{"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-je-omogocil-ucenje-v-realnem-casu-v-trajnostnem-kmetijstvu-studija-o-pridelavi-sladkorne-pese","status":"publish","type":"post","link":"https:\/\/geopard.tech\/sl\/blog\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\/","title":{"rendered":"U\u010denje v realnem \u010dasu, podprto s tehnologijo 5G, v trajnostnem kmetijstvu: \u0160tudija o sladkorni pesi"},"content":{"rendered":"<p class=\"wp-block-paragraph\">Z veseljem sporo\u010damo uspe\u0161en zaklju\u010dek projekta \u201cOmre\u017eja 5G kot orodje za u\u010denje v realnem \u010dasu v trajnostnem kmetijstvu\u201d, ki ga delno financira Ministrstvo za gospodarstvo, industrijo, podnebne ukrepe in energijo zvezne de\u017eele Severno Porenje-Vestfalija.<\/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\">Ta pobuda predstavlja pomemben korak naprej pri raziskovanju transformativnega potenciala tehnologije 5G v kmetijstvu, zlasti s ciljem izbolj\u0161anja ekolo\u0161kih, ekonomskih in trajnostnih vidikov gojenja sladkorne pese.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Izkoristil je nizko zakasnitev 5G za integracijo naprednih informacijskih sistemov v realnem \u010dasu, kar je omogo\u010dilo takoj\u0161en odziv na podatke senzorjev in polo\u017eaja v vnaprej dolo\u010denih \u010dasovnih okvirih.<\/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\/sl\/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-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=\"Slika z zaklju\u010dnega dogodka predstavitve projekta na visoki \u0161oli 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\">Slika z zaklju\u010dnega dogodka predstavitve projekta na visoki \u0161oli Hamm-Lippstadt (HSHL)<\/figcaption><\/figure>\n<\/div>\n\n\n<h2 class=\"wp-block-heading\" id=\"h-project-focus-and-partnership\">Osredoto\u010denost projekta in partnerstvo<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">V sodelovanju s partnerji pri HSHL in s podporo podjetja Pfeifer &amp; Langen se je projekt osredoto\u010dil na preu\u010devanje celotnega \u017eivljenjskega cikla gojenja sladkorne pese na poljih, ki pripadajo partnerjem. Njegov cilj je bil prikazati, kako bi lahko 5G slu\u017eil kot klju\u010dni tehnolo\u0161ki katalizator v kmetijskem sektorju Severnega Porenja-Vestfalije, in predstavil njegov potencial kot gonilne sile za inovacije in u\u010dinkovitost.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-role-of-geopard-agriculture\">Vloga GeoPard Agriculture<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">GeoPard Agriculture je imel klju\u010dno vlogo pri opredelitvi in izvajanju klju\u010dnih vidikov projekta, vklju\u010dno s scenariji za zaznavanje, spremljanje in napovedovanje proizvodnje rastlin. Razvili smo prototip sistema umetne inteligence, prilagojenega za kmetijsko okolje 5G, izvedli modele znotraj obla\u010dne infrastrukture in ustvarili mobilno aplikacijo za interakcijo v realnem \u010dasu z modeli v oblaku.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-technological-integration\">Tehnolo\u0161ka integracija<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Metode umetne inteligence (UI) so bile uvedene prek robustne infrastrukture v oblaku z visokimi ra\u010dunalni\u0161kimi zmogljivostmi. Algoritmi UI so rastline med vsakim kri\u017eanjem v realnem \u010dasu kategorizirali in spremljali njihovo rast skozi celoten \u017eivljenjski cikel, s \u010dimer so odpravili potrebo po nepotrebnih obiskih na terenu zgolj za zbiranje podatkov.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Ta napredek je omogo\u010dil natan\u010dno uporabo gnojil in sredstev za za\u0161\u010dito pridelkov, dinami\u010dno prilagajanje odmerkov med kri\u017eanjem s pomo\u010djo algoritmov strojnega u\u010denja.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-deployment-of-unmanned-vehicles\">Uporaba brezpilotnih vozil<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Poleg tega je projekt izkoristil zmanj\u0161ano zakasnitev 5G za uporabo brezpilotnih vozil za spremljanje rastlin in zbiranje podatkov. Ta vozila so igrala klju\u010dno vlogo pri zbiranju vpogledov v realnem \u010dasu in nadaljnji optimizaciji kmetijskih praks.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-project-outcomes-enhancing-sugar-beet-production-with-5g-technology\">Rezultati projekta: Izbolj\u0161anje pridelave sladkorne pese s tehnologijo 5G<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Projekt je z analizo celotnega \u017eivljenjskega cikla gojenja sladkorne pese pokazal, kako bi lahko tehnologija 5G slu\u017eila kot transformativni dejavnik v kmetijskem sektorju Severnega Porenja-Vestfalije, pri \u010demer je poudaril znatne izbolj\u0161ave, ki jih omogo\u010da tehnologija 5G. Vendar pa so raziskovalci za u\u010dinkovito predstavitev rezultatov projekta uporabili delovne pakete, ki vsebujejo razli\u010dne scenarije in infrastrukture.<\/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\/sl\/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-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=\"Poskusno polje sladkorne pese \" 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\">Poskusno polje sladkorne pese<\/figcaption><\/figure>\n<\/div>\n\n\n<h3 class=\"wp-block-heading\" id=\"h-scenario-definition-considering-existing-geodata-and-ml-infrastructure\">Opredelitev scenarija ob upo\u0161tevanju obstoje\u010dih geopodatkov in infrastrukture strojnega u\u010denja<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Projekt je pokazal, kako bi lahko tradicionalne procese v \u017eivljenjskem ciklu pridelave sladkorne pese izbolj\u0161ali z integracijo tehnologije 5G. Klju\u010dni cilji so bili:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Razvili smo scenarije, ki so pripravljeni za izvedbo, za prepoznavanje, spremljanje in napovedovanje proizvodnje rastlin.<\/li>\n\n\n\n<li>Dolo\u010dene tehni\u010dne zahteve, potrebne za uspe\u0161no uvedbo teh scenarijev.<\/li>\n\n\n\n<li>Opredelili in ocenili ustrezne ekolo\u0161ke in ekonomske kazalnike za oceno dodane vrednosti, ki jo prina\u0161a omre\u017eje 5G.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Ta faza je poudarila zavezanost projekta k integraciji najsodobnej\u0161e tehnologije z obstoje\u010dimi kmetijskimi praksami. Ta arhitektura je izkoristila visokohitrostno povezljivost omre\u017eja 5G za la\u017eje zbiranje in obdelavo podatkov v realnem \u010dasu med robnimi napravami in oblakom. Obla\u010dna infrastruktura je zagotavljala bistvene vire za usposabljanje in uvajanje obse\u017enih modelov umetne inteligence, medtem ko je platforma umetne inteligence ponujala robustna orodja za razvoj in uvajanje modelov. Aplikacijska plast je kon\u010dnim uporabnikom predstavila uporabne vpoglede, pridobljene iz modelov umetne inteligence, in tako izbolj\u0161ala zmo\u017enosti odlo\u010danja.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-machine-learning-and-ai-in-the-context-of-5g\">Strojno u\u010denje in umetna inteligenca v kontekstu 5G<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Osrednji cilj tega dela je bil prilagoditi obstoje\u010de sisteme strojnega u\u010denja in umetne inteligence, da bi se uskladili z zgoraj opisanimi scenariji, in jih ustrezno optimizirati. Klju\u010dni cilji so bili:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Dolo\u010dite cilje sistema in razvijte njegovo arhitekturo<\/li>\n\n\n\n<li>Zbrani podatki o stvarnosti za u\u010denje in potrjevanje modelov umetne inteligence.<\/li>\n\n\n\n<li>Vzpostavljena in ozna\u010dena ustrezna baza podatkov, prilagojena za identifikacijo in spremljanje rastlin.<\/li>\n\n\n\n<li>Modeli umetne inteligence so bili brezhibno integrirani v omre\u017eno infrastrukturo 5G.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">V tej fazi so klju\u010dno vlogo igrale robne naprave, opremljene s SIM-karticami mobilnih telefonov, ki uporabljajo tehnologijo 5G. Pozorno so spremljali klju\u010dne kazalnike uspe\u0161nosti (KPI), kot sta latenca ali latenca od konca do konca (E2E). Meritve so vklju\u010devale oceno zanesljivosti in razpolo\u017eljivosti natan\u010dno prejetih podatkovnih paketov ter analizo hitrosti prenosa uporabnikov in najvi\u0161jih hitrosti prenosa.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Poleg tega so bile predpostavke postavljene na podlagi pretakanja videa v lo\u010dljivosti UHD v formatu MP4, ki se prena\u0161a prek protokola za nadzor prenosa (TCP). Med mo\u017enimi re\u0161itvami so bile optimizacija z eno samo sliko namesto z neprekinjenimi video tokovi, izvajanje osnovnih optimizacij neposredno na robnih napravah in implementacija tehnik kvantizacije modelov za pove\u010danje u\u010dinkovitosti.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-cloud-infrastructure-and-aws-services\">Obla\u010dna infrastruktura in storitve AWS<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Projekt se je mo\u010dno zana\u0161al na obla\u010dno infrastrukturo, ki je izkori\u0161\u010dala storitve AWS, kot so Lambda, SageMaker, S3, CloudWatch in RDS, ki so imele klju\u010dno vlogo pri zagotavljanju potrebnih virov za usposabljanje in uvajanje modelov umetne inteligence.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Za u\u010dinkovito upravljanje instanc in stre\u017eenje aplikacij je bil uporabljen AWS Lambda, medtem ko je AWS SageMaker olaj\u0161al gradnjo robustnih cevovodov strojnega u\u010denja. Re\u0161itve za shranjevanje, kot so S3, CloudWatch in RDS, so bile bistvene za shranjevanje naborov podatkov in dnevnikov, ki so klju\u010dni za delovanje modelov strojnega u\u010denja in nevronskih mre\u017e.<\/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\/sl\/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-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=\"Obla\u010dna infrastruktura 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\">Obla\u010dna infrastruktura AWS<\/figcaption><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\">Zato je ta infrastruktura podpirala zmogljivosti obdelave podatkov v realnem \u010dasu, ki jih omogo\u010da omre\u017eje 5G.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-5g-network-latency\">Zakasnitev omre\u017eja 5G<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Omre\u017eja 5G so bila zasnovana za doseganje izjemno nizke latence, ki se obi\u010dajno giblje od 1 do 10 milisekund. Ta latenca je odra\u017eala \u010das, potreben za potovanje podatkov med mobilnimi napravami in stre\u017eniki AWS prek omre\u017eja 5G. Na latenco so vplivale tudi zmogljivosti obdelave, specifi\u010dne za napravo, kot je hitrost zajemanja in obdelave fotografij na pametnih telefonih z visokozmogljivimi procesorji.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Hitrosti nalaganja podatkov v omre\u017eju 5G in velikost fotografije sta vplivali na \u010das prenosa podatkov v AWS. AWS je dodatno prispeval k zakasnitvi pri \u010dasih obdelave za naloge, kot sta zaznavanje in segmentacija na podlagi nevronskih mre\u017e, ki so se razlikovali glede na kompleksnost algoritma in u\u010dinkovitost storitve AWS. Po obdelavi so bili rezultati preneseni nazaj v mobilne naprave, na kar sta vplivala hitrost prenosa 5G in velikost rezultatov.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-plant-recognition-using-ai\">Prepoznavanje rastlin z uporabo umetne inteligence<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Na podro\u010dju prepoznavanja rastlin so procesi, ki jih poganja umetna inteligenca, vklju\u010devali ustvarjanje obse\u017ene baze podatkov slik rastlin za algoritme u\u010denja, ki temeljijo na nevronskih mre\u017eah. Ti algoritmi so bili usposobljeni za razlikovanje vrst sladkorne pese od drugih rastlin s prepoznavanjem zna\u010dilnosti, zna\u010dilnih za to dolo\u010deno vrsto rastline, kot so oblike listov, barve cvetov itd.<\/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\/sl\/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-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=\"Fenolo\u0161ki razvoj rastlin sladkorne pese\" 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\">Fenolo\u0161ki razvoj rastlin sladkorne pese. Vir: <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\">Tukaj s prepoznavanjem rastlin mislimo na nalogo odkrivanja plevela in segmentacije rastlin sladkorne pese.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Zaznavanje plevela<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Za zaznavanje plevela je projekt uporabil MobileNet-v3, ki je bil usposobljen z obse\u017enimi dopolnjevanji podatkov in ute\u017eenim vzor\u010denjem. Ta model je dosegel impresivno natan\u010dnost 0,984 in AUC 0,998.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Segmentacija sladkorne pese<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Za naloge segmentacije so bili uporabljeni modeli, kot so YOLACT, ResNeSt, SOLO in U-net, za natan\u010dno razmejitev posameznih vzorcev sladkorne pese znotraj slik. Nato je bil na podlagi razli\u010dnih meril: hitrosti, \u010dasa sklepanja itd. izbran naju\u010dinkovitej\u0161i model. Podatki za segmentacijo so bili pridobljeni iz slik RGB, posnetih z dronom, ki so bile spremenjene v velikost in ozna\u010dene za namene u\u010denja in validacije.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Naloge segmentacije so vklju\u010devale ustvarjanje mask, ki so natan\u010dno dolo\u010dale meje rastlin. Ta metoda je zmanj\u0161ala \u010dlove\u0161ki napor pri ozna\u010devanju, hkrati pa optimizirala u\u010dinkovitost. Z dajanjem prednosti ozna\u010devanju zahtevnih vzorcev se je delovanje modela znatno izbolj\u0161alo. Strategije iterativnega preu\u010denja in vzor\u010denja negotovosti so se izkazale za u\u010dinkovite, saj so dosegle stopnje natan\u010dnosti segmentacije, ki so presegle 98% v razli\u010dnih fazah rasti.<\/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\/sl\/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-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=\"Primer vhodno-izhodne segmentacije\" 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\">Primer vhodno-izhodne segmentacije<\/figcaption><\/figure>\n<\/div>\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Vrednotenje modela<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Model je bil usposobljen z uporabo strogih dopolnil podatkov. Model je bil ovrednoten z uporabo razli\u010dnih metrik, vklju\u010dno s prese\u010di\u0161\u010dem nad unijo (IoU). Analiza sklepanja za zgrajeni model, izvedena na podmno\u017eici iz nabora podatkov \u2018sadnice rastlin v2\u2019, je pokazala natan\u010dnost 81%. \u010cas sklepanja je po 7-sekundnem inicializacijskem obdobju, ki je potrebno le enkrat na sejo, trajal pribli\u017eno 320 milisekund.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Pri spremljanju rastlin, ki ga poganja umetna inteligenca (UI), so kamere in senzorji zajemali klju\u010dne podatke o rastlinah, ki so jih analizirali strojno u\u010denje in algoritmi UI. Ta analiza je imela klju\u010dno vlogo pri ocenjevanju zdravja rastlin, odkrivanju stresa, bolezni ali drugih dejavnikov, ki vplivajo na rast.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Uporaba se je raz\u0161irila od optimizacije kmetijske produktivnosti do spremljanja naravnih ekosistemov, kot so gozdovi, pomo\u010di pri prizadevanjih za ohranjanje narave in izbolj\u0161anja razumevanja vplivov na okolje.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-object-detection-in-plant-monitoring\">Zaznavanje objektov pri spremljanju obratov<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Naslednja faza po segmentaciji rastlin sladkorne pese je zaznavanje objektov, katerega cilj je razumevanje posebnosti vsake rastline glede zdravja, rasti in drugih dejavnikov. Za zaznavanje objektov pri spremljanju rastlin so bili uporabljeni napredni modeli, kot so YOLOv4, MobileNetV2 in VGG-19 z mehanizmi pozornosti. Ti modeli so analizirali segmentirane slike sladkorne pese, da bi zaznali specifi\u010dna obmo\u010dja stresa in bolezni, kar je omogo\u010dilo natan\u010dne in ciljno usmerjene intervencije.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Projekt je dosegel pomembne mejnike pri odkrivanju bolezni, saj je u\u010dil modele ResNet-18 in ResNet-34, predhodno usposobljene na ImageNetu. Ti modeli so pokazali impresivno natan\u010dnost 0,88 pri prepoznavanju bolezni, ki prizadenejo rastline sladkorne pese, s povr\u0161ino pod ROC krivuljo (AUC) 0,898. Modeli so pokazali visoko napovedno zanesljivost in natan\u010dno razlikovali med obolelimi in zdravimi 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\/sl\/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-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=\"Primer vhodno-izhodnega sistema zaznavanja objektov\" 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\">Primer vhodno-izhodnega sistema zaznavanja objektov<\/figcaption><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\">Projekt je uporabil sistemati\u010den pristop k odkrivanju bolezni, pri \u010demer je slike segmentiral v standardizirane zaplate. Te zaplate so bile natan\u010dno ozna\u010dene z interaktivnimi orodji za natan\u010dno dolo\u010ditev obmo\u010dij, ki jih je prizadela bolezen. Zaznavanje objektov je dodatno pove\u010dalo natan\u010dnost z ozna\u010devanjem omejevalnih okvirjev okoli rastlin, kar je omogo\u010dilo natan\u010dno spremljanje zdravja rastlin.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-plant-production-prediction\">Napoved rastlinske proizvodnje<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Na podro\u010dju napovedovanja rastlinske pridelave so modeli umetne inteligence za napovedovanje pridelka uporabili okoljske podatke, kot so vremenske razmere in parametri tal. Uporabljeni so bili regresijski modeli, kot so izolacijski gozd, linearna regresija in grebenska regresija.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Ti modeli so integrirali numeri\u010dne zna\u010dilnosti, pridobljene iz obmo\u010dij omejevalnega okvira, skupaj s podatki o tleh za optimizacijo uporabe gnojil.<\/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\/sl\/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-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=\"Sladkorna pesa na testnem polju\" 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\">Sladkorna pesa na testnem polju<\/figcaption><\/figure>\n<\/div>\n\n\n<h2 class=\"wp-block-heading\" id=\"h-model-deployment-considerations\">Premisleki o uvajanju modela<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Strategije uvajanja razvitih modelov so bile ovrednotene tako za robne naprave kot za obla\u010dne platforme. Uvajanje modelov na robne naprave je ponudilo prednosti, kot so ni\u017eji stro\u0161ki in manj\u0161a latenca.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Vendar pa bi ta pristop lahko zaradi omejitev strojne opreme zmanj\u0161al potencialno natan\u010dnost. Po drugi strani pa je uvedba v oblaku ponujala hitrej\u0161e \u010dase sklepanja z uporabo visokozmogljivih grafi\u010dnih procesorjev, vendar bi lahko povzro\u010dila dodatne stro\u0161ke in je bila odvisna od internetne povezave, kar bi lahko povzro\u010dilo komunikacijsko zakasnitev.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-comparative-analysis-with-5g-network\">Primerjalna analiza z omre\u017ejem 5G<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Primerjalna analiza je pokazala, da je uporaba omre\u017eja 5G znatno izbolj\u0161ala segmentacijo sladkorne pese v primerjavi s tradicionalnimi nastavitvami 4G\/WiFi. To izbolj\u0161anje se je pokazalo v kraj\u0161em povpre\u010dnem \u010dasu nastavitve in omre\u017eja, kar poudarja pove\u010danje u\u010dinkovitosti, dose\u017eeno s tehnologijo 5G.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Postopek priprave podatkov<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Postopek priprave podatkov je vklju\u010deval zbiranje podatkovnih nizov zdravih in obolelih rastlin, odkrivanje plevela, prepoznavanje rastnih stopenj in ekstrakcijo slik iz 4K RAW videoposnetka. Za pripravo podatkov za analizo so bile uporabljene tehnike, kot so izena\u010devanje histograma, filtriranje slik in transformacija barvnega prostora HSV.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Zbrani so bili vzorci zdravih listov sladkorne pese in obolelih vzorcev, kot so koruzni listi s sivo listno pegavostjo. Ekstrakcija zna\u010dilnosti bolezni je vklju\u010devala lo\u010devanje listov od ozadja, spreminjanje velikosti, preoblikovanje in zdru\u017eevanje slik, da bi ustvarili realisti\u010dne vzorce za analizo.<\/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\/sl\/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-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=\"Postopek ozna\u010devanja za segmentacijo\" 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\">Postopek ozna\u010devanja za segmentacijo<\/figcaption><\/figure>\n<\/div>\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Aktivna u\u010dna zanka<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Aktivna u\u010dna zanka je bila spro\u017eena z neozna\u010denimi podatki, ki so bili uporabljeni za u\u010denje modelov zaznavanja. Ti modeli so ustvarili poizvedbe za opombe, ki so jih obravnavali \u010dlove\u0161ki opomniki, s \u010dimer so nenehno izbolj\u0161evali natan\u010dnost modela z iterativnimi cikli u\u010denja in opombe.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Anotacija podatkov prek multimodalnega temeljnega modela<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Projekt je pri re\u0161evanju izziva omejenih ozna\u010denih podatkov izkoristil robustne temeljne modele za ustvarjanje anotacij o realnosti na terenu. Klju\u010dno vlogo je imel CLIP, model, ki temelji na transformatorju in ga je razvil OpenAI ter je bil usposobljen na obse\u017enem naboru podatkov z ve\u010d kot 400 milijoni parov slike in besedila.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Z uporabo Vision Transformers kot ogrodje je CLIP dosegel izjemno natan\u010dnost 95% na validacijskih sklopih, pri \u010demer je slike z visoko natan\u010dnostjo u\u010dinkovito razvrstil v razli\u010dne razrede, kot sta sladkorna pesa in plevel.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Tehnologija dronov za zbiranje podatkov<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Ena od klju\u010dnih tehnologij, uporabljenih v projektu, je bila uporaba dronov, opremljenih z RGB kamerami, ki so snemale 4K video. Ti droni so zagotavljali podrobne slike (lo\u010dljivost 3840 \u00d7 2160) za analizo.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Predhodna obdelava teh slik je znatno izbolj\u0161ala natan\u010dnost modela, pri \u010demer so bile opa\u017eene opazne izbolj\u0161ave pri modelih, kot so VGGNet (+38,52%), ResNet50 (+21,14%), DenseNet121 (+7,53%) in MobileNet (+6,6%).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Za izbolj\u0161anje kontrasta slike so bile uporabljene tehnike, kot je izena\u010devanje histograma, medtem ko je transformacija v barvni prostor HSV pomagala poudariti rastlinska obmo\u010dja in izpostaviti pomembne zna\u010dilnosti.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Generiranje sinteti\u010dnih podatkov<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Za re\u0161evanje izziva omejenih slikovnih podatkov so bili s pomo\u010djo strojnega u\u010denja in umetne inteligence ustvarjeni sinteti\u010dni nabori podatkov. Zbiranje podatkov je bilo izvedeno z droni, ki so leteli na vi\u0161inah med 1 m in 4 m ter s hitrostjo 2 m\/s ali ve\u010d, pri \u010demer so bile uporabljene RGB kamere.<\/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\/sl\/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-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=\"Emulacijsko okolje\" 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\">Emulacijsko okolje<\/figcaption><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\">Za zbiranje podatkov so bila uporabljena tudi druga vozila, kot so traktorji. Ta sinteti\u010dna pridobitev podatkov se je izkazala za \u0161e posebej koristno za odkrivanje bolezni sladkorne pese.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-conclusion\">Zaklju\u010dek<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Projekt \u201cOmre\u017eja 5G kot omogo\u010devalec u\u010denja v realnem \u010dasu v trajnostnem kmetijstvu\u201d je uspe\u0161no pokazal, kako lahko tehnologija 5G izbolj\u0161a ekolo\u0161ke, ekonomske in trajnostne vidike gojenja sladkorne pese. S sodelovanjem s HSHL in Pfeifer &amp; Langen je projekt integriral zbiranje podatkov v realnem \u010dasu in analizo, ki jo poganja umetna inteligenca, s \u010dimer je izbolj\u0161al u\u010dinkovitost in zmanj\u0161al nepotrebne obiske na terenu. <\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Namensko kampusno omre\u017eje 5G je omogo\u010dilo natan\u010dno uporabo gnojil in sredstev za za\u0161\u010dito pridelkov. Geopard Agriculture je imel klju\u010dno vlogo pri razvoju scenarijev za zaznavanje in spremljanje rastlin ter pri ustvarjanju prototipa sistema strojnega u\u010denja za kmetijsko okolje 5G. Uspeh projekta je poudaril pomen naprednih tehnologij v trajnostnem kmetijstvu in poudaril potencial 5G za spodbujanje inovacij in u\u010dinkovitosti. <\/p>","protected":false},"excerpt":{"rendered":"<p>Z veseljem sporo\u010damo uspe\u0161en zaklju\u010dek projekta \u201cOmre\u017eja 5G kot omogo\u010devalec u\u010denja v realnem \u010dasu v trajnostnem kmetijstvu\u201d, ki ga delno podpirajo\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_hide_featured_image":false,"_jetpack_featured_image_duplicate_dismissed":0,"_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 v28.8 - 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