{"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-a-permis-invatarea-in-timp-real-in-agricultura-durabila-un-studiu-privind-productia-de-sfecla-de-zahar","status":"publish","type":"post","link":"https:\/\/geopard.tech\/ro\/blog\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\/","title":{"rendered":"5G-Enabled Real-time Learning in Sustainable Farming: Un studiu privind sfecla de zah\u0103r"},"content":{"rendered":"<p class=\"wp-block-paragraph\">Suntem \u00eenc\u00e2nta\u021bi s\u0103 anun\u021b\u0103m finalizarea cu succes a proiectului \u201cRe\u021belele 5G ca facilitator al \u00eenv\u0103\u021b\u0103rii \u00een timp real \u00een agricultura durabil\u0103\u201d, sus\u021binut par\u021bial prin finan\u021bare din partea Ministerului Afacerilor Economice, Industriei, Ac\u021biunii Climatice \u0219i Energiei din landul Renania de Nord-Westfalia.<\/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\">Aceast\u0103 ini\u021biativ\u0103 reprezint\u0103 un pas semnificativ \u00eenainte \u00een explorarea poten\u021bialului transformator al tehnologiei 5G \u00een agricultur\u0103, viz\u00e2nd \u00een special \u00eembun\u0103t\u0103\u021birea aspectelor ecologice, economice \u0219i sustenabile ale cultiv\u0103rii sfeclei de zah\u0103r.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A valorificat laten\u021ba redus\u0103 a 5G pentru a integra sisteme avansate de tehnologie informa\u021bional\u0103 \u00een timp real, permi\u021b\u00e2nd r\u0103spunsuri imediate la datele senzorilor \u0219i pozi\u021bionale \u00een intervale de timp predefinite.<\/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\/ro\/blog\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\/picture-from-the-final-event-of-the-project-presentation-at-hochschule-hamm-lippstadt-hshl\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Picture-from-the-final-event-of-the-project-presentation-at-Hochschule-Hamm-Lippstadt-HSHL.png?fit=624%2C386&amp;ssl=1\" data-orig-size=\"624,386\" data-comments-opened=\"1\" data-image-title=\"Picture from the final event of the project presentation at Hochschule Hamm-Lippstadt (HSHL)\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Picture-from-the-final-event-of-the-project-presentation-at-Hochschule-Hamm-Lippstadt-HSHL.png?fit=624%2C386&amp;ssl=1\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Picture-from-the-final-event-of-the-project-presentation-at-Hochschule-Hamm-Lippstadt-HSHL.png?resize=624%2C386&#038;ssl=1\" alt=\"Fotografie de la evenimentul final de prezentare a proiectului la 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\">Fotografie de la evenimentul final de prezentare a proiectului la Hochschule Hamm-Lippstadt (HSHL)<\/figcaption><\/figure>\n<\/div>\n\n\n<h2 class=\"wp-block-heading\" id=\"h-project-focus-and-partnership\">Focalizarea proiectului \u0219i parteneriatul<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">\u00cen colaborare cu partenerii de la HSHL \u0219i cu sprijinul Pfeifer &amp; Langen, proiectul s-a concentrat pe studierea \u00eentregului ciclu de via\u021b\u0103 al cultiv\u0103rii sfeclei de zah\u0103r pe c\u00e2mpurile apar\u021bin\u00e2nd partenerilor. Acesta a urm\u0103rit s\u0103 demonstreze modul \u00een care 5G ar putea servi drept catalizator tehnologic esen\u021bial \u00een sectorul agricol din Renania de Nord-Westfalia, demonstr\u00e2ndu-\u0219i poten\u021bialul de factor de inovare \u0219i eficien\u021b\u0103.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-role-of-geopard-agriculture\">Rolul GeoPard Agriculture<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">GeoPard Agriculture a jucat un rol crucial \u00een definirea \u0219i implementarea aspectelor cheie ale proiectului, inclusiv scenarii pentru detectarea, monitorizarea \u0219i predic\u021bia produc\u021biei plantelor. Am dezvoltat un prototip de sistem de inteligen\u021b\u0103 artificial\u0103 adaptat pentru mediul agricol 5G, am executat modele \u00eentr-o infrastructur\u0103 cloud \u0219i am creat o aplica\u021bie mobil\u0103 pentru interac\u021biunea \u00een timp real cu modele bazate pe cloud.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-technological-integration\">Integrare tehnologic\u0103<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Metodele de inteligen\u021b\u0103 artificial\u0103 (IA) au fost implementate prin intermediul unei infrastructuri cloud robuste, cu capacit\u0103\u021bi de calcul ridicate. Algoritmii de IA au clasificat plantele \u00een timp real \u00een timpul fiec\u0103rei \u00eencruci\u0219\u0103ri \u0219i au monitorizat cre\u0219terea acestora pe parcursul \u00eentregului ciclu de via\u021b\u0103, elimin\u00e2nd necesitatea vizitelor inutile pe teren, exclusiv \u00een scopuri de colectare a datelor.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Aceast\u0103 avansare a permis aplicarea precis\u0103 a \u00eengr\u0103\u0219\u0103mintelor \u0219i a produselor de protec\u021bie a culturilor, ajust\u00e2nd dinamic ratele de aplicare \u00een timpul travers\u0103rilor prin intermediul algoritmilor de \u00eenv\u0103\u021bare automat\u0103.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-deployment-of-unmanned-vehicles\">Implementarea vehiculelor f\u0103r\u0103 pilot<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">\u00cen plus, proiectul a utilizat laten\u021ba redus\u0103 a 5G pentru a implementa vehicule f\u0103r\u0103 pilot pentru monitorizarea plantelor \u0219i colectarea de date. Aceste vehicule au jucat un rol crucial \u00een colectarea de informa\u021bii \u00een timp real \u0219i optimizarea practicilor agricole.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-project-outcomes-enhancing-sugar-beet-production-with-5g-technology\">Rezultatele proiectului: \u00cembun\u0103t\u0103\u021birea produc\u021biei de sfecl\u0103 de zah\u0103r cu ajutorul tehnologiei 5G<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Proiectul a demonstrat modul \u00een care tehnologia 5G ar putea servi drept factor transformator \u00een sectorul agricol din Renania de Nord-Westfalia, analiz\u00e2nd \u00eentregul ciclu de via\u021b\u0103 al cultiv\u0103rii sfeclei de zah\u0103r, eviden\u021biind \u00eembun\u0103t\u0103\u021birile substan\u021biale facilitate de tehnologia 5G. Cu toate acestea, pentru a demonstra eficient rezultatele proiectului, cercet\u0103torii au folosit pachete de lucru care con\u021bin diferite scenarii \u0219i infrastructuri.<\/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\/ro\/blog\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\/sugar-beet-test-field\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Sugar-beet-test-field.png?fit=2048%2C1542&amp;ssl=1\" data-orig-size=\"2048,1542\" data-comments-opened=\"1\" data-image-title=\"Sugar beet test field\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Sugar-beet-test-field.png?fit=1024%2C771&amp;ssl=1\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Sugar-beet-test-field.png?resize=810%2C610&#038;ssl=1\" alt=\"C\u00e2mp de testare pentru sfecl\u0103 de zah\u0103r \" 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\">C\u00e2mp de testare pentru sfecl\u0103 de zah\u0103r<\/figcaption><\/figure>\n<\/div>\n\n\n<h3 class=\"wp-block-heading\" id=\"h-scenario-definition-considering-existing-geodata-and-ml-infrastructure\">Definirea scenariului lu\u00e2nd \u00een considerare infrastructura existent\u0103 de geodatele \u0219i ML<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Proiectul a demonstrat modul \u00een care procesele tradi\u021bionale din cadrul ciclului de via\u021b\u0103 al produc\u021biei de sfecl\u0103 de zah\u0103r ar putea fi \u00eembun\u0103t\u0103\u021bite prin integrarea tehnologiei 5G. Obiectivele cheie au inclus:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Am dezvoltat scenarii gata de implementare pentru recunoa\u0219terea, monitorizarea \u0219i predic\u021bia produc\u021biei plantelor.<\/li>\n\n\n\n<li>A stabilit cerin\u021bele tehnice necesare pentru implementarea cu succes a acestor scenarii.<\/li>\n\n\n\n<li>A identificat \u0219i evaluat indicatori ecologici \u0219i economici relevan\u021bi pentru a evalua valoarea ad\u0103ugat\u0103 adus\u0103 de re\u021beaua 5G.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Aceast\u0103 faz\u0103 a subliniat angajamentul proiectului de a integra tehnologia de ultim\u0103 genera\u021bie cu practicile agricole existente. Aceast\u0103 arhitectur\u0103 a valorificat conectivitatea de mare vitez\u0103 a re\u021belei 5G pentru a facilita colectarea \u0219i procesarea datelor \u00een timp real \u00eentre dispozitivele edge \u0219i cloud. Infrastructura cloud a furnizat resurse esen\u021biale pentru antrenarea \u0219i implementarea modelelor de inteligen\u021b\u0103 artificial\u0103 la scar\u0103 larg\u0103, \u00een timp ce platforma de inteligen\u021b\u0103 artificial\u0103 a oferit instrumente robuste pentru dezvoltarea \u0219i implementarea modelelor. Nivelul de aplica\u021bie a prezentat utilizatorilor finali informa\u021bii concrete derivate din modelele de inteligen\u021b\u0103 artificial\u0103, sporind capacit\u0103\u021bile de luare a deciziilor.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-machine-learning-and-ai-in-the-context-of-5g\">\u00cenv\u0103\u021barea automat\u0103 \u0219i inteligen\u021ba artificial\u0103 \u00een contextul 5G<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Obiectivul acestei p\u0103r\u021bi a fost adaptarea sistemelor existente de \u00eenv\u0103\u021bare automat\u0103 \u0219i inteligen\u021b\u0103 artificial\u0103 pentru a se alinia scenariilor descrise mai sus, optimiz\u00e2ndu-le \u00een consecin\u021b\u0103. Obiectivele cheie au inclus:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Defini\u021bi obiectivele sistemului \u0219i dezvolta\u021bi arhitectura acestuia<\/li>\n\n\n\n<li>Am colectat date concrete pentru antrenarea \u0219i validarea modelelor de IA.<\/li>\n\n\n\n<li>A creat \u0219i adnotat o baz\u0103 de date adecvat\u0103, adaptat\u0103 pentru identificarea \u0219i monitorizarea plantelor.<\/li>\n\n\n\n<li>Modele de inteligen\u021b\u0103 artificial\u0103 integrate perfect \u00een infrastructura re\u021belei 5G.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">\u00cen aceast\u0103 faz\u0103, dispozitivele edge echipate cu cartele SIM de telefoane mobile care utilizeaz\u0103 tehnologia 5G au jucat un rol crucial. Indicatorii cheie de performan\u021b\u0103 (KPI), cum ar fi laten\u021ba sau laten\u021ba end-to-end (E2E), au fost monitoriza\u021bi \u00eendeaproape. M\u0103sur\u0103torile au inclus evaluarea fiabilit\u0103\u021bii \u0219i disponibilit\u0103\u021bii pachetelor de date recep\u021bionate cu precizie, \u00eempreun\u0103 cu analiza ratelor de date ale utilizatorilor \u0219i a ratelor de date maxime.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u00cen plus, s-au f\u0103cut presupuneri bazate pe streamingul video cu rezolu\u021bie UHD \u00een format MP4, transmis prin Transmission Control Protocol (TCP). Printre solu\u021biile poten\u021biale explorate s-au num\u0103rat optimizarea cu imagini individuale \u00een loc de fluxuri video continue, efectuarea optimiz\u0103rilor de baz\u0103 direct pe dispozitivele de la marginea dispozitivului \u0219i implementarea tehnicilor de cuantizare a modelului pentru a spori eficien\u021ba.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-cloud-infrastructure-and-aws-services\">Infrastructur\u0103 cloud \u0219i servicii AWS<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Proiectul s-a bazat \u00een mare m\u0103sur\u0103 pe infrastructura cloud care valorific\u0103 servicii AWS precum Lambda, SageMaker, S3, CloudWatch \u0219i RDS, care au jucat un rol esen\u021bial \u00een furnizarea resurselor necesare pentru antrenarea \u0219i implementarea modelelor de inteligen\u021b\u0103 artificial\u0103.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">AWS Lambda a fost utilizat pentru gestionarea eficient\u0103 a instan\u021belor \u0219i servirea aplica\u021biilor, \u00een timp ce AWS SageMaker a facilitat construirea unor conducte robuste de \u00eenv\u0103\u021bare automat\u0103. Solu\u021biile de stocare precum S3, CloudWatch \u0219i RDS au fost esen\u021biale pentru stocarea seturilor de date \u0219i a jurnalelor cruciale pentru func\u021bionarea modelelor de \u00eenv\u0103\u021bare automat\u0103 \u0219i a re\u021belelor neuronale.<\/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\/ro\/blog\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\/aws-cloud-infrastructure\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/AWS-cloud-infrastructure.png?fit=800%2C490&amp;ssl=1\" data-orig-size=\"800,490\" data-comments-opened=\"1\" data-image-title=\"AWS cloud infrastructure\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/AWS-cloud-infrastructure.png?fit=800%2C490&amp;ssl=1\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/AWS-cloud-infrastructure.png?resize=800%2C490&#038;ssl=1\" alt=\"Infrastructura cloud AWS\" class=\"wp-image-10627\" srcset=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/AWS-cloud-infrastructure.png?w=800&amp;ssl=1 800w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/AWS-cloud-infrastructure.png?resize=300%2C184&amp;ssl=1 300w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/AWS-cloud-infrastructure.png?resize=768%2C470&amp;ssl=1 768w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/><figcaption class=\"wp-element-caption\">Infrastructura cloud AWS<\/figcaption><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\">Prin urmare, aceast\u0103 infrastructur\u0103 a suportat capacit\u0103\u021bile de procesare a datelor \u00een timp real oferite de re\u021beaua 5G.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-5g-network-latency\">Laten\u021ba re\u021belei 5G<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Re\u021belele 5G au fost proiectate pentru a ob\u021bine o laten\u021b\u0103 extrem de sc\u0103zut\u0103, de obicei \u00eentre 1 \u0219i 10 milisecunde. Aceast\u0103 laten\u021b\u0103 reflect\u0103 timpul necesar pentru ca datele s\u0103 circule \u00eentre dispozitivele mobile \u0219i serverele AWS prin intermediul re\u021belei 5G. Capacit\u0103\u021bile de procesare specifice dispozitivului, cum ar fi viteza de captare \u0219i procesare a fotografiilor pe smartphone-uri cu procesoare de \u00eenalt\u0103 performan\u021b\u0103, au influen\u021bat, de asemenea, laten\u021ba.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Vitezele de \u00eenc\u0103rcare a datelor \u00een re\u021beaua 5G \u0219i dimensiunea fotografiei au influen\u021bat timpii de transfer a datelor c\u0103tre AWS. AWS a contribuit \u00een continuare la laten\u021b\u0103 prin timpii de procesare pentru sarcini precum detectarea \u0219i segmentarea bazate pe re\u021bele neuronale, care au variat \u00een func\u021bie de complexitatea algoritmului \u0219i de eficien\u021ba serviciilor AWS. Dup\u0103 procesare, rezultatele au fost desc\u0103rcate \u00eenapoi pe dispozitivele mobile, influen\u021bate de viteza de desc\u0103rcare a 5G \u0219i de dimensiunea datelor rezultate.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-plant-recognition-using-ai\">Recunoa\u0219terea plantelor folosind inteligen\u021ba artificial\u0103<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">\u00cen domeniul recunoa\u0219terii plantelor, procesele bazate pe inteligen\u021b\u0103 artificial\u0103 au implicat crearea unei baze de date cuprinz\u0103toare de imagini ale plantelor pentru antrenarea algoritmilor baza\u021bi pe re\u021bele neuronale. Ace\u0219ti algoritmi au fost antrena\u021bi s\u0103 disting\u0103 speciile de sfecl\u0103 de zah\u0103r de alte plante prin recunoa\u0219terea caracteristicilor specifice acelui tip de plant\u0103, cum ar fi forma frunzelor, culorile florilor 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\/ro\/blog\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\/phenological-development-of-sugar-beet-plants\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Phenological-development-of-sugar-beet-plants.jpg?fit=2048%2C667&amp;ssl=1\" data-orig-size=\"2048,667\" data-comments-opened=\"1\" data-image-title=\"Phenological development of sugar beet plants\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Phenological-development-of-sugar-beet-plants.jpg?fit=1024%2C334&amp;ssl=1\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Phenological-development-of-sugar-beet-plants.jpg?resize=810%2C264&#038;ssl=1\" alt=\"Dezvoltarea fenologic\u0103 a plantelor de sfecl\u0103 de zah\u0103r\" 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\">Dezvoltarea fenologic\u0103 a plantelor de sfecl\u0103 de zah\u0103r. Sursa: <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\">Aici, prin recunoa\u0219terea plantelor \u00een\u021belegem sarcina de detectare a buruienilor \u0219i segmentare a plantelor de sfecl\u0103 de zah\u0103r.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Detectarea buruienilor<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Pentru detectarea buruienilor, proiectul a utilizat MobileNet-v3, care a fost antrenat cu augment\u0103ri extinse de date \u0219i e\u0219antionare ponderat\u0103. Acest model a atins o precizie impresionant\u0103 de 0,984 \u0219i o ASC de 0,998.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Segmentarea sfeclei de zah\u0103r<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Pentru sarcinile de segmentare, s-au folosit modele precum YOLACT, ResNeSt, SOLO \u0219i U-net pentru a delimita cu precizie mostrele individuale de sfecl\u0103 de zah\u0103r \u00een cadrul imaginilor. Apoi, a fost ales cel mai eficient model pe baza diferitelor criterii: vitez\u0103, timp de inferen\u021b\u0103 etc. Datele pentru segmentare au provenit din imagini RGB capturate cu drone, care au fost redimensionate \u0219i adnotate \u00een scopuri de antrenament \u0219i validare.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Sarcinile de segmentare au implicat crearea de m\u0103\u0219ti care delimitau cu precizie limitele plantelor. Aceast\u0103 metod\u0103 a redus eforturile umane de adnotare, optimiz\u00e2nd \u00een acela\u0219i timp eficien\u021ba. Prin prioritizarea etichet\u0103rii probelor dificile, performan\u021ba modelului a fost \u00eembun\u0103t\u0103\u021bit\u0103 semnificativ. Strategiile iterative de reantrenare \u0219i e\u0219antionare incert\u0103 s-au dovedit eficiente, ating\u00e2nd rate de precizie a segment\u0103rii care dep\u0103\u0219esc 98% \u00een diferite stadii de cre\u0219tere.<\/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\/ro\/blog\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\/example-of-input-output-of-segmentation\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Example-of-input-output-of-segmentation.png?fit=355%2C146&amp;ssl=1\" data-orig-size=\"355,146\" data-comments-opened=\"1\" data-image-title=\"Example of input-output of segmentation\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Example-of-input-output-of-segmentation.png?fit=355%2C146&amp;ssl=1\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Example-of-input-output-of-segmentation.png?resize=355%2C146&#038;ssl=1\" alt=\"Exemplu de segmentare input-output\" 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\">Exemplu de segmentare input-output<\/figcaption><\/figure>\n<\/div>\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Evaluarea modelului<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Modelul a fost antrenat cu augment\u0103ri riguroase ale datelor. Modelul a fost evaluat folosind diferite metrici, inclusiv Intersection over Union (IoU). Analiza inferen\u021bei pentru modelul construit, efectuat\u0103 pe un subset din setul de date \u2018plant reseats v2\u2019, a demonstrat o precizie de 81%. Timpul de inferen\u021b\u0103 a fost de aproximativ 320 de milisecunde pentru a fi calculat dup\u0103 o perioad\u0103 de ini\u021bializare de 7 secunde, necesar\u0103 o singur\u0103 dat\u0103 pe sesiune.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u00cen monitorizarea plantelor bazat\u0103 pe inteligen\u021b\u0103 artificial\u0103 (IA), camerele \u0219i senzorii au captat date vitale despre plante, analizate prin \u00eenv\u0103\u021bare automat\u0103 \u0219i algoritmi de inteligen\u021b\u0103 artificial\u0103. Aceast\u0103 analiz\u0103 a jucat un rol crucial \u00een evaluarea s\u0103n\u0103t\u0103\u021bii plantelor, identificarea stresului, a bolilor sau a altor factori care afecteaz\u0103 cre\u0219terea.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Aplica\u021biile s-au extins de la optimizarea productivit\u0103\u021bii agricole la monitorizarea ecosistemelor naturale precum p\u0103durile, sprijinirea eforturilor de conservare \u0219i \u00eembun\u0103t\u0103\u021birea \u00een\u021belegerii impactului asupra mediului.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-object-detection-in-plant-monitoring\">Detectarea obiectelor \u00een monitorizarea instala\u021biilor<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Urm\u0103toarea faz\u0103 dup\u0103 segmentarea plantelor de sfecl\u0103 de zah\u0103r este detectarea obiectelor, menit\u0103 s\u0103 \u00een\u021beleag\u0103 specificul fiec\u0103rei plante \u00een ceea ce prive\u0219te s\u0103n\u0103tatea, cre\u0219terea \u0219i al\u021bi factori. Pentru detectarea obiectelor \u00een monitorizarea plantelor, au fost utilizate modele avansate precum YOLOv4, MobileNetV2 \u0219i VGG-19 cu mecanisme de aten\u021bie. Aceste modele au analizat imagini segmentate ale sfeclei de zah\u0103r pentru a detecta zone specifice de stres \u0219i boli, permi\u021b\u00e2nd interven\u021bii precise \u0219i \u021bintite.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Proiectul a atins etape semnificative \u00een detectarea bolilor, antren\u00e2nd modelele ResNet-18 \u0219i ResNet-34 pre-antrenate pe ImageNet. Aceste modele au demonstrat o precizie impresionant\u0103 de 0,88 \u00een identificarea bolilor care afecteaz\u0103 plantele de sfecl\u0103 de zah\u0103r, cu o Arie de Sub Curba ROC (AUC) de 0,898. Modelele au demonstrat o \u00eencredere ridicat\u0103 \u00een predic\u021bie, disting\u00e2nd cu precizie \u00eentre plantele bolnave \u0219i cele s\u0103n\u0103toase.<\/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\/ro\/blog\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\/example-of-input-output-of-object-detection\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Example-of-input-output-of-object-detection.png?fit=414%2C194&amp;ssl=1\" data-orig-size=\"414,194\" data-comments-opened=\"1\" data-image-title=\"Example of input-output of object detection\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Example-of-input-output-of-object-detection.png?fit=414%2C194&amp;ssl=1\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Example-of-input-output-of-object-detection.png?resize=414%2C194&#038;ssl=1\" alt=\"Exemplu de intrare-ie\u0219ire a detect\u0103rii obiectelor\" 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\">Exemplu de intrare-ie\u0219ire a detect\u0103rii obiectelor<\/figcaption><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\">Proiectul a utilizat o abordare sistematic\u0103 pentru detectarea bolilor, segment\u00e2nd imaginile \u00een zone standardizate. Aceste zone au fost adnotate meticulos folosind instrumente interactive pentru a identifica zonele afectate de boli. Detectarea obiectelor a \u00eembun\u0103t\u0103\u021bit \u0219i mai mult precizia prin conturarea unor c\u0103su\u021be de delimitare \u00een jurul plantelor, facilit\u00e2nd monitorizarea precis\u0103 a s\u0103n\u0103t\u0103\u021bii plantelor.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-plant-production-prediction\">Predic\u021bia produc\u021biei vegetale<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">\u00cen domeniul predic\u021biei produc\u021biei vegetale, modelele de inteligen\u021b\u0103 artificial\u0103 au utilizat date de mediu, cum ar fi condi\u021biile meteorologice \u0219i parametrii solului, pentru a prognoza randamentele culturilor. Au fost utilizate modele de regresie precum P\u0103durea de Izolare, Regresia Liniar\u0103 \u0219i Regresia Ridge.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Aceste modele au integrat caracteristici numerice extrase din regiunile delimitatoare \u00eempreun\u0103 cu date despre sol pentru a optimiza aplicarea \u00eengr\u0103\u0219\u0103mintelor.<\/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\/ro\/blog\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\/sugar-beet-on-test-field\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Sugar-beet-on-test-field.png?fit=1542%2C2048&amp;ssl=1\" data-orig-size=\"1542,2048\" data-comments-opened=\"1\" data-image-title=\"Sugar beet on test field\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Sugar-beet-on-test-field.png?fit=771%2C1024&amp;ssl=1\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Sugar-beet-on-test-field.png?resize=810%2C1076&#038;ssl=1\" alt=\"Sfecl\u0103 de zah\u0103r pe c\u00e2mpul de testare\" 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\">Sfecl\u0103 de zah\u0103r pe c\u00e2mpul de testare<\/figcaption><\/figure>\n<\/div>\n\n\n<h2 class=\"wp-block-heading\" id=\"h-model-deployment-considerations\">Considera\u021bii privind implementarea modelului<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Strategiile de implementare pentru modelele dezvoltate au fost evaluate at\u00e2t pentru dispozitivele edge, c\u00e2t \u0219i pentru platformele cloud. Implementarea modelelor pe dispozitive edge a oferit avantaje precum costuri reduse \u0219i laten\u021b\u0103 mai mic\u0103.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Totu\u0219i, aceast\u0103 abordare ar putea compromite precizia poten\u021bial\u0103 din cauza constr\u00e2ngerilor hardware. Pe de alt\u0103 parte, implementarea \u00een cloud a oferit timpi de inferen\u021b\u0103 mai rapizi folosind GPU-uri de \u00eenalt\u0103 performan\u021b\u0103, dar ar putea genera costuri suplimentare \u0219i se baza pe conectivitatea la internet, ceea ce ar putea introduce laten\u021b\u0103 \u00een comunicare.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-comparative-analysis-with-5g-network\">Analiz\u0103 comparativ\u0103 cu re\u021beaua 5G<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">O analiz\u0103 comparativ\u0103 a demonstrat c\u0103 utilizarea unei re\u021bele 5G a \u00eembun\u0103t\u0103\u021bit semnificativ segmentarea sfeclei de zah\u0103r \u00een compara\u021bie cu configura\u021biile tradi\u021bionale 4G\/WiFi. Aceast\u0103 \u00eembun\u0103t\u0103\u021bire a fost eviden\u021biat\u0103 de reducerea timpilor medii de configurare \u0219i de conectare la re\u021bea, eviden\u021biind c\u00e2\u0219tigurile de eficien\u021b\u0103 ob\u021binute prin tehnologia 5G.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Procesul de preg\u0103tire a datelor<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Procesul de preg\u0103tire a datelor a implicat colectarea seturilor de date ale plantelor s\u0103n\u0103toase \u0219i bolnave, detectarea buruienilor, identificarea etapelor de cre\u0219tere \u0219i extragerea imaginilor din videoclipuri brute 4K. Tehnici precum egalizarea histogramei, filtrarea imaginilor \u0219i transformarea spa\u021biului de culoare HSV au fost utilizate pentru a preg\u0103ti datele pentru analiz\u0103.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Au fost colectate probe de frunze s\u0103n\u0103toase de sfecl\u0103 de zah\u0103r \u0219i probe bolnave, cum ar fi frunzele de porumb cu p\u0103tare cenu\u0219ie a frunzelor. Extragerea caracteristicilor bolii a implicat separarea frunzei de fundal, redimensionarea, transformarea \u0219i \u00eembinarea imaginilor pentru a crea probe realiste pentru analiz\u0103.<\/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\/ro\/blog\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\/annotation-process-for-segmentation\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Annotation-process-for-segmentation.png?fit=2048%2C505&amp;ssl=1\" data-orig-size=\"2048,505\" data-comments-opened=\"1\" data-image-title=\"Annotation process for segmentation\" data-image-description=\"\" data-image-caption=\"&lt;p&gt;Caption 3&lt;\/p&gt;\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Annotation-process-for-segmentation.png?fit=1024%2C253&amp;ssl=1\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Annotation-process-for-segmentation.png?resize=810%2C200&#038;ssl=1\" alt=\"Procesul de adnotare pentru segmentare\" 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\">Procesul de adnotare pentru segmentare<\/figcaption><\/figure>\n<\/div>\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Bucla de \u00eenv\u0103\u021bare activ\u0103<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">O bucl\u0103 de \u00eenv\u0103\u021bare activ\u0103 a fost ini\u021biat\u0103 cu date neetichetate, utilizate pentru antrenarea modelelor de detec\u021bie. Aceste modele au generat interog\u0103ri de adnotare care au fost abordate de adnotatori umani, rafin\u00e2nd continuu acurate\u021bea modelului prin cicluri iterative de antrenament \u0219i adnotare.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Adnotarea datelor prin intermediul modelului de funda\u021bie multimodal<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Abord\u00e2nd provocarea reprezentat\u0103 de datele etichetate limitate, proiectul a valorificat modele de funda\u021bie robuste pentru a genera adnot\u0103ri bazate pe date concrete. \u00cen special, CLIP, un model bazat pe transformatoare dezvoltat de OpenAI, antrenat pe un set vast de date de peste 400 de milioane de perechi imagine-text, a jucat un rol esen\u021bial.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Utiliz\u00e2nd Vision Transformers ca baz\u0103, CLIP a ob\u021binut o precizie remarcabil\u0103 95% pe seturile de validare, clasific\u00e2nd eficient imaginile \u00een clase distincte, cum ar fi sfecla de zah\u0103r \u0219i buruienile, cu o precizie ridicat\u0103.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Tehnologia dronelor pentru colectarea datelor<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Una dintre tehnologiile critice utilizate \u00een cadrul proiectului a fost utilizarea dronelor echipate cu camere RGB care au captat videoclipuri 4K. Aceste drone au furnizat imagini detaliate (rezolu\u021bie 3840\u00d72160) pentru analiz\u0103.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Preprocesarea acestor imagini a sporit semnificativ acurate\u021bea modelului, cu \u00eembun\u0103t\u0103\u021biri notabile observate \u00een modele precum VGGNet (+38.52%), ResNet50 (+21.14%), DenseNet121 (+7.53%) \u0219i MobileNet (+6.6%).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Tehnici precum egalizarea histogramei au fost utilizate pentru a \u00eembun\u0103t\u0103\u021bi contrastul imaginii, \u00een timp ce transformarea \u00een spa\u021biul de culoare HSV a ajutat la accentuarea zonelor plantelor \u0219i la eviden\u021bierea caracteristicilor relevante.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Generarea de date sintetice<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Pentru a aborda provocarea reprezentat\u0103 de datele limitate de imagini, au fost generate seturi de date sintetice prin \u00eenv\u0103\u021bare automat\u0103 \u0219i inteligen\u021b\u0103 artificial\u0103. Colectarea datelor a fost efectuat\u0103 folosind drone care zburau la \u00een\u0103l\u021bimi cuprinse \u00eentre 1 m \u0219i 4 m \u0219i viteze de 2 m\/s sau mai mult, utiliz\u00e2nd camere RGB.<\/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\/ro\/blog\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\/emulation-environment\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Emulation-environment.png?fit=2048%2C1542&amp;ssl=1\" data-orig-size=\"2048,1542\" data-comments-opened=\"1\" data-image-title=\"Emulation environment\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Emulation-environment.png?fit=1024%2C771&amp;ssl=1\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/Emulation-environment.png?resize=810%2C610&#038;ssl=1\" alt=\"Mediu de emulare\" 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\">Mediu de emulare<\/figcaption><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\">Alte vehicule, cum ar fi tractoarele, au fost folosite \u0219i pentru colectarea datelor. Aceast\u0103 generare de date sintetice s-a dovedit deosebit de benefic\u0103 pentru detectarea bolilor sfeclei de zah\u0103r.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-conclusion\">Concluzie<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Proiectul \u201cRe\u021belele 5G ca facilitator al \u00eenv\u0103\u021b\u0103rii \u00een timp real \u00een agricultura durabil\u0103\u201d a demonstrat cu succes modul \u00een care tehnologia 5G poate \u00eembun\u0103t\u0103\u021bi aspectele ecologice, economice \u0219i durabile ale cultiv\u0103rii sfeclei de zah\u0103r. Prin colaborarea cu HSHL \u0219i Pfeifer &amp; Langen, proiectul a integrat colectarea de date \u00een timp real \u0219i analiza bazat\u0103 pe inteligen\u021b\u0103 artificial\u0103, \u00eembun\u0103t\u0103\u021bind eficien\u021ba \u0219i reduc\u00e2nd vizitele inutile pe teren. <\/p>\n\n\n\n<p class=\"wp-block-paragraph\">O re\u021bea dedicat\u0103 de campus 5G a permis aplic\u0103ri precise de \u00eengr\u0103\u0219\u0103minte \u0219i produse de protec\u021bie a culturilor. Geopard Agriculture a jucat un rol crucial \u00een dezvoltarea scenariilor de detectare \u0219i monitorizare a plantelor \u0219i \u00een crearea unui prototip de sistem de \u00eenv\u0103\u021bare automat\u0103 pentru mediul agricol 5G. Succesul proiectului a subliniat importan\u021ba tehnologiilor avansate \u00een agricultura durabil\u0103, eviden\u021biind poten\u021bialul 5G de a stimula inova\u021bia \u0219i eficien\u021ba. <\/p>","protected":false},"excerpt":{"rendered":"<p>Suntem \u00eenc\u00e2nta\u021bi s\u0103 anun\u021b\u0103m finalizarea cu succes a proiectului \u201cRe\u021belele 5G ca facilitator pentru \u00eenv\u0103\u021barea \u00een timp real \u00een agricultura durabil\u0103\u201d, sus\u021binut par\u021bial de\u2026<\/p>","protected":false},"author":210157960,"featured_media":10639,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","_eb_attr":"","content-type":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_wpcom_ai_launchpad_first_post":false,"_jetpack_hide_featured_image":false,"_jetpack_featured_image_duplicate_dismissed":0,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"{title}\n\n{excerpt}\n\n{url}","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","enabled":false},"version":2},"_wpas_customize_per_network":false,"jetpack_post_was_ever_published":false},"categories":[1657,1367],"tags":[],"class_list":["post-10617","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-precision-farming","category-use-cases"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>5G-Enabled Real-time Learning in Sustainable Farming: A Study on Sugar Beet - GeoPard<\/title>\n<meta name=\"description\" content=\"Successful completion of &quot;5G Networks as an Enabler for Real-time Learning in Sustainable Farming&quot; project, supported by State of North Rhine-Westphalia.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/geopard.tech\/ro\/blog\/5g-a-permis-invatarea-in-timp-real-in-agricultura-durabila-un-studiu-privind-productia-de-sfecla-de-zahar\/\" \/>\n<meta property=\"og:locale\" content=\"ro_RO\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"5G-Enabled Real-time Learning in Sustainable Farming: A Study on Sugar Beet - GeoPard\" \/>\n<meta property=\"og:description\" content=\"Successful completion of &quot;5G Networks as an Enabler for Real-time Learning in Sustainable Farming&quot; project, supported by State of North Rhine-Westphalia.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/geopard.tech\/ro\/blog\/5g-a-permis-invatarea-in-timp-real-in-agricultura-durabila-un-studiu-privind-productia-de-sfecla-de-zahar\/\" \/>\n<meta property=\"og:site_name\" content=\"GeoPard - Precision agriculture Mapping software\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/geopardAgriculture\/\" \/>\n<meta property=\"article:published_time\" content=\"2024-07-13T18:50:45+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-07-13T18:50:51+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/geopard.tech\/wp-content\/uploads\/2024\/07\/5G-Enabled-Real-time-Learning-in-Sustainable-Farming-A-Study-on-Sugar-Beet-1024x576.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1024\" \/>\n\t<meta property=\"og:image:height\" content=\"576\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"dementievgeopard\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@geopardagri\" \/>\n<meta name=\"twitter:site\" content=\"@geopardagri\" \/>\n<meta name=\"twitter:label1\" content=\"Scris de\" \/>\n\t<meta name=\"twitter:data1\" content=\"dementievgeopard\" \/>\n\t<meta name=\"twitter:label2\" content=\"Timp estimat pentru citire\" \/>\n\t<meta name=\"twitter:data2\" content=\"11 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/geopard.tech\\\/blog\\\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/geopard.tech\\\/blog\\\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\\\/\"},\"author\":{\"name\":\"dementievgeopard\",\"@id\":\"https:\\\/\\\/geopard.tech\\\/#\\\/schema\\\/person\\\/dd217733c742620adc57befbbcd84a8a\"},\"headline\":\"5G-Enabled Real-time Learning in Sustainable Farming: A Study on Sugar Beet\",\"datePublished\":\"2024-07-13T18:50:45+00:00\",\"dateModified\":\"2024-07-13T18:50:51+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/geopard.tech\\\/blog\\\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\\\/\"},\"wordCount\":2143,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\\\/\\\/geopard.tech\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/geopard.tech\\\/blog\\\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/i0.wp.com\\\/geopard.tech\\\/wp-content\\\/uploads\\\/2024\\\/07\\\/5G-Enabled-Real-time-Learning-in-Sustainable-Farming-A-Study-on-Sugar-Beet.png?fit=1920%2C1080&ssl=1\",\"articleSection\":[\"Precision Farming\",\"Use-cases\"],\"inLanguage\":\"ro-RO\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\\\/\\\/geopard.tech\\\/blog\\\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\\\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/geopard.tech\\\/blog\\\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\\\/\",\"url\":\"https:\\\/\\\/geopard.tech\\\/blog\\\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\\\/\",\"name\":\"5G-Enabled Real-time Learning in Sustainable Farming: A Study on Sugar Beet - GeoPard\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/geopard.tech\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/geopard.tech\\\/blog\\\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/geopard.tech\\\/blog\\\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/i0.wp.com\\\/geopard.tech\\\/wp-content\\\/uploads\\\/2024\\\/07\\\/5G-Enabled-Real-time-Learning-in-Sustainable-Farming-A-Study-on-Sugar-Beet.png?fit=1920%2C1080&ssl=1\",\"datePublished\":\"2024-07-13T18:50:45+00:00\",\"dateModified\":\"2024-07-13T18:50:51+00:00\",\"description\":\"Successful completion of \\\"5G Networks as an Enabler for Real-time Learning in Sustainable Farming\\\" project, supported by State of North Rhine-Westphalia.\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/geopard.tech\\\/blog\\\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\\\/#breadcrumb\"},\"inLanguage\":\"ro-RO\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/geopard.tech\\\/blog\\\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"ro-RO\",\"@id\":\"https:\\\/\\\/geopard.tech\\\/blog\\\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\\\/#primaryimage\",\"url\":\"https:\\\/\\\/i0.wp.com\\\/geopard.tech\\\/wp-content\\\/uploads\\\/2024\\\/07\\\/5G-Enabled-Real-time-Learning-in-Sustainable-Farming-A-Study-on-Sugar-Beet.png?fit=1920%2C1080&ssl=1\",\"contentUrl\":\"https:\\\/\\\/i0.wp.com\\\/geopard.tech\\\/wp-content\\\/uploads\\\/2024\\\/07\\\/5G-Enabled-Real-time-Learning-in-Sustainable-Farming-A-Study-on-Sugar-Beet.png?fit=1920%2C1080&ssl=1\",\"width\":1920,\"height\":1080,\"caption\":\"Caption 2\"},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/geopard.tech\\\/blog\\\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/geopard.tech\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"5G-Enabled Real-time Learning in Sustainable Farming: A Study on Sugar Beet\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/geopard.tech\\\/#website\",\"url\":\"https:\\\/\\\/geopard.tech\\\/\",\"name\":\"GeoPard - Precision agriculture software\",\"description\":\"Precision agriculture Mapping software\",\"publisher\":{\"@id\":\"https:\\\/\\\/geopard.tech\\\/#organization\"},\"alternateName\":\"GeoPard\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/geopard.tech\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"ro-RO\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/geopard.tech\\\/#organization\",\"name\":\"GeoPard Agriculture\",\"alternateName\":\"GeoPard\",\"url\":\"https:\\\/\\\/geopard.tech\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"ro-RO\",\"@id\":\"https:\\\/\\\/geopard.tech\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/i0.wp.com\\\/geopard.tech\\\/wp-content\\\/uploads\\\/2026\\\/07\\\/geopard_logo_transparentbackground.png?fit=512%2C68&ssl=1\",\"contentUrl\":\"https:\\\/\\\/i0.wp.com\\\/geopard.tech\\\/wp-content\\\/uploads\\\/2026\\\/07\\\/geopard_logo_transparentbackground.png?fit=512%2C68&ssl=1\",\"width\":512,\"height\":68,\"caption\":\"GeoPard Agriculture\"},\"image\":{\"@id\":\"https:\\\/\\\/geopard.tech\\\/#\\\/schema\\\/logo\\\/image\\\/\"},\"sameAs\":[\"https:\\\/\\\/www.facebook.com\\\/geopardAgriculture\\\/\",\"https:\\\/\\\/x.com\\\/geopardagri\",\"https:\\\/\\\/www.linkedin.com\\\/company\\\/geopard-agriculture\\\/\",\"https:\\\/\\\/www.instagram.com\\\/geopardagriculture\\\/\",\"https:\\\/\\\/www.youtube.com\\\/channel\\\/UCiaPGLAhRPNh-s85dXdC-Sw\",\"https:\\\/\\\/www.g2.com\\\/products\\\/geopard-agriculture-precision-farming-software\\\/reviews\"]},{\"@type\":\"Person\",\"@id\":\"https:\\\/\\\/geopard.tech\\\/#\\\/schema\\\/person\\\/dd217733c742620adc57befbbcd84a8a\",\"name\":\"dementievgeopard\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"ro-RO\",\"@id\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/7c09adac9c20b00454199df5a41ea67c812a6c97e948bf109899836ddde31354?s=96&d=identicon&r=g\",\"url\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/7c09adac9c20b00454199df5a41ea67c812a6c97e948bf109899836ddde31354?s=96&d=identicon&r=g\",\"contentUrl\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/7c09adac9c20b00454199df5a41ea67c812a6c97e948bf109899836ddde31354?s=96&d=identicon&r=g\",\"caption\":\"dementievgeopard\"}}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"\u00cenv\u0103\u021bare \u00een timp real bazat\u0103 pe 5G \u00een agricultura durabil\u0103: Un studiu asupra sfeclei de zah\u0103r - GeoPard","description":"Finalizarea cu succes a proiectului \"Re\u021belele 5G ca facilitator al \u00eenv\u0103\u021b\u0103rii \u00een timp real \u00een agricultura durabil\u0103\", sus\u021binut de statul Renania de Nord-Westfalia.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/geopard.tech\/ro\/blog\/5g-a-permis-invatarea-in-timp-real-in-agricultura-durabila-un-studiu-privind-productia-de-sfecla-de-zahar\/","og_locale":"ro_RO","og_type":"article","og_title":"5G-Enabled Real-time Learning in Sustainable Farming: A Study on Sugar Beet - GeoPard","og_description":"Successful completion of \"5G Networks as an Enabler for Real-time Learning in Sustainable Farming\" project, supported by State of North Rhine-Westphalia.","og_url":"https:\/\/geopard.tech\/ro\/blog\/5g-a-permis-invatarea-in-timp-real-in-agricultura-durabila-un-studiu-privind-productia-de-sfecla-de-zahar\/","og_site_name":"GeoPard - Precision agriculture Mapping software","article_publisher":"https:\/\/www.facebook.com\/geopardAgriculture\/","article_published_time":"2024-07-13T18:50:45+00:00","article_modified_time":"2024-07-13T18:50:51+00:00","og_image":[{"width":1024,"height":576,"url":"https:\/\/geopard.tech\/wp-content\/uploads\/2024\/07\/5G-Enabled-Real-time-Learning-in-Sustainable-Farming-A-Study-on-Sugar-Beet-1024x576.png","type":"image\/png"}],"author":"dementievgeopard","twitter_card":"summary_large_image","twitter_creator":"@geopardagri","twitter_site":"@geopardagri","twitter_misc":{"Scris de":"dementievgeopard","Timp estimat pentru citire":"11 minute"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/geopard.tech\/blog\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\/#article","isPartOf":{"@id":"https:\/\/geopard.tech\/blog\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\/"},"author":{"name":"dementievgeopard","@id":"https:\/\/geopard.tech\/#\/schema\/person\/dd217733c742620adc57befbbcd84a8a"},"headline":"5G-Enabled Real-time Learning in Sustainable Farming: A Study on Sugar Beet","datePublished":"2024-07-13T18:50:45+00:00","dateModified":"2024-07-13T18:50:51+00:00","mainEntityOfPage":{"@id":"https:\/\/geopard.tech\/blog\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\/"},"wordCount":2143,"commentCount":0,"publisher":{"@id":"https:\/\/geopard.tech\/#organization"},"image":{"@id":"https:\/\/geopard.tech\/blog\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\/#primaryimage"},"thumbnailUrl":"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/5G-Enabled-Real-time-Learning-in-Sustainable-Farming-A-Study-on-Sugar-Beet.png?fit=1920%2C1080&ssl=1","articleSection":["Precision Farming","Use-cases"],"inLanguage":"ro-RO","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/geopard.tech\/blog\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\/#respond"]}]},{"@type":"WebPage","@id":"https:\/\/geopard.tech\/blog\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\/","url":"https:\/\/geopard.tech\/blog\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\/","name":"\u00cenv\u0103\u021bare \u00een timp real bazat\u0103 pe 5G \u00een agricultura durabil\u0103: Un studiu asupra sfeclei de zah\u0103r - GeoPard","isPartOf":{"@id":"https:\/\/geopard.tech\/#website"},"primaryImageOfPage":{"@id":"https:\/\/geopard.tech\/blog\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\/#primaryimage"},"image":{"@id":"https:\/\/geopard.tech\/blog\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\/#primaryimage"},"thumbnailUrl":"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/5G-Enabled-Real-time-Learning-in-Sustainable-Farming-A-Study-on-Sugar-Beet.png?fit=1920%2C1080&ssl=1","datePublished":"2024-07-13T18:50:45+00:00","dateModified":"2024-07-13T18:50:51+00:00","description":"Finalizarea cu succes a proiectului \"Re\u021belele 5G ca facilitator al \u00eenv\u0103\u021b\u0103rii \u00een timp real \u00een agricultura durabil\u0103\", sus\u021binut de statul Renania de Nord-Westfalia.","breadcrumb":{"@id":"https:\/\/geopard.tech\/blog\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\/#breadcrumb"},"inLanguage":"ro-RO","potentialAction":[{"@type":"ReadAction","target":["https:\/\/geopard.tech\/blog\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\/"]}]},{"@type":"ImageObject","inLanguage":"ro-RO","@id":"https:\/\/geopard.tech\/blog\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\/#primaryimage","url":"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/5G-Enabled-Real-time-Learning-in-Sustainable-Farming-A-Study-on-Sugar-Beet.png?fit=1920%2C1080&ssl=1","contentUrl":"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/5G-Enabled-Real-time-Learning-in-Sustainable-Farming-A-Study-on-Sugar-Beet.png?fit=1920%2C1080&ssl=1","width":1920,"height":1080,"caption":"Caption 2"},{"@type":"BreadcrumbList","@id":"https:\/\/geopard.tech\/blog\/5g-enabled-real-time-learning-in-sustainable-farming-a-study-on-sugar-beet-production\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/geopard.tech\/"},{"@type":"ListItem","position":2,"name":"5G-Enabled Real-time Learning in Sustainable Farming: A Study on Sugar Beet"}]},{"@type":"WebSite","@id":"https:\/\/geopard.tech\/#website","url":"https:\/\/geopard.tech\/","name":"GeoPard - Software pentru agricultur\u0103 de precizie","description":"Agricultur\u0103 de precizie Software de cartografiere","publisher":{"@id":"https:\/\/geopard.tech\/#organization"},"alternateName":"GeoPard","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/geopard.tech\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"ro-RO"},{"@type":"Organization","@id":"https:\/\/geopard.tech\/#organization","name":"GeoPard Agricultur\u0103","alternateName":"GeoPard","url":"https:\/\/geopard.tech\/","logo":{"@type":"ImageObject","inLanguage":"ro-RO","@id":"https:\/\/geopard.tech\/#\/schema\/logo\/image\/","url":"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2026\/07\/geopard_logo_transparentbackground.png?fit=512%2C68&ssl=1","contentUrl":"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2026\/07\/geopard_logo_transparentbackground.png?fit=512%2C68&ssl=1","width":512,"height":68,"caption":"GeoPard Agriculture"},"image":{"@id":"https:\/\/geopard.tech\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/geopardAgriculture\/","https:\/\/x.com\/geopardagri","https:\/\/www.linkedin.com\/company\/geopard-agriculture\/","https:\/\/www.instagram.com\/geopardagriculture\/","https:\/\/www.youtube.com\/channel\/UCiaPGLAhRPNh-s85dXdC-Sw","https:\/\/www.g2.com\/products\/geopard-agriculture-precision-farming-software\/reviews"]},{"@type":"Person","@id":"https:\/\/geopard.tech\/#\/schema\/person\/dd217733c742620adc57befbbcd84a8a","name":"dementievgeopard","image":{"@type":"ImageObject","inLanguage":"ro-RO","@id":"https:\/\/secure.gravatar.com\/avatar\/7c09adac9c20b00454199df5a41ea67c812a6c97e948bf109899836ddde31354?s=96&d=identicon&r=g","url":"https:\/\/secure.gravatar.com\/avatar\/7c09adac9c20b00454199df5a41ea67c812a6c97e948bf109899836ddde31354?s=96&d=identicon&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/7c09adac9c20b00454199df5a41ea67c812a6c97e948bf109899836ddde31354?s=96&d=identicon&r=g","caption":"dementievgeopard"}}]}},"jetpack_publicize_connections":[],"jetpack_likes_enabled":true,"jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/pdiCPa-2Lf","jetpack_featured_media_url":"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/07\/5G-Enabled-Real-time-Learning-in-Sustainable-Farming-A-Study-on-Sugar-Beet.png?fit=1920%2C1080&ssl=1","_links":{"self":[{"href":"https:\/\/geopard.tech\/ro\/wp-json\/wp\/v2\/posts\/10617","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/geopard.tech\/ro\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/geopard.tech\/ro\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/geopard.tech\/ro\/wp-json\/wp\/v2\/users\/210157960"}],"replies":[{"embeddable":true,"href":"https:\/\/geopard.tech\/ro\/wp-json\/wp\/v2\/comments?post=10617"}],"version-history":[{"count":0,"href":"https:\/\/geopard.tech\/ro\/wp-json\/wp\/v2\/posts\/10617\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/geopard.tech\/ro\/wp-json\/wp\/v2\/media\/10639"}],"wp:attachment":[{"href":"https:\/\/geopard.tech\/ro\/wp-json\/wp\/v2\/media?parent=10617"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/geopard.tech\/ro\/wp-json\/wp\/v2\/categories?post=10617"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/geopard.tech\/ro\/wp-json\/wp\/v2\/tags?post=10617"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}