{"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-mojliggjort-realtidsinlarning-inom-hallbart-jordbruk-en-studie-om-sockerbetsproduktion","status":"publish","type":"post","link":"https:\/\/geopard.tech\/swe\/blog\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\/","title":{"rendered":"5G-aktiverat realtidsinl\u00e4rning inom h\u00e5llbart jordbruk: En studie om sockerbetor"},"content":{"rendered":"<p class=\"wp-block-paragraph\">Vi \u00e4r glada att kunna meddela att projektet \u201c5G-n\u00e4tverk som m\u00f6jligg\u00f6rare f\u00f6r realtidsinl\u00e4rning inom h\u00e5llbart jordbruk\u201d har slutf\u00f6rts med framg\u00e5ng, vilket st\u00f6ds av delvis finansiering fr\u00e5n ministeriet f\u00f6r ekonomi, industri, klimatpolitik och energi i delstaten Nordrhein-Westfalen.<\/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\">Detta initiativ representerar ett betydande steg fram\u00e5t i att utforska den transformativa potentialen hos 5G-teknik inom jordbruket, s\u00e4rskilt inriktat p\u00e5 att f\u00f6rb\u00e4ttra de ekologiska, ekonomiska och h\u00e5llbara aspekterna av sockerbetsodling.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Den utnyttjade 5G:s l\u00e5ga latens f\u00f6r att integrera avancerade informationsteknologisystem i realtid, vilket m\u00f6jliggjorde omedelbara svar p\u00e5 sensor- och positionsdata inom f\u00f6rdefinierade tidsramar.<\/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\/swe\/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=\"Bild fr\u00e5n projektpresentationens slutevenemang p\u00e5 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\">Bild fr\u00e5n projektpresentationens slutevenemang p\u00e5 Hochschule Hamm-Lippstadt (HSHL)<\/figcaption><\/figure>\n<\/div>\n\n\n<h2 class=\"wp-block-heading\" id=\"h-project-focus-and-partnership\">Projektfokus och partnerskap<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">I samarbete med partners p\u00e5 HSHL och med st\u00f6d av Pfeifer &amp; Langen fokuserade projektet p\u00e5 att studera hela livscykeln f\u00f6r sockerbetsodling p\u00e5 partnernas \u00e5krar. Syftet var att visa hur 5G skulle kunna fungera som en central teknikkatalysator inom Nordrhein-Westfalens jordbrukssektor och visa upp dess potential som en m\u00f6jligg\u00f6rare f\u00f6r innovation och effektivitet.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-role-of-geopard-agriculture\">GeoPard-jordbrukets roll<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">GeoPard Agriculture spelade en avg\u00f6rande roll i att definiera och implementera viktiga aspekter av projektet, inklusive scenarier f\u00f6r v\u00e4xtdetektering, \u00f6vervakning och produktionsprognoser. Vi utvecklade ett prototyp-AI-system skr\u00e4ddarsytt f\u00f6r 5G-jordbruksmilj\u00f6n, exekverade modeller inom en molninfrastruktur och skapade en mobilapplikation f\u00f6r realtidsinteraktion med molnbaserade modeller.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-technological-integration\">Teknologisk integration<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Metoder f\u00f6r artificiell intelligens (AI) implementerades via en robust molninfrastruktur med h\u00f6g ber\u00e4kningskapacitet. AI-algoritmer kategoriserade v\u00e4xter i realtid under varje korsning och \u00f6vervakade deras tillv\u00e4xt under hela deras livscykel, vilket eliminerade behovet av on\u00f6diga f\u00e4ltbes\u00f6k enbart f\u00f6r datainsamling.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Denna utveckling m\u00f6jliggjorde exakt applicering av g\u00f6dningsmedel och v\u00e4xtskyddsmedel, och justerade dynamiskt appliceringsm\u00e4ngderna under korsningar genom maskininl\u00e4rningsalgoritmer.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-deployment-of-unmanned-vehicles\">Utplacering av obemannade fordon<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Dessutom utnyttjade projektet den reducerade latensen hos 5G f\u00f6r att drifts\u00e4tta obemannade fordon f\u00f6r \u00f6vervakning av anl\u00e4ggningar och datainsamling. Dessa fordon spelade en avg\u00f6rande roll f\u00f6r att samla in realtidsinsikter och ytterligare optimera jordbruksmetoder.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-project-outcomes-enhancing-sugar-beet-production-with-5g-technology\">Projektresultat: \u00d6kad sockerbetsproduktion med 5G-teknik<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Projektet visade hur 5G-teknik skulle kunna fungera som en transformativ m\u00f6jligg\u00f6rare inom jordbrukssektorn i Nordrhein-Westfalen genom att analysera hela livscykeln f\u00f6r sockerbetsodling och lyfta fram betydande f\u00f6rb\u00e4ttringar som m\u00f6jligg\u00f6rs av 5G-tekniken. F\u00f6r att effektivt demonstrera projektets resultat har forskarna anv\u00e4nt arbetspaket som inneh\u00e5ller olika scenarier och infrastrukturer.<\/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\/swe\/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=\"Testf\u00e4lt f\u00f6r sockerbetor \" 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\">Testf\u00e4lt f\u00f6r sockerbetor<\/figcaption><\/figure>\n<\/div>\n\n\n<h3 class=\"wp-block-heading\" id=\"h-scenario-definition-considering-existing-geodata-and-ml-infrastructure\">Scenariodefinition med h\u00e4nsyn till befintlig geodata- och ML-infrastruktur<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Projektet visade hur traditionella processer inom sockerbetsproduktionens livscykel kunde f\u00f6rb\u00e4ttras genom integration av 5G-teknik. De viktigaste m\u00e5len inkluderade:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Utvecklade implementationsf\u00e4rdiga scenarier f\u00f6r anl\u00e4ggningsidentifiering, \u00f6vervakning och produktionsprognoser.<\/li>\n\n\n\n<li>Fastst\u00e4llda tekniska krav som \u00e4r n\u00f6dv\u00e4ndiga f\u00f6r en framg\u00e5ngsrik implementering av dessa scenarier.<\/li>\n\n\n\n<li>Identifierade och bed\u00f6mde relevanta ekologiska och ekonomiska indikatorer f\u00f6r att utv\u00e4rdera merv\u00e4rdet som 5G-n\u00e4tet medf\u00f6r.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Denna fas understr\u00f6k projektets engagemang f\u00f6r att integrera banbrytande teknik med befintliga jordbruksmetoder. Denna arkitektur utnyttjade 5G-n\u00e4tverkets h\u00f6ghastighetsanslutning f\u00f6r att underl\u00e4tta insamling och bearbetning av data i realtid mellan edge-enheter och molnet. Molninfrastrukturen tillhandah\u00f6ll viktiga resurser f\u00f6r utbildning och drifts\u00e4ttning av storskaliga AI-modeller, medan AI-plattformen erbj\u00f6d robusta verktyg f\u00f6r modellutveckling och drifts\u00e4ttning. Applikationslagret presenterade handlingsbara insikter fr\u00e5n AI-modeller till slutanv\u00e4ndare, vilket f\u00f6rb\u00e4ttrade beslutsfattandekapaciteten.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-machine-learning-and-ai-in-the-context-of-5g\">Maskininl\u00e4rning och AI i samband med 5G<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Fokus f\u00f6r denna del var att anpassa befintliga maskininl\u00e4rnings- och AI-system f\u00f6r att anpassa sig till de scenarier som beskrivs ovan, och optimera dem d\u00e4refter. De viktigaste m\u00e5len inkluderade:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Definiera systemets m\u00e5l och utveckla systemets arkitektur<\/li>\n\n\n\n<li>Insamlade markdata f\u00f6r tr\u00e4ning och validering av AI-modeller.<\/li>\n\n\n\n<li>Etablerade och kommenterade en l\u00e4mplig databas skr\u00e4ddarsydd f\u00f6r identifiering och \u00f6vervakning av v\u00e4xter.<\/li>\n\n\n\n<li>Integrerade AI-modeller s\u00f6ml\u00f6st i 5G-n\u00e4tverkets infrastruktur.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">I den h\u00e4r fasen spelade edge-enheter utrustade med mobiltelefon-SIM-kort som anv\u00e4nde 5G-teknik en avg\u00f6rande roll. Nyckeltal (KPI:er) som latens eller end-to-end (E2E) latens \u00f6vervakades noggrant. M\u00e4tningarna inkluderade bed\u00f6mning av tillf\u00f6rlitligheten och tillg\u00e4ngligheten f\u00f6r korrekt mottagna datapaket, tillsammans med analys av anv\u00e4ndardatahastigheter och toppdatahastigheter.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Vidare gjordes antaganden baserade p\u00e5 str\u00f6mmande UHD-uppl\u00f6sningsvideo i MP4-format, \u00f6verf\u00f6rd via Transmission Control Protocol (TCP). Potentiella l\u00f6sningar som utforskades inkluderade optimering med enskilda bilder ist\u00e4llet f\u00f6r kontinuerliga videostr\u00f6mmar, utf\u00f6rande av basoptimeringar direkt p\u00e5 edge-enheter och implementering av modellkvantiseringstekniker f\u00f6r att f\u00f6rb\u00e4ttra effektiviteten.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-cloud-infrastructure-and-aws-services\">Molninfrastruktur och AWS-tj\u00e4nster<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Projektet f\u00f6rlitade sig starkt p\u00e5 molninfrastruktur som utnyttjade AWS-tj\u00e4nster som Lambda, SageMaker, S3, CloudWatch och RDS, vilka spelade en avg\u00f6rande roll f\u00f6r att tillhandah\u00e5lla de n\u00f6dv\u00e4ndiga resurserna f\u00f6r utbildning och drifts\u00e4ttning av AI-modeller.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">AWS Lambda anv\u00e4ndes f\u00f6r effektiv instanshantering och applikationsserver, medan AWS SageMaker underl\u00e4ttade konstruktionen av robusta maskininl\u00e4rningspipelines. Lagringsl\u00f6sningar som S3, CloudWatch och RDS var viktiga f\u00f6r att lagra datam\u00e4ngder och loggar som \u00e4r avg\u00f6rande f\u00f6r driften av maskininl\u00e4rningsmodeller och neurala n\u00e4tverk.<\/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\/swe\/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 molninfrastruktur\" 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 molninfrastruktur<\/figcaption><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\">D\u00e4rf\u00f6r st\u00f6dde denna infrastruktur de realtidsdatabehandlingsfunktioner som 5G-n\u00e4tet m\u00f6jligg\u00f6r.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-5g-network-latency\">5G-n\u00e4tverkslatens<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">5G-n\u00e4tverk utformades f\u00f6r att uppn\u00e5 ultral\u00e5g latens, vanligtvis mellan 1 och 10 millisekunder. Denna latens \u00e5terspeglade den tid det tar f\u00f6r data att f\u00e4rdas mellan mobila enheter och AWS-servrar via 5G-n\u00e4tverket. Enhetsspecifika bearbetningsfunktioner, s\u00e5som hastigheten f\u00f6r att ta och bearbeta foton p\u00e5 smartphones med h\u00f6gpresterande processorer, p\u00e5verkade ocks\u00e5 latensen.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Datauppladdningshastigheterna i 5G-n\u00e4tet och storleken p\u00e5 fotot p\u00e5verkade data\u00f6verf\u00f6ringstiderna till AWS. AWS bidrog ytterligare till latens med bearbetningstider f\u00f6r uppgifter som neurala n\u00e4tverksbaserad detektering och segmentering, vilka varierade beroende p\u00e5 algoritmens komplexitet och AWS-tj\u00e4nstens effektivitet. Efter bearbetning laddades resultaten ner tillbaka till mobila enheter, p\u00e5verkade av nedladdningshastigheten f\u00f6r 5G och storleken p\u00e5 resultatdatan.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-plant-recognition-using-ai\">V\u00e4xtigenk\u00e4nning med hj\u00e4lp av AI<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Inom v\u00e4xtidentifiering involverade AI-drivna processer att skapa en omfattande databas med v\u00e4xtbilder f\u00f6r tr\u00e4ningsalgoritmer baserade p\u00e5 neurala n\u00e4tverk. Dessa algoritmer tr\u00e4nades f\u00f6r att skilja sockerbetsarter fr\u00e5n andra v\u00e4xter genom att k\u00e4nna igen egenskaper som \u00e4r specifika f\u00f6r den specifika v\u00e4xttypen, s\u00e5som bladformer, blomf\u00e4rger etc.<\/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\/swe\/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=\"Fenologisk utveckling av sockerbetsplantor\" 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\">Fenologisk utveckling av sockerbetsplantor. K\u00e4lla: <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\">H\u00e4r menar vi med v\u00e4xtigenk\u00e4nning uppgiften att uppt\u00e4cka ogr\u00e4s och segmentera sockerbetsplantor.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Ogr\u00e4sdetektering<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">F\u00f6r ogr\u00e4sdetektering anv\u00e4nde projektet MobileNet-v3, som tr\u00e4nades med omfattande dataut\u00f6kningar och viktad sampling. Denna modell uppn\u00e5dde en imponerande noggrannhet p\u00e5 0,984 och en AUC p\u00e5 0,998.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Sockerbetssegmentering<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">F\u00f6r segmenteringsuppgifter anv\u00e4ndes modeller som YOLACT, ResNeSt, SOLO och U-net f\u00f6r att exakt avgr\u00e4nsa individuella sockerbetsprover i bilder. D\u00e4refter valdes den mest effektiva modellen baserat p\u00e5 olika kriterier: hastighet, inferenstid etc. Data f\u00f6r segmentering h\u00e4mtades fr\u00e5n dr\u00f6nartagna RGB-bilder, vilka \u00e4ndrades i storlek och kommenterades f\u00f6r tr\u00e4nings- och validerings\u00e4ndam\u00e5l.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Segmenteringsuppgifterna innebar att skapa masker som exakt avgr\u00e4nsade v\u00e4xtgr\u00e4nser. Denna metod minskade m\u00e4nskliga annoteringsinsatser samtidigt som effektiviteten optimerades. Genom att prioritera m\u00e4rkning av utmanande prover f\u00f6rb\u00e4ttrades modellens prestanda avsev\u00e4rt. Iterativ omskolning och os\u00e4kerhetsprovtagningsstrategier har visat sig effektiva och uppn\u00e5tt segmenteringsnoggrannhetsgrader som \u00f6verstiger 98% \u00f6ver olika tillv\u00e4xtstadier.<\/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\/swe\/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=\"Exempel p\u00e5 input-output vid segmentering\" 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\">Exempel p\u00e5 input-output vid segmentering<\/figcaption><\/figure>\n<\/div>\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Modellutv\u00e4rdering<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Modellen tr\u00e4nades med rigor\u00f6sa dataut\u00f6kningar. Modellen utv\u00e4rderades med hj\u00e4lp av olika m\u00e4tv\u00e4rden, inklusive Intersection over Union (IoU). Inferensanalys f\u00f6r den byggda modellen, utf\u00f6rd p\u00e5 en delm\u00e4ngd fr\u00e5n datasetet &quot;plant seedlings v2&quot;, visade en noggrannhet p\u00e5 81%. Inferenstiden tog cirka 320 millisekunder att ber\u00e4kna efter en 7-sekunders initialiseringsperiod, vilket endast var n\u00f6dv\u00e4ndigt en g\u00e5ng per session.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Vid v\u00e4xt\u00f6vervakning med hj\u00e4lp av artificiell intelligens (AI) samlade kameror och sensorer in viktig v\u00e4xtdata, som analyserades med hj\u00e4lp av maskininl\u00e4rning och AI-algoritmer. Denna analys spelade en avg\u00f6rande roll f\u00f6r att bed\u00f6ma v\u00e4xternas h\u00e4lsa, identifiera stress, sjukdomar eller andra faktorer som p\u00e5verkar tillv\u00e4xten.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Till\u00e4mpningar str\u00e4ckte sig fr\u00e5n att optimera jordbruksproduktiviteten till att \u00f6vervaka naturliga ekosystem som skogar, st\u00f6dja bevarandeinsatser och \u00f6ka f\u00f6rst\u00e5elsen av milj\u00f6p\u00e5verkan.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-object-detection-in-plant-monitoring\">Objektdetektering i anl\u00e4ggnings\u00f6vervakning<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">N\u00e4sta fas efter segmentering av sockerbetsplantor \u00e4r objektdetektering som syftar till att f\u00f6rst\u00e5 varje plantas specifika egenskaper vad g\u00e4ller h\u00e4lsa, tillv\u00e4xt och andra faktorer. F\u00f6r objektdetektering i v\u00e4xt\u00f6vervakning anv\u00e4ndes avancerade modeller som YOLOv4, MobileNetV2 och VGG-19 med uppm\u00e4rksamhetsmekanismer. Dessa modeller analyserade segmenterade bilder av sockerbetor f\u00f6r att uppt\u00e4cka specifika stress- och sjukdomsomr\u00e5den, vilket m\u00f6jliggjorde exakta och riktade interventioner.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Projektet uppn\u00e5dde betydande milstolpar inom sjukdomsdetektering och tr\u00e4ning av ResNet-18- och ResNet-34-modeller som f\u00f6rtr\u00e4nats p\u00e5 ImageNet. Dessa modeller visade en imponerande noggrannhet p\u00e5 0,88 vid identifiering av sjukdomar som drabbar sockerbetsplantor, med en arean under ROC-kurvan (AUC) p\u00e5 0,898. Modellerna uppvisade h\u00f6g prediktionss\u00e4kerhet och kunde korrekt skilja mellan sjuka och friska plantor.<\/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\/swe\/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=\"Exempel p\u00e5 indata-utdata f\u00f6r objektdetektering\" 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\">Exempel p\u00e5 indata-utdata f\u00f6r objektdetektering<\/figcaption><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\">Projektet anv\u00e4nde en systematisk metod f\u00f6r sjukdomsdetektering, d\u00e4r bilderna segmenterades i standardiserade omr\u00e5den. Dessa omr\u00e5den genomgick noggranna anteckningar med hj\u00e4lp av interaktiva verktyg f\u00f6r att lokalisera omr\u00e5den som drabbats av sjukdomar. Objektdetektering f\u00f6rb\u00e4ttrade ytterligare noggrannheten genom att avgr\u00e4nsa v\u00e4xter, vilket underl\u00e4ttade exakt \u00f6vervakning av v\u00e4xternas h\u00e4lsa.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-plant-production-prediction\">F\u00f6ruts\u00e4gelse av v\u00e4xtproduktion<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Inom omr\u00e5det f\u00f6r prognoser av v\u00e4xtproduktion utnyttjade AI-modeller milj\u00f6data som v\u00e4derf\u00f6rh\u00e5llanden och jordparametrar f\u00f6r att prognostisera gr\u00f6dor. Regressionsmodeller som Isolation Forest, Linear Regression och Ridge Regression anv\u00e4ndes.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Dessa modeller integrerade numeriska funktioner extraherade fr\u00e5n avgr\u00e4nsande rutor tillsammans med jorddata f\u00f6r att optimera g\u00f6dselmedelsapplikationen.<\/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\/swe\/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=\"Sockerbetor p\u00e5 testf\u00e4lt\" 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\">Sockerbetor p\u00e5 testf\u00e4lt<\/figcaption><\/figure>\n<\/div>\n\n\n<h2 class=\"wp-block-heading\" id=\"h-model-deployment-considerations\">Att t\u00e4nka p\u00e5 vid modelldistribution<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Implementeringsstrategier f\u00f6r de utvecklade modellerna utv\u00e4rderades f\u00f6r b\u00e5de edge-enheter och molnplattformar. Att distribuera modeller p\u00e5 edge-enheter erbj\u00f6d f\u00f6rdelar som minskade kostnader och l\u00e4gre latens.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Denna metod kan dock kompromissa med potentiell noggrannhet p\u00e5 grund av h\u00e5rdvarubegr\u00e4nsningar. \u00c5 andra sidan erbj\u00f6d molndistribution snabbare inferenstider med h\u00f6gpresterande GPU:er, men kunde medf\u00f6ra ytterligare kostnader och var beroende av internetanslutning, vilket kunde introducera kommunikationslatens.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-comparative-analysis-with-5g-network\">J\u00e4mf\u00f6rande analys med 5G-n\u00e4tverk<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">En j\u00e4mf\u00f6rande analys visade att anv\u00e4ndningen av ett 5G-n\u00e4tverk avsev\u00e4rt f\u00f6rb\u00e4ttrade segmenteringen av sockerbetor j\u00e4mf\u00f6rt med traditionella 4G\/WiFi-uppst\u00e4llningar. Denna f\u00f6rb\u00e4ttring framgick av minskade genomsnittliga uppkopplings- och n\u00e4tverkstider, vilket belyser de effektivitetsvinster som uppn\u00e5tts genom 5G-tekniken.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Dataf\u00f6rberedelseprocess<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Dataf\u00f6rberedelseprocessen innebar insamling av datam\u00e4ngder av friska och sjuka v\u00e4xter, detektering av ogr\u00e4s, identifiering av tillv\u00e4xtstadier och extrahering av bilder fr\u00e5n 4K-r\u00e5video. Tekniker som histogramutj\u00e4mning, bildfiltrering och HSV-f\u00e4rgrymdstransformation anv\u00e4ndes f\u00f6r att f\u00f6rbereda data f\u00f6r analys.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Prover av friska sockerbetsblad och sjuka prover, s\u00e5som majsblad med gr\u00e5bladsfl\u00e4ckar, samlades in. Extraktion av sjukdomsdrag innebar att bladet separerades fr\u00e5n bakgrunden, \u00e4ndrades storlek, omvandlades och sammanfogades f\u00f6r att skapa realistiska prover f\u00f6r analys.<\/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\/swe\/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=\"Annoteringsprocess f\u00f6r segmentering\" 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\">Annoteringsprocess f\u00f6r segmentering<\/figcaption><\/figure>\n<\/div>\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Aktiv inl\u00e4rningsslinga<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">En aktiv inl\u00e4rningsslinga initierades med om\u00e4rkt data, som anv\u00e4ndes f\u00f6r att tr\u00e4na detekteringsmodeller. Dessa modeller genererade annoteringsfr\u00e5gor som adresserades av m\u00e4nskliga annotat\u00f6rer, och kontinuerligt f\u00f6rfinade modellens noggrannhet genom iterativ tr\u00e4ning och annoteringscykler.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Dataannotering via multimodal grundmodell<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">F\u00f6r att hantera utmaningen med begr\u00e4nsad m\u00e4rkt data utnyttjade projektet robusta grundmodeller f\u00f6r att generera sanningsannoteringar. Framf\u00f6r allt spelade CLIP, en transformerbaserad modell utvecklad av OpenAI, tr\u00e4nad p\u00e5 en stor datam\u00e4ngd med \u00f6ver 400 miljoner bild-text-par, en avg\u00f6rande roll.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Med hj\u00e4lp av Vision Transformers som ryggrad uppn\u00e5dde CLIP en anm\u00e4rkningsv\u00e4rd 95%-noggrannhet p\u00e5 valideringsupps\u00e4ttningar, och kategoriserade effektivt bilder i distinkta klasser som sockerbetor och ogr\u00e4s med h\u00f6g precision.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Dr\u00f6narteknik f\u00f6r datainsamling<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">En av de viktigaste teknikerna som anv\u00e4ndes i projektet var anv\u00e4ndningen av dr\u00f6nare utrustade med RGB-kameror som spelade in 4K-video. Dessa dr\u00f6nare gav detaljerade bilder (3840\u00d72160 uppl\u00f6sning) f\u00f6r analys.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">F\u00f6rbehandling av dessa bilder \u00f6kade modellens noggrannhet avsev\u00e4rt, med m\u00e4rkbara f\u00f6rb\u00e4ttringar observerade i modeller som VGGNet (+38.52%), ResNet50 (+21.14%), DenseNet121 (+7.53%) och MobileNet (+6.6%).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Tekniker som histogramutj\u00e4mning anv\u00e4ndes f\u00f6r att f\u00f6rb\u00e4ttra bildkontrasten, medan omvandling till HSV-f\u00e4rgrymd hj\u00e4lpte till att betona v\u00e4xtomr\u00e5den och framh\u00e4va relevanta funktioner.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Generering av syntetisk data<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">F\u00f6r att hantera utmaningen med begr\u00e4nsad bilddata genererades syntetiska datam\u00e4ngder via maskininl\u00e4rning och AI. Datainsamlingen utf\u00f6rdes med hj\u00e4lp av dr\u00f6nare som fl\u00f6g p\u00e5 h\u00f6jder mellan 1 m och 4 m och hastigheter p\u00e5 2 m\/s eller mer, med hj\u00e4lp av RGB-kameror.<\/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\/swe\/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=\"Emuleringsmilj\u00f6\" 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\">Emuleringsmilj\u00f6<\/figcaption><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\">Andra fordon, s\u00e5som traktorer, anv\u00e4ndes ocks\u00e5 f\u00f6r datainsamling. Denna syntetiska datagenerering visade sig vara s\u00e4rskilt f\u00f6rdelaktig f\u00f6r att uppt\u00e4cka sjukdomar hos sockerbetor.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-conclusion\">Slutsats<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Projektet \u201c5G-n\u00e4tverk som m\u00f6jligg\u00f6rare f\u00f6r realtidsinl\u00e4rning inom h\u00e5llbart jordbruk\u201d visade framg\u00e5ngsrikt hur 5G-teknik kan f\u00f6rb\u00e4ttra de ekologiska, ekonomiska och h\u00e5llbara aspekterna av sockerbetsodling. Genom samarbete med HSHL och Pfeifer &amp; Langen integrerade projektet datainsamling i realtid och AI-driven analys, vilket f\u00f6rb\u00e4ttrade effektiviteten och minskade on\u00f6diga f\u00e4ltbes\u00f6k. <\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Ett dedikerat 5G-campusn\u00e4tverk m\u00f6jliggjorde exakta appliceringar av g\u00f6dningsmedel och v\u00e4xtskyddsmedel. Geopard Agriculture spelade en avg\u00f6rande roll i utvecklingen av scenarier f\u00f6r v\u00e4xtdetektering och \u00f6vervakning, och i skapandet av ett prototypsystem f\u00f6r maskininl\u00e4rning f\u00f6r 5G-jordbruksmilj\u00f6n. Projektets framg\u00e5ng understr\u00f6k vikten av avancerad teknik inom h\u00e5llbart jordbruk och lyfte fram 5G:s potential att driva innovation och effektivitet. <\/p>","protected":false},"excerpt":{"rendered":"<p>Vi \u00e4r glada att kunna meddela att projektet &quot;5G-n\u00e4tverk som m\u00f6jligg\u00f6rare f\u00f6r realtidsinl\u00e4rning inom h\u00e5llbart jordbruk&quot; har slutf\u00f6rts med framg\u00e5ng, med delvis st\u00f6d fr\u00e5n...<\/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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