0117jimo objekto aptikimui\" class=\"wp-image-10630\" style=\"width:750px;height:auto\" srcset=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Example-of-input-output-of-object-detection.png?w=414&amp;ssl=1 414w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Example-of-input-output-of-object-detection.png?resize=300%2C141&amp;ssl=1 300w\" sizes=\"(max-width: 414px) 100vw, 414px\" \/><figcaption class=\"wp-element-caption\">Pavyzdys \u012f\u0117jimo-i\u0161\u0117jimo objekto aptikimui<\/figcaption><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\">Projekte buvo naudojamas sistemingas lig\u0173 aptikimo metodas, vaizdai suskirstyti \u012f standartizuotus fragmentus. \u0160ie fragmentai buvo kruop\u0161\u010diai anotacijami naudojant interaktyvius \u012frankius lig\u0173 paveiktoms sritims nustatyti. Objekt\u0173 aptikimas dar labiau padidino tikslum\u0105, apibr\u0117\u017eiant ribojan\u010dius langelius aplink augalus, taip palengvinant tiksl\u0173 augal\u0173 sveikatos steb\u0117jim\u0105.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-plant-production-prediction\">Augal\u0173 gamybos prognoz\u0117<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Augal\u0173 gamybos prognozavimo srityje dirbtinio intelekto modeliai naudojosi aplinkos duomenimis, tokiais kaip oro s\u0105lygos ir dirvo\u017eemio parametrai, siekdami prognozuoti kult\u016br\u0173 derli\u0173. Buvo pasitelkti regresijos modeliai, tokie kaip \u201eIsolation Forest\u201c, \u201eLinear Regression\u201c ir \u201eRidge Regression\u201c.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u0160ie modeliai integravimo skaitines ypatybes, i\u0161gautas i\u0161 ribojan\u010di\u0173 sta\u010diakampi\u0173 sri\u010di\u0173, kartu su dirvo\u017eemio duomenimis, siekiant optimizuoti tr\u0105\u0161\u0173 naudojim\u0105.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full is-resized\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"810\" height=\"1076\" data-attachment-id=\"10632\" data-permalink=\"https:\/\/geopard.tech\/lt\/blog\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\/sugar-beet-on-test-field\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Sugar-beet-on-test-field.png?fit=1542%2C2048&amp;ssl=1\" data-orig-size=\"1542,2048\" data-comments-opened=\"1\" data-image-meta=\"{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;0&quot;}\" data-image-title=\"Sugar beet on test field\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Sugar-beet-on-test-field.png?fit=771%2C1024&amp;ssl=1\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Sugar-beet-on-test-field.png?resize=810%2C1076&#038;ssl=1\" alt=\"Cukriniai runkeliai bandym\u0173 lauke\" class=\"wp-image-10632\" style=\"width:700px;height:auto\" srcset=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Sugar-beet-on-test-field.png?w=1542&amp;ssl=1 1542w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Sugar-beet-on-test-field.png?resize=226%2C300&amp;ssl=1 226w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Sugar-beet-on-test-field.png?resize=771%2C1024&amp;ssl=1 771w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Sugar-beet-on-test-field.png?resize=768%2C1020&amp;ssl=1 768w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Sugar-beet-on-test-field.png?resize=1157%2C1536&amp;ssl=1 1157w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Sugar-beet-on-test-field.png?resize=150%2C200&amp;ssl=1 150w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Sugar-beet-on-test-field.png?resize=1200%2C1594&amp;ssl=1 1200w\" sizes=\"(max-width: 810px) 100vw, 810px\" \/><figcaption class=\"wp-element-caption\">Cukriniai runkeliai bandym\u0173 lauke<\/figcaption><\/figure>\n<\/div>\n\n\n<h2 class=\"wp-block-heading\" id=\"h-model-deployment-considerations\">Modelio diegimo svarstymai<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Sukurt\u0173 modeli\u0173 diegimo strategijos buvo vertinamos tiek kra\u0161to \u012frenginiams, tiek debes\u0173 platformoms. Modeliai, diegiami kra\u0161to \u012frenginiuose, suteik\u0117 privalum\u0173, toki\u0173 kaip ma\u017eesn\u0117s i\u0161laidos ir ma\u017eesn\u0117 latentija.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Ta\u010diau toks metodas gali lemti galimo tikslumo praradim\u0105 d\u0117l technin\u0117s \u012frangos apribojim\u0173. Kita vertus, debes\u0173 sprendimai pasi\u016bl\u0117 greitesn\u012f i\u0161vedimo laik\u0105 naudojant didelio na\u0161umo GPU, ta\u010diau gal\u0117jo pareikalauti papildom\u0173 i\u0161laid\u0173 ir priklaus\u0117 nuo interneto ry\u0161io, kuris gal\u0117jo sukelti ry\u0161io v\u0117lavim\u0105.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-comparative-analysis-with-5g-network\">Lyginamoji analiz\u0117 su 5G tinklu<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Lyginamoji analiz\u0117 parod\u0117, kad naudojant 5G tinkl\u0105 \u017eymiai pager\u0117jo cukrini\u0173 runkeli\u0173 segmentavimas, palyginti su tradicin\u0117mis 4G\/WiFi sistemomis. \u0160is patobulinimas pasirei\u0161k\u0117 trumpesniu vidutiniu s\u0105rankos ir tinklo laiku, pabr\u0117\u017eiant 5G technologijos d\u0117ka pasiekt\u0105 efektyvum\u0105.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Duomen\u0173 paruo\u0161imo procesas<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Duomen\u0173 paruo\u0161imo procesas ap\u0117m\u0117 sveik\u0173 ir sergan\u010di\u0173 augal\u0173 duomen\u0173 rinkini\u0173 rinkim\u0105, pikt\u017eoli\u0173 aptikim\u0105, augimo stadij\u0173 identifikavim\u0105 ir vaizd\u0173 i\u0161skyrim\u0105 i\u0161 4K \u017eali\u0173j\u0173 vaizdo \u012fra\u0161\u0173. Duomenims paruo\u0161ti analizei buvo naudoti tokie metodai kaip histogramos i\u0161lyginimas, vaizd\u0173 filtravimas ir HSV spalv\u0173 erdv\u0117s transformavimas.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Buvo surinkti sveiki cukrini\u0173 runkeli\u0173 lap\u0173 ir sergan\u010di\u0173 m\u0117gini\u0173, toki\u0173 kaip kukur\u016bz\u0173 lapai su pilk\u0105ja d\u0117m\u0117tlig\u0119, pavyzd\u017eiai. Ligos po\u017eymi\u0173 i\u0161skyrimas ap\u0117m\u0117 lapo atskyrim\u0105 nuo fono, vaizd\u0173 pakeitim\u0105, transformavim\u0105 ir sujungim\u0105, siekiant sukurti realius analizei skirtus pavyzd\u017eius.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"810\" height=\"200\" data-attachment-id=\"10633\" data-permalink=\"https:\/\/geopard.tech\/lt\/blog\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\/annotation-process-for-segmentation\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Annotation-process-for-segmentation.png?fit=2048%2C505&amp;ssl=1\" data-orig-size=\"2048,505\" data-comments-opened=\"1\" data-image-meta=\"{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;0&quot;}\" data-image-title=\"Annotation process for segmentation\" data-image-description=\"\" data-image-caption=\"&lt;p&gt;Caption 3&lt;\/p&gt;\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Annotation-process-for-segmentation.png?fit=1024%2C253&amp;ssl=1\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Annotation-process-for-segmentation.png?resize=810%2C200&#038;ssl=1\" alt=\"Segmentavimo anotavimo procesas\" class=\"wp-image-10633\" srcset=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Annotation-process-for-segmentation.png?w=2048&amp;ssl=1 2048w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Annotation-process-for-segmentation.png?resize=300%2C74&amp;ssl=1 300w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Annotation-process-for-segmentation.png?resize=1024%2C253&amp;ssl=1 1024w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Annotation-process-for-segmentation.png?resize=768%2C189&amp;ssl=1 768w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Annotation-process-for-segmentation.png?resize=1536%2C379&amp;ssl=1 1536w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Annotation-process-for-segmentation.png?resize=1200%2C296&amp;ssl=1 1200w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Annotation-process-for-segmentation.png?w=1620&amp;ssl=1 1620w\" sizes=\"(max-width: 810px) 100vw, 810px\" \/><figcaption class=\"wp-element-caption\">Segmentavimo anotavimo procesas<\/figcaption><\/figure>\n<\/div>\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Aktyvaus mokymosi ciklas<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Buvo inicijuotas aktyvaus mokymosi ciklas su nepa\u017eym\u0117tais duomenimis, kurie buvo naudojami atrankos modeliams apmokyti. \u0160ie modeliai generavo anotavimo u\u017eklausas, kurias atliko \u017emoni\u0173 anotatoriai, nuolat tobulindami modelio tikslum\u0105 per iteracinius mokymo ir anotavimo ciklus.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Duomen\u0173 anotavimas naudojant daugialyp\u012f pamatin\u012f model\u012f<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Sprend\u017eiant riboto pa\u017eenklint\u0173 duomen\u0173 i\u0161\u0161\u016bk\u012f, projekte buvo pasitelkti patikimi pagrindiniai modeliai, siekiant sugeneruoti tikruosius anota\u010dius. Vis\u0173 pirma, CLIP, \u201eOpenAI\u201c sukurtas transformeri\u0173 modelis, apmokytas naudojant did\u017eiul\u012f, daugiau nei 400 milijon\u0173 paveiksl\u0117li\u0173-teksto por\u0173, duomen\u0173 rinkin\u012f, atliko svarb\u0173 vaidmen\u012f.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Naudodamas Vision Transformers kaip savo pagrind\u0105, CLIP pasiek\u0117 nuostab\u0173 95% tikslum\u0105 validavimo rinkiniuose, \u012fgudusiai su dideliu tikslumu klasifikuodamas vaizdus \u012f skirtingas klases, tokias kaip cukriniai runkeliai ir pikt\u017eol\u0117s.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Dron\u0173 technologijos duomen\u0173 rinkimui<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Viena i\u0161 kritini\u0173 technologij\u0173, naudot\u0173 projekte, buvo dron\u0173, apr\u016bpint\u0173 RGB kameromis, kurios fiksuoja 4K vaizdo \u012fra\u0161us, naudojimas. \u0160ie dronai pateik\u0117 detalius vaizdus (3840 \u00d7 2160 rai\u0161ka) analizei.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u0160i\u0173 vaizd\u0173 i\u0161ankstinis apdorojimas \u017eymiai padidino modelio tikslum\u0105, ypa\u010d pastebimas pager\u0117jimas tokiuose modeliuose kaip VGGNet (+38,52%), ResNet50 (+21,14%), DenseNet121 (+7,53%) ir MobileNet (+6,6%).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Vaizdo kontrastui pagerinti naudotos tokios technikos kaip histogramos i\u0161lyginimas, o transformacija \u012f HSV spalv\u0173 erdv\u0119 pad\u0117jo pary\u0161kinti augal\u0173 plotus ir i\u0161ry\u0161kinti reik\u0161mingas ypatybes.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Sintetini\u0173 duomen\u0173 generavimas<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Sprend\u017eiant ribotos vaizdin\u0117s informacijos tr\u016bkumo problem\u0105, dirbtinio intelekto ir ma\u0161ininio mokymosi pagalba buvo sukurtos sintetin\u0117s duomen\u0173 baz\u0117s. Duomenys buvo renkami naudojant dronus, skraidan\u010dius 1\u20134 metr\u0173 auk\u0161tyje ir 2 m\/s ar didesniu grei\u010diu, naudojant RGB kameras.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full is-resized\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"810\" height=\"610\" data-attachment-id=\"10634\" data-permalink=\"https:\/\/geopard.tech\/lt\/blog\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\/emulation-environment\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Emulation-environment.png?fit=2048%2C1542&amp;ssl=1\" data-orig-size=\"2048,1542\" data-comments-opened=\"1\" data-image-meta=\"{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;0&quot;}\" data-image-title=\"Emulation environment\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Emulation-environment.png?fit=1024%2C771&amp;ssl=1\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Emulation-environment.png?resize=810%2C610&#038;ssl=1\" alt=\"Emuliacin\u0117 aplinka\" class=\"wp-image-10634\" style=\"width:646px;height:auto\" srcset=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Emulation-environment.png?w=2048&amp;ssl=1 2048w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Emulation-environment.png?resize=300%2C226&amp;ssl=1 300w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Emulation-environment.png?resize=1024%2C771&amp;ssl=1 1024w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Emulation-environment.png?resize=768%2C578&amp;ssl=1 768w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Emulation-environment.png?resize=1536%2C1157&amp;ssl=1 1536w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Emulation-environment.png?resize=400%2C300&amp;ssl=1 400w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Emulation-environment.png?resize=200%2C150&amp;ssl=1 200w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Emulation-environment.png?resize=1200%2C904&amp;ssl=1 1200w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Emulation-environment.png?w=1620&amp;ssl=1 1620w\" sizes=\"(max-width: 810px) 100vw, 810px\" \/><figcaption class=\"wp-element-caption\">Emuliacin\u0117 aplinka<\/figcaption><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\">Taip pat buvo naudojamos kitos transporto priemon\u0117s, pavyzd\u017eiui, traktoriai, duomenims rinkti. \u0160is sintetini\u0173 duomen\u0173 generavimas pasirod\u0117 ypa\u010d naudingas aptinkant cukrini\u0173 runkeli\u0173 ligas.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-conclusion\">I\u0161vada<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Projektas \u201c5G tinklai kaip darnaus \u016bkininkavimo mokymosi realiame laike leid\u0117jai\u201d s\u0117kmingai pademonstravo, kaip 5G technologija gali pagerinti cukrini\u0173 runkeli\u0173 auginimo ekologin\u012f, ekonomin\u012f ir tvarumo aspektus. Bendradarbiaujant su HSHL ir Pfeifer &amp; Langen, projekte buvo integruotas duomen\u0173 rinkimas realiuoju laiku ir dirbtinio intelekto valdoma analiz\u0117, siekiant padidinti efektyvum\u0105 ir suma\u017einti nereikalingus lauko ap\u017ei\u016br\u0117jim\u0173 skai\u010dius. <\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Savaiminio 5G tinklo diegimas leido tiksliai naudoti tr\u0105\u0161as ir augal\u0173 apsaugos produktus. Bendrov\u0117 \u201eGeopard Agriculture\u201c atliko itin svarb\u0173 vaidmen\u012f kuriant augal\u0173 aptikimo ir steb\u0117jimo scenarijus bei sukuriant prototipin\u0119 ma\u0161ininio mokymosi sistem\u0105 5G \u017eem\u0117s \u016bkio aplinkai. Projekto s\u0117km\u0117 pabr\u0117\u017e\u0117 pa\u017eangi\u0173 technologij\u0173 svarb\u0105 tvariai \u017eemdirbystei, i\u0161ry\u0161kinant 5G potencial\u0105 skatinti inovacijas ir efektyvum\u0105. <\/p>","protected":false},"excerpt":{"rendered":"<p>D\u017eiaugiam\u0117s gal\u0117dami prane\u0161ti apie s\u0117kming\u0105 projekto \u201c5G tinklai kaip realaus laiko mokymosi tvariame \u016bkininkavime \u012fgalintojas\u201d, kur\u012f i\u0161 dalies remia\u2026<\/p>","protected":false},"author":210157960,"featured_media":10639,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","_eb_attr":"","content-type":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_wpcom_ai_launchpad_first_post":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"{title}\n\n{excerpt}\n\n{url}","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","enabled":false},"version":2},"_wpas_customize_per_network":false,"jetpack_post_was_ever_published":false},"categories":[1657,1367],"tags":[],"class_list":["post-10617","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-precision-farming","category-use-cases"],"acf":[],"yoast_head":"<!-- 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