{"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-umoznilo-ucenie-sa-v-realnom-case-v-udrzatelnom-polnohospodarstve-studia-o-produkcii-cukrovej-repy","status":"publish","type":"post","link":"https:\/\/geopard.tech\/sk\/blog\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\/","title":{"rendered":"5G-umo\u017enen\u00e9 u\u010denie v re\u00e1lnom \u010dase v udr\u017eate\u013enom po\u013enohospod\u00e1rstve: \u0160t\u00fadia cukrovej repy"},"content":{"rendered":"<p class=\"wp-block-paragraph\">S nad\u0161en\u00edm oznamujeme \u00faspe\u0161n\u00e9 dokon\u010denie projektu \u201c5G siete ako n\u00e1stroj na u\u010denie sa v re\u00e1lnom \u010dase v udr\u017eate\u013enom po\u013enohospod\u00e1rstve\u201d, ktor\u00fd \u010diasto\u010dne financovalo Ministerstvo hospod\u00e1rstva, priemyslu, ochrany kl\u00edmy a energetiky spolkovej krajiny Severn\u00e9 Por\u00fdnie-Vestf\u00e1lsko.<\/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\">T\u00e1to iniciat\u00edva predstavuje v\u00fdznamn\u00fd krok vpred v sk\u00faman\u00ed transforma\u010dn\u00e9ho potenci\u00e1lu technol\u00f3gie 5G v po\u013enohospod\u00e1rstve, konkr\u00e9tne zameranej na zlep\u0161enie ekologick\u00fdch, ekonomick\u00fdch a udr\u017eate\u013en\u00fdch aspektov pestovania cukrovej repy.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Vyu\u017eila n\u00edzku latenciu 5G na integr\u00e1ciu pokro\u010dil\u00fdch informa\u010dn\u00fdch technologick\u00fdch syst\u00e9mov v re\u00e1lnom \u010dase, \u010do umo\u017enilo okam\u017eit\u00e9 reakcie na \u00fadaje zo senzorov a polohy v r\u00e1mci vopred definovan\u00fdch \u010dasov\u00fdch r\u00e1mcov.<\/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\/sk\/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=\"Obr\u00e1zok zo z\u00e1vere\u010dn\u00e9ho podujatia prezent\u00e1cie projektu na Hochschule Hamm-Lippstadt (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\">Obr\u00e1zok zo z\u00e1vere\u010dn\u00e9ho podujatia prezent\u00e1cie projektu na Hochschule Hamm-Lippstadt (HSHL)<\/figcaption><\/figure>\n<\/div>\n\n\n<h2 class=\"wp-block-heading\" id=\"h-project-focus-and-partnership\">Zameranie projektu a partnerstvo<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">V spolupr\u00e1ci s partnermi z HSHL a s podporou spolo\u010dnosti Pfeifer &amp; Langen sa projekt zameral na \u0161t\u00fadium cel\u00e9ho \u017eivotn\u00e9ho cyklu pestovania cukrovej repy na poliach patriacich partnerom. Jeho cie\u013eom bolo demon\u0161trova\u0165, ako by 5G mohlo sl\u00fa\u017ei\u0165 ako k\u013e\u00fa\u010dov\u00fd technologick\u00fd katalyz\u00e1tor v po\u013enohospod\u00e1rskom sektore Severn\u00e9ho Por\u00fdnia-Vestf\u00e1lska a prezentova\u0165 jeho potenci\u00e1l ako n\u00e1stroja umo\u017e\u0148uj\u00faceho inov\u00e1cie a efekt\u00edvnos\u0165.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-role-of-geopard-agriculture\">\u00daloha po\u013enohospod\u00e1rstva GeoPard<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Spolo\u010dnos\u0165 GeoPard Agriculture zohrala k\u013e\u00fa\u010dov\u00fa \u00falohu pri definovan\u00ed a implement\u00e1cii k\u013e\u00fa\u010dov\u00fdch aspektov projektu vr\u00e1tane scen\u00e1rov pre detekciu, monitorovanie a predikciu produkcie rastl\u00edn. Vyvinuli sme prototyp syst\u00e9mu umelej inteligencie prisp\u00f4soben\u00e9ho pre po\u013enohospod\u00e1rske prostredie 5G, spustili sme modely v cloudovej infra\u0161trukt\u00fare a vytvorili sme mobiln\u00fa aplik\u00e1ciu pre interakciu s cloudov\u00fdmi modelmi v re\u00e1lnom \u010dase.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-technological-integration\">Technologick\u00e1 integr\u00e1cia<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Met\u00f3dy umelej inteligencie (AI) boli nasaden\u00e9 prostredn\u00edctvom robustnej cloudovej infra\u0161trukt\u00fary s vysok\u00fdmi v\u00fdpo\u010dtov\u00fdmi schopnos\u0165ami. Algoritmy AI kategorizovali rastliny v re\u00e1lnom \u010dase po\u010das ka\u017ed\u00e9ho kr\u00ed\u017eenia a monitorovali ich rast po\u010das cel\u00e9ho ich \u017eivotn\u00e9ho cyklu, \u010d\u00edm sa eliminovala potreba zbyto\u010dn\u00fdch n\u00e1v\u0161tev ter\u00e9nu v\u00fdlu\u010dne na \u00fa\u010dely zberu \u00fadajov.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Tento pokrok umo\u017enil presn\u00fa aplik\u00e1ciu hnoj\u00edv a pr\u00edpravkov na ochranu plod\u00edn, pri\u010dom dynamicky upravoval d\u00e1vkovanie po\u010das kri\u017eovan\u00ed pomocou algoritmov strojov\u00e9ho u\u010denia.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-deployment-of-unmanned-vehicles\">Nasadenie bezpilotn\u00fdch vozidiel<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Projekt navy\u0161e vyu\u017eil zn\u00ed\u017een\u00fa latenciu 5G na nasadenie bezpilotn\u00fdch vozidiel na monitorovanie rastl\u00edn a zber \u00fadajov. Tieto vozidl\u00e1 zohrali k\u013e\u00fa\u010dov\u00fa \u00falohu pri zhroma\u017e\u010fovan\u00ed inform\u00e1ci\u00ed v re\u00e1lnom \u010dase a \u010fal\u0161ej optimaliz\u00e1cii po\u013enohospod\u00e1rskych postupov.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-project-outcomes-enhancing-sugar-beet-production-with-5g-technology\">V\u00fdsledky projektu: Zv\u00fd\u0161enie produkcie cukrovej repy pomocou technol\u00f3gie 5G<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Projekt demon\u0161troval, ako by technol\u00f3gia 5G mohla sl\u00fa\u017ei\u0165 ako transforma\u010dn\u00fd n\u00e1stroj v po\u013enohospod\u00e1rskom sektore Severn\u00e9ho Por\u00fdnia-Vestf\u00e1lska, a to anal\u00fdzou cel\u00e9ho \u017eivotn\u00e9ho cyklu pestovania cukrovej repy, pri\u010dom zd\u00f4raznil podstatn\u00e9 zlep\u0161enia, ktor\u00e9 technol\u00f3gia 5G umo\u017enila. Na efekt\u00edvnu demon\u0161tr\u00e1ciu v\u00fdsledkov projektu v\u0161ak v\u00fdskumn\u00edci pou\u017eili pracovn\u00e9 bal\u00edky obsahuj\u00face r\u00f4zne scen\u00e1re a infra\u0161trukt\u00fary.<\/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\/sk\/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=\"Testovacie pole s cukrovou repou \" 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\">Testovacie pole s cukrovou repou<\/figcaption><\/figure>\n<\/div>\n\n\n<h3 class=\"wp-block-heading\" id=\"h-scenario-definition-considering-existing-geodata-and-ml-infrastructure\">Defin\u00edcia scen\u00e1ra s oh\u013eadom na existuj\u00face geod\u00e1ta a infra\u0161trukt\u00faru strojov\u00e9ho u\u010denia<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Projekt demon\u0161troval, ako by sa dali tradi\u010dn\u00e9 procesy v r\u00e1mci \u017eivotn\u00e9ho cyklu produkcie cukrovej repy vylep\u0161i\u0165 integr\u00e1ciou technol\u00f3gie 5G. Medzi k\u013e\u00fa\u010dov\u00e9 ciele patrilo:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Vyvinul som scen\u00e1re pripraven\u00e9 na implement\u00e1ciu pre rozpozn\u00e1vanie, monitorovanie a predikciu produkcie rastl\u00edn.<\/li>\n\n\n\n<li>Stanoven\u00e9 technick\u00e9 po\u017eiadavky potrebn\u00e9 pre \u00faspe\u0161n\u00e9 nasadenie t\u00fdchto scen\u00e1rov.<\/li>\n\n\n\n<li>Identifikovali a pos\u00fadili relevantn\u00e9 ekologick\u00e9 a ekonomick\u00e9 ukazovatele na vyhodnotenie pridanej hodnoty, ktor\u00fa prin\u00e1\u0161a sie\u0165 5G.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">T\u00e1to f\u00e1za zd\u00f4raznila z\u00e1v\u00e4zok projektu integrova\u0165 \u0161pi\u010dkov\u00e9 technol\u00f3gie s existuj\u00facimi po\u013enohospod\u00e1rskymi postupmi. T\u00e1to architekt\u00fara vyu\u017eila vysokor\u00fdchlostn\u00e9 pripojenie siete 5G na u\u013eah\u010denie zberu a spracovania \u00fadajov v re\u00e1lnom \u010dase medzi okrajov\u00fdmi zariadeniami a cloudom. Cloudov\u00e1 infra\u0161trukt\u00fara poskytovala z\u00e1kladn\u00e9 zdroje na \u0161kolenie a nasadzovanie rozsiahlych modelov umelej inteligencie, zatia\u013e \u010do platforma umelej inteligencie pon\u00fakala robustn\u00e9 n\u00e1stroje na v\u00fdvoj a nasadzovanie modelov. Aplika\u010dn\u00e1 vrstva prezentovala koncov\u00fdm pou\u017e\u00edvate\u013eom u\u017eito\u010dn\u00e9 poznatky odvoden\u00e9 z modelov umelej inteligencie, \u010d\u00edm sa zlep\u0161ili ich rozhodovacie schopnosti.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-machine-learning-and-ai-in-the-context-of-5g\">Strojov\u00e9 u\u010denie a umel\u00e1 inteligencia v kontexte 5G<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Cie\u013eom tejto \u010dasti bolo prisp\u00f4sobi\u0165 existuj\u00face syst\u00e9my strojov\u00e9ho u\u010denia a umelej inteligencie tak, aby zodpovedali vy\u0161\u0161ie uveden\u00fdm scen\u00e1rom, a pod\u013ea toho ich optimalizova\u0165. Medzi k\u013e\u00fa\u010dov\u00e9 ciele patrilo:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Definova\u0165 ciele syst\u00e9mu a vyvin\u00fa\u0165 architekt\u00faru syst\u00e9mu<\/li>\n\n\n\n<li>Zhroma\u017eden\u00e9 skuto\u010dn\u00e9 \u00fadaje na tr\u00e9novanie a overovanie modelov umelej inteligencie.<\/li>\n\n\n\n<li>Vytvorila a anotovala vhodn\u00fa datab\u00e1zu prisp\u00f4soben\u00fa na identifik\u00e1ciu a monitorovanie rastl\u00edn.<\/li>\n\n\n\n<li>Bezprobl\u00e9mov\u00e1 integr\u00e1cia modelov umelej inteligencie do infra\u0161trukt\u00fary siete 5G.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">V tejto f\u00e1ze zohrali k\u013e\u00fa\u010dov\u00fa \u00falohu okrajov\u00e9 zariadenia vybaven\u00e9 SIM kartami mobiln\u00fdch telef\u00f3nov vyu\u017e\u00edvaj\u00facimi technol\u00f3giu 5G. K\u013e\u00fa\u010dov\u00e9 ukazovatele v\u00fdkonnosti (KPI), ako je latencia alebo latencia medzi koncov\u00fdmi bodmi (E2E), boli d\u00f4kladne monitorovan\u00e9. Merania zah\u0155\u0148ali pos\u00fadenie spo\u013eahlivosti a dostupnosti presne prijat\u00fdch d\u00e1tov\u00fdch paketov spolu s anal\u00fdzou r\u00fdchlosti prenosu pou\u017e\u00edvate\u013esk\u00fdch d\u00e1t a \u0161pi\u010dkov\u00fdch r\u00fdchlost\u00ed prenosu d\u00e1t.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Okrem toho boli predpoklady zalo\u017een\u00e9 na streamovan\u00ed videa v rozl\u00ed\u0161en\u00ed UHD vo form\u00e1te MP4, pren\u00e1\u0161an\u00e9ho prostredn\u00edctvom protokolu Transmission Control Protocol (TCP). Medzi sk\u00faman\u00e9 potenci\u00e1lne rie\u0161enia patrila optimaliz\u00e1cia s jednotliv\u00fdmi obr\u00e1zkami namiesto s\u00favisl\u00fdch video streamov, vykon\u00e1vanie z\u00e1kladn\u00fdch optimaliz\u00e1ci\u00ed priamo na okrajov\u00fdch zariadeniach a implement\u00e1cia techn\u00edk kvantiz\u00e1cie modelu na zv\u00fd\u0161enie efekt\u00edvnosti.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-cloud-infrastructure-and-aws-services\">Cloudov\u00e1 infra\u0161trukt\u00fara a slu\u017eby AWS<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Projekt sa vo ve\u013ekej miere spoliehal na cloudov\u00fa infra\u0161trukt\u00faru vyu\u017e\u00edvaj\u00facu slu\u017eby AWS, ako s\u00fa Lambda, SageMaker, S3, CloudWatch a RDS, ktor\u00e9 zohrali k\u013e\u00fa\u010dov\u00fa \u00falohu pri poskytovan\u00ed potrebn\u00fdch zdrojov na \u0161kolenie a nasadenie modelov umelej inteligencie.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">AWS Lambda sa pou\u017eila na efekt\u00edvnu spr\u00e1vu in\u0161tanci\u00ed a obsluhu aplik\u00e1ci\u00ed, zatia\u013e \u010do AWS SageMaker u\u013eah\u010dil vytvorenie robustn\u00fdch kan\u00e1lov strojov\u00e9ho u\u010denia. \u00dalo\u017en\u00e9 rie\u0161enia ako S3, CloudWatch a RDS boli nevyhnutn\u00e9 na ukladanie s\u00faborov \u00fadajov a protokolov, ktor\u00e9 s\u00fa k\u013e\u00fa\u010dov\u00e9 pre prev\u00e1dzku modelov strojov\u00e9ho u\u010denia a neur\u00f3nov\u00fdch siet\u00ed.<\/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\/sk\/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=\"Cloudov\u00e1 infra\u0161trukt\u00fara AWS\" 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\">Cloudov\u00e1 infra\u0161trukt\u00fara AWS<\/figcaption><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\">T\u00e1to infra\u0161trukt\u00fara teda podporovala mo\u017enosti spracovania \u00fadajov v re\u00e1lnom \u010dase, ktor\u00e9 umo\u017enila sie\u0165 5G.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-5g-network-latency\">Latencia siete 5G<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Siete 5G boli navrhnut\u00e9 tak, aby dosahovali ultran\u00edzku latenciu, ktor\u00e1 sa zvy\u010dajne pohybuje od 1 do 10 milisek\u00fand. T\u00e1to latencia odr\u00e1\u017eala \u010das potrebn\u00fd na prenos d\u00e1t medzi mobiln\u00fdmi zariadeniami a servermi AWS prostredn\u00edctvom siete 5G. Latenciu ovplyv\u0148ovali aj \u0161pecifick\u00e9 mo\u017enosti spracovania \u00fadajov zariadenia, ako napr\u00edklad r\u00fdchlos\u0165 sn\u00edmania a spracovania fotografi\u00ed v smartf\u00f3noch s vysokov\u00fdkonn\u00fdmi procesormi.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">R\u00fdchlosti nahr\u00e1vania d\u00e1t v sieti 5G a ve\u013ekos\u0165 fotografie ovplyvnili \u010dasy prenosu d\u00e1t do AWS. AWS \u010falej prispel k latencii s \u010dasmi spracovania \u00faloh, ako je detekcia a segment\u00e1cia zalo\u017een\u00e1 na neur\u00f3nov\u00fdch sie\u0165ach, ktor\u00e9 sa l\u00ed\u0161ili v z\u00e1vislosti od zlo\u017eitosti algoritmu a efekt\u00edvnosti slu\u017eby AWS. Po spracovan\u00ed boli v\u00fdsledky stiahnut\u00e9 sp\u00e4\u0165 do mobiln\u00fdch zariaden\u00ed, \u010do bolo ovplyvnen\u00e9 r\u00fdchlos\u0165ou s\u0165ahovania 5G a ve\u013ekos\u0165ou v\u00fdsledn\u00fdch d\u00e1t.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-plant-recognition-using-ai\">Rozpozn\u00e1vanie rastl\u00edn pomocou umelej inteligencie<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">V oblasti rozpozn\u00e1vania rastl\u00edn zah\u0155\u0148ali procesy riaden\u00e9 umelou inteligenciou vytvorenie komplexnej datab\u00e1zy obr\u00e1zkov rastl\u00edn pre tr\u00e9novacie algoritmy zalo\u017een\u00e9 na neur\u00f3nov\u00fdch sie\u0165ach. Tieto algoritmy boli tr\u00e9novan\u00e9 na rozl\u00ed\u0161enie druhov cukrovej repy od in\u00fdch rastl\u00edn rozpozn\u00e1van\u00edm znakov \u0161pecifick\u00fdch pre dan\u00fd typ rastliny, ako s\u00fa tvary listov, farby kvetov at\u010f.<\/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\/sk\/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=\"Fenologick\u00fd v\u00fdvoj rastl\u00edn cukrovej repy\" 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\">Fenologick\u00fd v\u00fdvoj rastl\u00edn cukrovej repy. Zdroj: <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\">Rozpozn\u00e1van\u00edm rastl\u00edn tu mysl\u00edme \u00falohu detekcie buriny a segment\u00e1cie rastl\u00edn cukrovej repy.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Detekcia buriny<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Na detekciu buriny projekt vyu\u017eil MobileNet-v3, ktor\u00fd bol tr\u00e9novan\u00fd s rozsiahlym roz\u0161\u00edren\u00edm d\u00e1t a v\u00e1\u017een\u00fdm vzorkovan\u00edm. Tento model dosiahol p\u00f4sobiv\u00fa presnos\u0165 0,984 a AUC 0,998.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Segment\u00e1cia cukrovej repy<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Na segmenta\u010dn\u00e9 \u00falohy boli pou\u017eit\u00e9 modely ako YOLACT, ResNeSt, SOLO a U-net na presn\u00e9 vymedzenie jednotliv\u00fdch vzoriek cukrovej repy v r\u00e1mci obr\u00e1zkov. N\u00e1sledne bol na z\u00e1klade r\u00f4znych krit\u00e9ri\u00ed: r\u00fdchlos\u0165, \u010das inferencie at\u010f. vybran\u00fd najefekt\u00edvnej\u0161\u00ed model. D\u00e1ta na segment\u00e1ciu boli z\u00edskan\u00e9 z RGB obr\u00e1zkov zachyten\u00fdch dronmi, ktor\u00e9 boli na \u00fa\u010dely tr\u00e9novania a valid\u00e1cie upraven\u00e9 a anotovan\u00e9.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u00dalohy segment\u00e1cie zah\u0155\u0148ali vytv\u00e1ranie masiek, ktor\u00e9 presne vymedzovali hranice rastl\u00edn. T\u00e1to met\u00f3da zn\u00ed\u017eila \u00fasilie \u013eudsk\u00e9ho anotovania a z\u00e1rove\u0148 optimalizovala efektivitu. Uprednostnen\u00edm ozna\u010dovania n\u00e1ro\u010dn\u00fdch vzoriek sa v\u00fdrazne zlep\u0161il v\u00fdkon modelu. Iterat\u00edvne strat\u00e9gie pretr\u00e9novania a neistoty vzorkovania sa uk\u00e1zali ako \u00fa\u010dinn\u00e9 a dosiahli mieru presnosti segment\u00e1cie presahuj\u00facu 98% v r\u00f4znych \u0161t\u00e1di\u00e1ch rastu.<\/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\/sk\/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=\"Pr\u00edklad vstupno-v\u00fdstupnej segment\u00e1cie\" 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\">Pr\u00edklad vstupno-v\u00fdstupnej segment\u00e1cie<\/figcaption><\/figure>\n<\/div>\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Vyhodnotenie modelu<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Model bol tr\u00e9novan\u00fd s d\u00f4sledn\u00fdm roz\u0161irovan\u00edm d\u00e1t. Model bol vyhodnoten\u00fd pomocou r\u00f4znych metr\u00edk vr\u00e1tane prieniku cez \u00faniu (IoU). Inferen\u010dn\u00e1 anal\u00fdza pre zostaven\u00fd model, vykonan\u00e1 na podmno\u017eine z d\u00e1tov\u00e9ho s\u00faboru \u2018sadenice rastl\u00edn v2\u2019, preuk\u00e1zala presnos\u0165 81%. V\u00fdpo\u010det inferen\u010dn\u00e9ho \u010dasu trval pribli\u017ene 320 milisek\u00fand po 7-sekundovej inicializa\u010dnej peri\u00f3de, ktor\u00e1 je potrebn\u00e1 iba raz za rel\u00e1ciu.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Pri monitorovan\u00ed rastl\u00edn s vyu\u017eit\u00edm umelej inteligencie (AI) kamery a senzory zachyt\u00e1vali d\u00f4le\u017eit\u00e9 \u00fadaje o rastlin\u00e1ch, ktor\u00e9 analyzovalo strojov\u00e9 u\u010denie a algoritmy AI. T\u00e1to anal\u00fdza zohrala k\u013e\u00fa\u010dov\u00fa \u00falohu pri posudzovan\u00ed zdravia rastl\u00edn, identifik\u00e1cii stresu, chor\u00f4b alebo in\u00fdch faktorov ovplyv\u0148uj\u00facich rast.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Aplik\u00e1cie siahali od optimaliz\u00e1cie po\u013enohospod\u00e1rskej produktivity a\u017e po monitorovanie pr\u00edrodn\u00fdch ekosyst\u00e9mov, ako s\u00fa lesy, pomoc pri ochrane pr\u00edrody a zlep\u0161enie pochopenia vplyvov na \u017eivotn\u00e9 prostredie.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-object-detection-in-plant-monitoring\">Detekcia objektov pri monitorovan\u00ed rastl\u00edn<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">\u010eal\u0161ou f\u00e1zou po segment\u00e1cii rastl\u00edn cukrovej repy je detekcia objektov zameran\u00e1 na pochopenie \u0161pecif\u00edk ka\u017edej rastliny z h\u013eadiska zdravia, rastu a \u010fal\u0161\u00edch faktorov. Na detekciu objektov pri monitorovan\u00ed rastl\u00edn boli nasaden\u00e9 pokro\u010dil\u00e9 modely ako YOLOv4, MobileNetV2 a VGG-19 s mechanizmami pozornosti. Tieto modely analyzovali segmentovan\u00e9 obrazy cukrovej repy s cie\u013eom detekova\u0165 \u0161pecifick\u00e9 oblasti stresu a chor\u00f4b, \u010do umo\u017enilo presn\u00e9 a cielen\u00e9 z\u00e1sahy.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Projekt dosiahol v\u00fdznamn\u00e9 m\u00ed\u013eniky v detekcii chor\u00f4b, tr\u00e9novan\u00ed modelov ResNet-18 a ResNet-34 predtr\u00e9novan\u00fdch na ImageNet. Tieto modely preuk\u00e1zali p\u00f4sobiv\u00fa presnos\u0165 0,88 pri identifik\u00e1cii chor\u00f4b postihuj\u00facich rastliny cukrovej repy s plochou pod ROC krivkou (AUC) 0,898. Modely vykazovali vysok\u00fa spo\u013eahlivos\u0165 predikcie a presne rozli\u0161ovali medzi chor\u00fdmi a zdrav\u00fdmi rastlinami.<\/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\/sk\/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=\"Pr\u00edklad vstupno-v\u00fdstupnej detekcie objektu\" 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\">Pr\u00edklad vstupno-v\u00fdstupnej detekcie objektu<\/figcaption><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\">Projekt vyu\u017e\u00edval systematick\u00fd pr\u00edstup k detekcii chor\u00f4b, segmentoval sn\u00edmky do \u0161tandardizovan\u00fdch oblast\u00ed. Tieto oblasti boli d\u00f4kladne anotovan\u00e9 pomocou interakt\u00edvnych n\u00e1strojov na identifik\u00e1ciu oblast\u00ed postihnut\u00fdch chorobami. Detekcia objektov \u010falej zv\u00fd\u0161ila presnos\u0165 vyzna\u010den\u00edm ohrani\u010duj\u00facich r\u00e1m\u010dekov okolo rastl\u00edn, \u010do u\u013eah\u010dilo presn\u00e9 monitorovanie zdravia rastl\u00edn.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-plant-production-prediction\">Predikcia rastlinnej produkcie<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">V oblasti predikcie rastlinnej produkcie modely umelej inteligencie vyu\u017e\u00edvali environment\u00e1lne \u00fadaje, ako s\u00fa poveternostn\u00e9 podmienky a parametre p\u00f4dy, na predpovedanie v\u00fdnosov plod\u00edn. Pou\u017eili sa regresn\u00e9 modely ako Isolation Forest, Linear Regression a Ridge Regression.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Tieto modely integrovali numerick\u00e9 prvky extrahovan\u00e9 z oblast\u00ed ohrani\u010duj\u00facich r\u00e1m\u010dekov spolu s \u00fadajmi o p\u00f4de s cie\u013eom optimalizova\u0165 aplik\u00e1ciu hnoj\u00edv.<\/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\/sk\/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=\"Cukrov\u00e1 repa na testovacom poli\" 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\">Cukrov\u00e1 repa na testovacom poli<\/figcaption><\/figure>\n<\/div>\n\n\n<h2 class=\"wp-block-heading\" id=\"h-model-deployment-considerations\">\u00davahy o nasaden\u00ed modelu<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Strat\u00e9gie nasadenia vyvinut\u00fdch modelov boli vyhodnoten\u00e9 pre edge zariadenia aj cloudov\u00e9 platformy. Nasadenie modelov na edge zariadeniach pon\u00fakalo v\u00fdhody, ako s\u00fa zn\u00ed\u017een\u00e9 n\u00e1klady a ni\u017e\u0161ia latencia.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Tento pr\u00edstup v\u0161ak m\u00f4\u017ee vies\u0165 k zn\u00ed\u017eeniu potenci\u00e1lnej presnosti kv\u00f4li hardv\u00e9rov\u00fdm obmedzeniam. Na druhej strane, nasadenie cloudu pon\u00faka r\u00fdchlej\u0161ie inferen\u010dn\u00e9 \u010dasy s pou\u017eit\u00edm vysokov\u00fdkonn\u00fdch GPU, ale m\u00f4\u017ee vies\u0165 k dodato\u010dn\u00fdm n\u00e1kladom a je z\u00e1visl\u00e9 od internetov\u00e9ho pripojenia, \u010do m\u00f4\u017ee vies\u0165 k latencii komunik\u00e1cie.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-comparative-analysis-with-5g-network\">Porovn\u00e1vacia anal\u00fdza so sie\u0165ou 5G<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Porovn\u00e1vacia anal\u00fdza uk\u00e1zala, \u017ee vyu\u017eitie siete 5G v\u00fdrazne zlep\u0161ilo segment\u00e1ciu cukrovej repy v porovnan\u00ed s tradi\u010dn\u00fdmi nastaveniami 4G\/WiFi. Toto zlep\u0161enie sa prejavilo skr\u00e1ten\u00edm priemern\u00fdch \u010dasov nastavenia a pripojenia siete, \u010do zd\u00f4raz\u0148uje zv\u00fd\u0161enie efektivity dosiahnut\u00e9 v\u010faka technol\u00f3gii 5G.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Proces pr\u00edpravy \u00fadajov<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Proces pr\u00edpravy \u00fadajov zah\u0155\u0148al zhroma\u017e\u010fovanie s\u00faborov \u00fadajov o zdrav\u00fdch a chor\u00fdch rastlin\u00e1ch, detekciu buriny, identifik\u00e1ciu \u0161t\u00e1di\u00ed rastu a extrakciu obr\u00e1zkov zo 4K videa v surovom form\u00e1te. Na pr\u00edpravu \u00fadajov na anal\u00fdzu boli pou\u017eit\u00e9 techniky ako vyrovn\u00e1vanie histogramu, filtrovanie obr\u00e1zkov a transform\u00e1cia farebn\u00e9ho priestoru HSV.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Boli zozbieran\u00e9 vzorky zdrav\u00fdch listov cukrovej repy a chor\u00fdch vzoriek, ako napr\u00edklad listy kukurice so sivou \u0161kvrnitos\u0165ou listov. Extrakcia znakov choroby zah\u0155\u0148ala oddelenie listu od pozadia, zmenu ve\u013ekosti, transform\u00e1ciu a zl\u00fa\u010denie obr\u00e1zkov s cie\u013eom vytvori\u0165 realistick\u00e9 vzorky na anal\u00fdzu.<\/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\/sk\/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=\"Anota\u010dn\u00fd proces pre segment\u00e1ciu\" 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\">Anota\u010dn\u00fd proces pre segment\u00e1ciu<\/figcaption><\/figure>\n<\/div>\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Akt\u00edvna u\u010debn\u00e1 slu\u010dka<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Bola iniciovan\u00e1 akt\u00edvna u\u010debn\u00e1 slu\u010dka s neozna\u010den\u00fdmi \u00fadajmi, ktor\u00e9 boli pou\u017eit\u00e9 na tr\u00e9novanie detek\u010dn\u00fdch modelov. Tieto modely generovali anota\u010dn\u00e9 dotazy, ktor\u00e9 boli rie\u0161en\u00e9 \u013eudsk\u00fdmi anot\u00e1tormi, \u010d\u00edm sa neust\u00e1le zdokona\u013eovala presnos\u0165 modelu prostredn\u00edctvom iterat\u00edvnych tr\u00e9novac\u00edch a anota\u010dn\u00fdch cyklov.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Anot\u00e1cia \u00fadajov prostredn\u00edctvom multimod\u00e1lneho z\u00e1kladov\u00e9ho modelu<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Projekt rie\u0161il probl\u00e9m s obmedzen\u00fdmi ozna\u010den\u00fdmi \u00fadajmi a vyu\u017eil robustn\u00e9 z\u00e1kladn\u00e9 modely na generovanie anot\u00e1ci\u00ed o skuto\u010dn\u00fdch \u00fadajoch. K\u013e\u00fa\u010dov\u00fa \u00falohu zohral najm\u00e4 CLIP, model zalo\u017een\u00fd na transform\u00e1tore vyvinut\u00fd spolo\u010dnos\u0165ou OpenAI, tr\u00e9novan\u00fd na rozsiahlom s\u00fabore \u00fadajov s viac ako 400 mili\u00f3nmi p\u00e1rov obr\u00e1zkov a textu.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">V\u010faka vyu\u017eitiu Vision Transformers ako svojej chrbtice dosiahol syst\u00e9m CLIP pozoruhodn\u00fa presnos\u0165 95% na valida\u010dn\u00fdch s\u00faboroch a s vysokou presnos\u0165ou odborne kategorizoval obr\u00e1zky do odli\u0161n\u00fdch tried, ako je cukrov\u00e1 repa a burina.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Technol\u00f3gia dronov na zber \u00fadajov<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Jednou z k\u013e\u00fa\u010dov\u00fdch technol\u00f3gi\u00ed pou\u017eit\u00fdch v projekte bolo pou\u017eitie dronov vybaven\u00fdch RGB kamerami, ktor\u00e9 zachyt\u00e1vali 4K video. Tieto drony poskytovali detailn\u00e9 sn\u00edmky (rozl\u00ed\u0161enie 3840 \u00d7 2160) na anal\u00fdzu.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Predspracovanie t\u00fdchto obr\u00e1zkov v\u00fdrazne zv\u00fd\u0161ilo presnos\u0165 modelu, pri\u010dom v\u00fdrazn\u00e9 zlep\u0161enia boli pozorovan\u00e9 v modeloch ako VGGNet (+38,52%), ResNet50 (+21,14%), DenseNet121 (+7,53%) a MobileNet (+6,6%).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Na zv\u00fd\u0161enie kontrastu obrazu sa pou\u017eili techniky ako vyrovn\u00e1vanie histogramu, zatia\u013e \u010do transform\u00e1cia do farebn\u00e9ho priestoru HSV pomohla zd\u00f4razni\u0165 oblasti rastl\u00edn a zv\u00fdrazni\u0165 relevantn\u00e9 prvky.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Generovanie syntetick\u00fdch \u00fadajov<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Na rie\u0161enie probl\u00e9mu s obmedzen\u00fdmi obrazov\u00fdmi \u00fadajmi boli pomocou strojov\u00e9ho u\u010denia a umelej inteligencie vygenerovan\u00e9 syntetick\u00e9 s\u00fabory \u00fadajov. Zber \u00fadajov sa vykon\u00e1val pomocou dronov lietaj\u00facich vo v\u00fd\u0161kach od 1 m do 4 m a r\u00fdchlostiach 2 m\/s alebo viac, s vyu\u017eit\u00edm RGB kamier.<\/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\/sk\/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=\"Emula\u010dn\u00e9 prostredie\" 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\">Emula\u010dn\u00e9 prostredie<\/figcaption><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\">Na zber \u00fadajov sa pou\u017eili aj in\u00e9 vozidl\u00e1, ako napr\u00edklad traktory. Toto syntetick\u00e9 generovanie \u00fadajov sa uk\u00e1zalo ako obzvl\u00e1\u0161\u0165 u\u017eito\u010dn\u00e9 pri detekcii chor\u00f4b cukrovej repy.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-conclusion\">Z\u00e1ver<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Projekt \u201c5G siete ako n\u00e1stroj na u\u010denie sa v re\u00e1lnom \u010dase v udr\u017eate\u013enom po\u013enohospod\u00e1rstve\u201d \u00faspe\u0161ne demon\u0161troval, ako m\u00f4\u017ee technol\u00f3gia 5G zlep\u0161i\u0165 ekologick\u00e9, ekonomick\u00e9 a udr\u017eate\u013en\u00e9 aspekty pestovania cukrovej repy. V\u010faka spolupr\u00e1ci so spolo\u010dnos\u0165ami HSHL a Pfeifer &amp; Langen projekt integroval zber \u00fadajov v re\u00e1lnom \u010dase a anal\u00fdzu riaden\u00fa umelou inteligenciou, \u010d\u00edm sa zlep\u0161ila efektivita a zn\u00ed\u017eili sa zbyto\u010dn\u00e9 n\u00e1v\u0161tevy ter\u00e9nu. <\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u0160pecializovan\u00e1 kampusov\u00e1 sie\u0165 5G umo\u017enila presn\u00fa aplik\u00e1ciu hnoj\u00edv a pr\u00edpravkov na ochranu plod\u00edn. Spolo\u010dnos\u0165 Geopard Agriculture zohrala k\u013e\u00fa\u010dov\u00fa \u00falohu pri v\u00fdvoji scen\u00e1rov detekcie a monitorovania rastl\u00edn a pri vytvoren\u00ed prototypu syst\u00e9mu strojov\u00e9ho u\u010denia pre po\u013enohospod\u00e1rske prostredie 5G. \u00daspech projektu pod\u010diarkol d\u00f4le\u017eitos\u0165 pokro\u010dil\u00fdch technol\u00f3gi\u00ed v udr\u017eate\u013enom po\u013enohospod\u00e1rstve a zd\u00f4raznil potenci\u00e1l 5G pre podporu inov\u00e1ci\u00ed a efekt\u00edvnosti. <\/p>","protected":false},"excerpt":{"rendered":"<p>S nad\u0161en\u00edm oznamujeme \u00faspe\u0161n\u00e9 dokon\u010denie projektu \u201c5G siete ako n\u00e1stroj na u\u010denie sa v re\u00e1lnom \u010dase v udr\u017eate\u013enom po\u013enohospod\u00e1rstve\u201d, ktor\u00fd bol \u010diasto\u010dne podporen\u00fd\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":"<!-- This site is optimized with the Yoast SEO plugin v28.5 - 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