{"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-omogucio-ucenje-u-stvarnom-vremenu-u-odrzivoj-poljoprivredi-studija-o-proizvodnji-secerne-repe","status":"publish","type":"post","link":"https:\/\/geopard.tech\/hr\/blog\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\/","title":{"rendered":"5G omogu\u0107eno u\u010denje u stvarnom vremenu u odr\u017eivoj poljoprivredi: Studija o \u0161e\u0107ernoj repi"},"content":{"rendered":"<p class=\"wp-block-paragraph\">S uzbu\u0111enjem objavljujemo uspje\u0161an zavr\u0161etak projekta \u201c5G mre\u017ee kao pokreta\u010d u\u010denja u stvarnom vremenu u odr\u017eivoj poljoprivredi\u201d, koji je podr\u017ean djelomi\u010dnim financiranjem od strane Ministarstva gospodarstva, industrije, klimatske akcije i energetike savezne dr\u017eave Sjeverna Rajna-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\">Ova inicijativa predstavlja zna\u010dajan korak naprijed u istra\u017eivanju transformacijskog potencijala 5G tehnologije u poljoprivredi, posebno usmjeren na unaprje\u0111enje ekolo\u0161kih, ekonomskih i odr\u017eivih aspekata uzgoja \u0161e\u0107erne repe.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Iskoristio je nisku latenciju 5G za integraciju naprednih informacijsko-tehnolo\u0161kih sustava u stvarnom vremenu, omogu\u0107uju\u0107i trenutne odgovore na podatke senzora i pozicije u definiranim vremenskim okvirima.<\/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\/hr\/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=\"Slika s zavr\u0161nog doga\u0111aja prezentacije projekta 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\">Slika s zavr\u0161nog doga\u0111aja prezentacije projekta na Hochschule Hamm-Lippstadt (HSHL)<\/figcaption><\/figure>\n<\/div>\n\n\n<h2 class=\"wp-block-heading\" id=\"h-project-focus-and-partnership\">Fokus projekta i partnerstvo<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">U suradnji s partnerima s HSHL-a i uz potporu Pfeifer &amp; Langen, projekt se usredoto\u010dio na prou\u010davanje cjelokupnog \u017eivotnog ciklusa uzgoja \u0161e\u0107erne repe na poljima koja pripadaju partnerima. Cilj mu je bio pokazati kako 5G mo\u017ee poslu\u017eiti kao klju\u010dni tehnolo\u0161ki katalizator unutar poljoprivrednog sektora Sjeverne Rajne-Vestfalije, prikazuju\u0107i njegov potencijal kao pokreta\u010da inovacija i u\u010dinkovitosti.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-role-of-geopard-agriculture\">Uloga GeoPard Agriculture<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">GeoPard Agriculture odigrao je klju\u010dnu ulogu u definiranju i implementaciji klju\u010dnih aspekata projekta, uklju\u010duju\u0107i scenarije za detekciju biljaka, pra\u0107enje i predvi\u0111anje proizvodnje. Razvili smo prototip sustava umjetne inteligencije prilago\u0111en poljoprivrednom okru\u017eenju 5G, izvr\u0161ili smo modele unutar cloud infrastrukture i izradili mobilnu aplikaciju za interakciju u stvarnom vremenu s modelima temeljenim na 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 umjetne inteligencije (AI) primijenjene su putem robusne ra\u010dunalne infrastrukture s velikim ra\u010dunalnim mogu\u0107nostima. AI algoritmi kategorizirali su biljke u stvarnom vremenu tijekom svakog unakrsnog spajanja i pratili njihov rast tijekom cijelog \u017eivotnog ciklusa, eliminiraju\u0107i potrebu za nepotrebnim posjetima terenu isklju\u010divo u svrhu prikupljanja podataka.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Ovo unapre\u0111enje omogu\u0107ilo je precizno nano\u0161enje gnojiva i za\u0161titnih sredstava za usjeve, dinami\u010dki prilago\u0111avaju\u0107i stope primjene tijekom prolaza pomo\u0107u algoritama strojnog u\u010denja.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-deployment-of-unmanned-vehicles\">Postavljanje bespilotnih vozila<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Nadalje, projekt je iskoristio smanjenu latenciju 5G mre\u017ee za implementaciju bespilotnih vozila za nadzor biljaka i prikupljanje podataka. Ta vozila odigrala su klju\u010dnu ulogu u prikupljanju uvida u stvarnom vremenu i daljnjoj optimizaciji poljoprivrednih praksi.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-project-outcomes-enhancing-sugar-beet-production-with-5g-technology\">Rezultati projekta: Unaprje\u0111enje proizvodnje \u0161e\u0107erne repe tehnologijom 5G<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Projekt je demonstrirao kako tehnologija 5G mo\u017ee poslu\u017eiti kao transformativni pokreta\u010d u poljoprivrednom sektoru Sjeverne Rajne-Vestfalije analiziraju\u0107i cjelokupni \u017eivotni ciklus uzgoja \u0161e\u0107erne repe, isti\u010du\u0107i zna\u010dajna pobolj\u0161anja koja omogu\u0107uje tehnologija 5G. Me\u0111utim, kako bi se u\u010dinkovito prikazali rezultati projekta, istra\u017eiva\u010di su koristili radne pakete koji sadr\u017ee razli\u010dite scenarije i 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\/hr\/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=\"Pokusno polje \u0161e\u0107erne repe \" 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\">Pokusno polje \u0161e\u0107erne repe<\/figcaption><\/figure>\n<\/div>\n\n\n<h3 class=\"wp-block-heading\" id=\"h-scenario-definition-considering-existing-geodata-and-ml-infrastructure\">Definicija scenarija uzimaju\u0107i u obzir postoje\u0107e geopodatke i ML infrastrukturu<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Projekt je demonstrirao kako se tradicionalni procesi unutar \u017eivotnog ciklusa proizvodnje \u0161e\u0107erne repe mogu pobolj\u0161ati integracijom 5G tehnologije. Klju\u010dni ciljevi uklju\u010divali su:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Razvijeni scenariji spremni za implementaciju za prepoznavanje biljaka, nadzor i predvi\u0111anje proizvodnje.<\/li>\n\n\n\n<li>Utvr\u0111eni tehni\u010dki zahtjevi potrebni za uspje\u0161no implementiranje ovih scenarija.<\/li>\n\n\n\n<li>Identificirani su i procijenjeni relevantni ekolo\u0161ki i ekonomski pokazatelji za vrednovanje dodane vrijednosti koju donosi 5G mre\u017ea.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Ova faza nagla\u0161ava predanost projekta integraciji najsuvremenije tehnologije s postoje\u0107im poljoprivrednim praksama. Ova arhitektura iskoristila je brzu povezanost 5G mre\u017ee za olak\u0161avanje prikupljanja i obrade podataka u stvarnom vremenu izme\u0111u rubnih ure\u0111aja i oblaka. Cloud infrastruktura pru\u017eila je osnovne resurse za obuku i implementaciju velikih AI modela, dok je AI platforma nudila robusne alate za razvoj i implementaciju modela. Sloj aplikacije prikazao je korisne uvide izvedene iz AI modela krajnjim korisnicima, pobolj\u0161avaju\u0107i sposobnost dono\u0161enja odluka.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-machine-learning-and-ai-in-the-context-of-5g\">Strojno u\u010denje i umjetna inteligencija u kontekstu 5G<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Fokus ovog dijela bio je prilagoditi postoje\u0107e sustave strojnog u\u010denja i umjetne inteligencije kako bi se uskladili s gore opisanim scenarijima, optimiziraju\u0107i ih u skladu s tim. Klju\u010dni ciljevi uklju\u010divali su:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Definirati ciljeve sustava i razviti arhitekturu sustava<\/li>\n\n\n\n<li>Prikupljeni podaci za obuku i validaciju AI modela.<\/li>\n\n\n\n<li>Uspostavljena i anotirana prikladna baza podataka prilago\u0111ena za identifikaciju i pra\u0107enje biljaka.<\/li>\n\n\n\n<li>Integrirani AI modeli neprimjetno u infrastrukturu 5G mre\u017ee.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">U ovoj fazi, rubni ure\u0111aji opremljeni SIM karticama za mobilne telefone koji koriste 5G tehnologiju odigrali su klju\u010dnu ulogu. Klju\u010dni pokazatelji uspje\u0161nosti (KPI) kao \u0161to su latencija ili krajnja (E2E) latencija pa\u017eljivo su pra\u0107eni. Mjerenja su uklju\u010divala procjenu pouzdanosti i dostupnosti podatkovnih paketa primljenih to\u010dno, zajedno s analizom korisni\u010dkih brzina prijenosa podataka i vr\u0161nih brzina prijenosa podataka.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Nadalje, pretpostavke su napravljene na temelju strujanja UHD videozapisa u MP4 formatu, prenesenog putem Transmission Control Protocol (TCP). Istra\u017eena potencijalna rje\u0161enja uklju\u010divala su optimizaciju pomo\u0107u pojedina\u010dnih slika umjesto kontinuiranih video strujanja, izvo\u0111enje osnovnih optimizacija izravno na rubnim ure\u0111ajima i implementaciju tehnika modelne kvantizacije za pobolj\u0161anje u\u010dinkovitosti.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-cloud-infrastructure-and-aws-services\">Infrastruktura u oblaku i AWS usluge<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Projekt se uvelike oslanjao na cloud infrastrukturu, koriste\u0107i AWS usluge poput Lambda, SageMaker, S3, CloudWatch i RDS, koje su imale klju\u010dnu ulogu u pru\u017eanju potrebnih resursa za treniranje i implementaciju AI modela.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">AWS Lambda je kori\u0161ten za u\u010dinkovito upravljanje instancama i poslu\u017eivanje aplikacija, dok je AWS SageMaker olak\u0161ao izgradnju robusnih cjevovoda za strojno u\u010denje. Rje\u0161enja za pohranu poput S3, CloudWatch i RDS bila su klju\u010dna za pohranu skupova podataka i dnevnika bitnih za rad modela strojnog u\u010denja i neuronskih mre\u017ea.<\/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\/hr\/blog\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\/aws-cloud-infrastructure\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/AWS-cloud-infrastructure.png?fit=800%2C490&amp;ssl=1\" data-orig-size=\"800,490\" data-comments-opened=\"1\" data-image-meta=\"{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;0&quot;}\" data-image-title=\"AWS cloud infrastructure\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/AWS-cloud-infrastructure.png?fit=800%2C490&amp;ssl=1\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/AWS-cloud-infrastructure.png?resize=800%2C490&#038;ssl=1\" alt=\"AWS oblak infrastruktura\" class=\"wp-image-10627\" srcset=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/AWS-cloud-infrastructure.png?w=800&amp;ssl=1 800w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/AWS-cloud-infrastructure.png?resize=300%2C184&amp;ssl=1 300w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/AWS-cloud-infrastructure.png?resize=768%2C470&amp;ssl=1 768w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/><figcaption class=\"wp-element-caption\">AWS oblak infrastruktura<\/figcaption><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\">Stoga je ova infrastruktura podr\u017eavala mogu\u0107nosti obrade podataka u stvarnom vremenu omogu\u0107ene 5G mre\u017eom.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-5g-network-latency\">5G Mre\u017ena Latencija<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">5G mre\u017ee su dizajnirane da postignu ultra nisku latenciju, koja se obi\u010dno kre\u0107e od 1 do 10 milisekundi. Ta latencija odra\u017eavala je vrijeme potrebno da podaci putuju izme\u0111u mobilnih ure\u0111aja i AWS poslu\u017eitelja putem 5G mre\u017ee. Sposobnosti obrade specifi\u010dne za ure\u0111aj, poput brzine snimanja i obrade fotografija na pametnim telefonima s procesorima visokih performansi, tako\u0111er su utjecale na latenciju.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Brzine prijenosa podataka na 5G mre\u017ei i veli\u010dina fotografije utjecale su na vrijeme prijenosa podataka u AWS. AWS je dodatno pridonio latenciji vremenima obrade za zadatke poput detekcije temeljene na neuralnim mre\u017eama i segmentacije, koja su varirala ovisno o slo\u017eenosti algoritma i u\u010dinkovitosti AWS usluge. Nakon obrade, rezultati su preuzeti natrag na mobilne ure\u0111aje, pod utjecajem brzine preuzimanja 5G i veli\u010dine rezultata.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-plant-recognition-using-ai\">Prepoznavanje biljaka pomo\u0107u umjetne inteligencije<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">U domeni prepoznavanja biljaka, procesi vo\u0111eni umjetnom inteligencijom uklju\u010divali su stvaranje opse\u017ene baze podataka slika biljaka za obuku algoritama temeljenih na neuralnim mre\u017eama. Ti su algoritmi trenirani da razlikuju vrste \u0161e\u0107erne repe od drugih biljaka prepoznavanjem zna\u010dajki specifi\u010dnih za tu odre\u0111enu vrstu biljke, poput oblika li\u0161\u0107a, boje cvjetova 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\/hr\/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=\"Fenolo\u0161ki razvoj biljaka \u0161e\u0107erne repe\" 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 biljaka \u0161e\u0107erne repe. Izvor: <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\">Ovdje pod prepoznavanjem biljaka mislimo na zadatak detekcije korova i segmentacije biljaka \u0161e\u0107erne repe.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Detekcija korova<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Za detekciju korova, projekt je koristio MobileNet-v3, koji je treniran s opse\u017enim augmentacijama podataka i ponderiranim uzorkovanjem. Ovaj model je postigao impresivnu to\u010dnost od 0,984 i AUC od 0,998.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Segmentacija \u0161e\u0107erne repe<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Za zadatke segmentacije, modeli poput YOLACT, ResNeSt, SOLO i U-net kori\u0161teni su za precizno razgrani\u010denje pojedina\u010dnih uzoraka \u0161e\u0107erne repe unutar slika. Zatim je odabran naju\u010dinkovitiji model na temelju razli\u010ditih kriterija: brzina, vrijeme inferencije itd. Podaci za segmentaciju dobiveni su iz RGB slika snimljenih dronom, koje su promijenjene veli\u010dine i ozna\u010dene za potrebe treniranja i validacije.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Zadaci segmentacije uklju\u010divali su izradu maski koje su to\u010dno ocrtavale granice biljaka. Ova metoda smanjila je napore za ru\u010dno ozna\u010davanje, a istovremeno optimizirala u\u010dinkovitost. Prioritiziranjem ozna\u010davanja izazovnih uzoraka, performanse modela su zna\u010dajno pobolj\u0161ane. Strategije iterativnog ponovnog treniranja i uzorkovanja neizvjesnosti pokazale su se u\u010dinkovitima, posti\u017eu\u0107i stope to\u010dnosti segmentacije ve\u0107e od 98% u razli\u010ditim fazama rasta.<\/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\/hr\/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=\"Primjer ulaz-izlaz 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\">Primjer ulaz-izlaz segmentacije<\/figcaption><\/figure>\n<\/div>\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Procjena modela<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Model je treniran uz rigorozne augmentacije podataka. Model je evaluiran kori\u0161tenjem razli\u010ditih metrika, uklju\u010duju\u0107i Intersection over Union (IoU). Analiza inferencije za izgra\u0111eni model, provedena na podskupu iz \u2018plant seedlings v2\u2019 skupa podataka, pokazala je to\u010dnost od 81%. Vrijeme inferencije trajalo je otprilike 320 milisekundi za izra\u010dun nakon 7-sekundnog perioda inicijalizacije, potrebnog samo jednom po sesiji.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">U pra\u0107enju biljaka potpomognutom umjetnom inteligencijom (AI), kamere i senzori prikupljali su vitalne podatke o biljkama, koje su analizirali algoritmi strojnog u\u010denja i umjetne inteligencije. Ta je analiza igrala klju\u010dnu ulogu u procjeni zdravlja biljaka, preciziranju stresa, bolesti ili drugih faktora koji utje\u010du na rast.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Aplikacije su se pro\u0161irile od optimiziranja poljoprivredne produktivnosti do pra\u0107enja prirodnih ekosustava poput \u0161uma, poma\u017eu\u0107i u naporima za za\u0161titu prirode i pobolj\u0161avaju\u0107i razumijevanje utjecaja na okoli\u0161.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-object-detection-in-plant-monitoring\">Detekcija objekata u nadzoru biljaka<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Sljede\u0107a faza nakon segmentacije biljaka \u0161e\u0107erne repe je detekcija objekata usmjerena na razumijevanje specifi\u010dnosti svake biljke u smislu zdravlja, rasta i drugih \u010dimbenika. Za detekciju objekata u pra\u0107enju biljaka, implementirani su napredni modeli poput YOLOv4, MobileNetV2 i VGG-19 s mehanizmima pa\u017enje. Ovi modeli analizirali su segmentirane slike \u0161e\u0107erne repe kako bi otkrili specifi\u010dna podru\u010dja stresa i bolesti, omogu\u0107uju\u0107i precizne i ciljane intervencije.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Projekt je postigao zna\u010dajne prekretnice u otkrivanju bolesti, treniraju\u0107i modele ResNet-18 i ResNet-34 pred-trenirane na ImageNetu. Ovi modeli pokazali su impresivnu to\u010dnost od 0,88 u identificiranju bolesti koje utje\u010du na biljke \u0161e\u0107erne repe, s povr\u0161inom ispod ROC krivulje (AUC) od 0,898. Modeli su pokazali visoku pouzdanost predvi\u0111anja, precizno razlikuju\u0107i bolesne i zdrave biljke.<\/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\/hr\/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=\"Primjer ulazno-izlaznog rada detekcije objekata\" 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\">Primjer ulazno-izlaznog rada detekcije objekata<\/figcaption><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\">Projekt je koristio sustavni pristup otkrivanju bolesti, segmentiraju\u0107i slike u standardizirane dijelove. Ti su dijelovi podvrgnuti preciznom ozna\u010davanju pomo\u0107u interaktivnih alata za lociranje podru\u010dja zahva\u0107enih bolestima. Detekcija objekata dodatno je pobolj\u0161ala to\u010dnost definiranjem okvirnih kutija oko biljaka, \u0161to je olak\u0161alo precizno pra\u0107enje zdravlja biljaka.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-plant-production-prediction\">Predvi\u0111anje proizvodnje usjeva<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">U domeni predvi\u0111anja proizvodnje biljaka, AI modeli su iskoristili ekolo\u0161ke podatke kao \u0161to su vremenske prilike i parametri tla za prognoziranje prinosa usjeva. Kori\u0161teni su regresijski modeli kao \u0161to su Isolation Forest, Linearna regresija i Ridge regresija.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Ovi modeli integrirali su numeri\u010dke zna\u010dajke izvu\u010dene iz podru\u010dja obuhvatnih okvira zajedno s podacima o tlu kako bi optimizirali primjenu gnojiva.<\/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\/hr\/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=\"\u0160e\u0107erna repa na pokusnom 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\">\u0160e\u0107erna repa na pokusnom polju<\/figcaption><\/figure>\n<\/div>\n\n\n<h2 class=\"wp-block-heading\" id=\"h-model-deployment-considerations\">Razmatranja kod implementacije modela<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Procijenjene su strategije implementacije razvijenih modela za rubne ure\u0111aje i ra\u010dunalne platforme u oblaku. Implementacija modela na rubnim ure\u0111ajima nudila je prednosti poput smanjenja tro\u0161kova i ni\u017ee latencije.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Me\u0111utim, ovakav pristup bi mogao \u017ertvovati potencijalnu to\u010dnost zbog hardverskih ograni\u010denja. S druge strane, implementacija u oblaku nudila je br\u017ea vremena izvo\u0111enja pomo\u0107u GPU-ova visokih performansi, ali je mogla uzrokovati dodatne tro\u0161kove i ovisila je o internetskoj povezanosti, \u0161to bi moglo unijeti ka\u0161njenje u komunikaciji.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-comparative-analysis-with-5g-network\">Usporedna analiza s 5G mre\u017eom<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Usporedna analiza pokazala je da je kori\u0161tenje 5G mre\u017ee zna\u010dajno pobolj\u0161alo segmentaciju \u0161e\u0107erne repe u usporedbi s tradicionalnim 4G\/WiFi postavkama. Ovo pobolj\u0161anje dokazano je smanjenjem prosje\u010dnog vremena postavljanja i vremena umre\u017eavanja, nagla\u0161avaju\u0107i dobitke u u\u010dinkovitosti postignute 5G tehnologijom.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Proces pripreme podataka<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Proces pripreme podataka obuhva\u0107ao je prikupljanje skupova podataka zdravih i bolesnih biljaka, detekciju korova, identifikaciju faza rasta i ekstrakciju slika iz sirovog 4K videa. Tehnike poput ekvalizacije histograma, filtriranja slika i transformacije HSV prostora boja kori\u0161tene su za pripremu podataka za analizu.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Sakupljeni su uzorci zdravog li\u0161\u0107a \u0161e\u0107erne repe te oboljelog li\u0161\u0107a, poput li\u0161\u0107a kukuruza s pepelnicom. Ekstrakcija zna\u010dajki bolesti uklju\u010divala je odvajanje li\u0161\u0107a od pozadine, mijenjanje veli\u010dine, transformiranje i spajanje slika radi stvaranja realisti\u010dnih uzoraka za analizu.<\/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\/hr\/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=\"Proces anotacije za segmentaciju\" 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\">Proces anotacije za segmentaciju<\/figcaption><\/figure>\n<\/div>\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Petlja aktivnog u\u010denja<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Pokrenuta je aktivna petlja u\u010denja s neozna\u010denim podacima, kori\u0161tenim za treniranje modela za detekciju. Ti su modeli generirali upite za anotaciju koje su obra\u0111ivali ljudski anotatori, neprekidno pobolj\u0161avaju\u0107i to\u010dnost modela kroz iterativne cikluse treniranja i anotacije.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Anatacija podataka putem multimodalnog temeljnog modela<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Rje\u0161avaju\u0107i izazov ograni\u010denih ozna\u010denih podataka, projekt je iskoristio robusne temeljne modele za generiranje anotacija istine. Zna\u010dajno, CLIP, model temeljen na transformeru koji je razvio OpenAI, obu\u010den na ogromnom skupu podataka od preko 400 milijuna parova slika i teksta, igrao je klju\u010dnu ulogu.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Koriste\u0107i Vision Transformere kao svoju osnovu, CLIP je postigao izvanrednih 95% to\u010dnosti na validacijskim skupovima, efikasno kategoriziraju\u0107i slike u razli\u010dite klase kao \u0161to su \u0161e\u0107erna repa i korov s visokom precizno\u0161\u0107u.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Tehnologija dronova za prikupljanje podataka<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Jedna od klju\u010dnih tehnologija kori\u0161tenih u projektu bila je upotreba dronova opremljenih RGB kamerama koje su snimale 4K video. Ovi dronovi pru\u017eili su detaljne slike (rezolucije 3840\u00d72160) za analizu.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Predobrada ovih slika zna\u010dajno je pove\u0107ala to\u010dnost modela, s primjetnim pobolj\u0161anjima uo\u010denim kod modela poput VGGNeta (+38,52%), ResNet50 (+21,14%), DenseNet121 (+7,53%) i MobileNet (+6,6%).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Tehnike poput izjedna\u010davanja histograma kori\u0161tene su za pove\u0107anje kontrasta slike, dok je transformacija u HSV \\[Human, Saturation, Value] \\[bojni prostor\\] pomogla naglasiti biljne povr\u0161ine i istaknuti relevantne zna\u010dajke.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Generiranje sinteti\u010dkih podataka<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Da bi se rije\u0161io izazov ograni\u010denih podataka o slikama, generirani su sinteti\u010dki skupovi podataka putem strojnog u\u010denja i umjetne inteligencije. Prikupljanje podataka izvedeno je pomo\u0107u dronova koji lete na visinama izme\u0111u 1 m i 4 m i brzinama od 2 m\/s ili vi\u0161e, koriste\u0107i 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\/hr\/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=\"Emulacijsko okru\u017eenje\" 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 okru\u017eenje<\/figcaption><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\">Kori\u0161tena su i druga vozila, poput traktora, za prikupljanje podataka. Generiranje ovih sinteti\u010dkih podataka pokazalo se posebno korisnim za otkrivanje bolesti \u0161e\u0107erne repe.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-conclusion\">Zaklju\u010dak<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Projekt \u201c5G mre\u017ee kao pokreta\u010d u\u010denja u stvarnom vremenu u odr\u017eivoj poljoprivredi\u201d uspje\u0161no je demonstrirao kako 5G tehnologija mo\u017ee pobolj\u0161ati ekolo\u0161ke, ekonomske i odr\u017eive aspekte uzgoja \u0161e\u0107erne repe. Kroz suradnju s HSHL-om i Pfeifer &amp; Langen, projekt je integrirao prikupljanje podataka u stvarnom vremenu i analizu pokretanu umjetnom inteligencijom, pobolj\u0161avaju\u0107i u\u010dinkovitost i smanjuju\u0107i nepotrebne posjete poljima. <\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Posve\u0107ena 5G mre\u017ena infrastruktura omogu\u0107ila je preciznu primjenu gnojiva i sredstava za za\u0161titu bilja. Geopard Agriculture odigrao je klju\u010dnu ulogu u razvoju scenarija za detekciju i nadzor biljaka te stvaranju prototipa sustava strojnog u\u010denja za 5G poljoprivredno okru\u017eenje. Uspjeh projekta naglasio je va\u017enost naprednih tehnologija u odr\u017eivoj poljoprivredi, isti\u010du\u0107i potencijal 5G mre\u017ee za poticanje inovacija i u\u010dinkovitosti. <\/p>","protected":false},"excerpt":{"rendered":"<p>Uzbu\u0111eni smo \u0161to mo\u017eemo objaviti uspje\u0161an zavr\u0161etak projekta \u201c5G mre\u017ee kao pokreta\u010d u\u010denja u stvarnom vremenu u odr\u017eivoj poljoprivredi\u201d, koji je djelomi\u010dno podr\u017ean od strane\u2026<\/p>","protected":false},"author":210249433,"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":"","_crdt_document":"","content-type":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","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 Premium plugin v21.6 (Yoast SEO v27.4) - 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