{"id":11416,"date":"2025-03-30T21:39:19","date_gmt":"2025-03-30T19:39:19","guid":{"rendered":"https:\/\/geopard.tech\/?p=11416"},"modified":"2025-03-30T21:45:02","modified_gmt":"2025-03-30T19:45:02","slug":"kako-visokopropusno-fenotipiziranje-temeljeno-na-bespilotnim-sustavima-uas-transformira-moderno-oplemenjivanje-biljaka","status":"publish","type":"post","link":"https:\/\/geopard.tech\/hr\/blog\/how-uas-based-high-throughput-phenotyping-is-transforming-modern-plant-breeding\/","title":{"rendered":"Kako visokoproduktivna fenotipizacija temeljena na UAS-u transformira moderno oplemenjivanje biljaka"},"content":{"rendered":"<p>Predvi\u0111a se da \u0107e do 2050. godine globalna populacija dosegnuti 9,8 milijardi ljudi, \u0161to \u0107e udvostru\u010diti potra\u017enju za hranom. Me\u0111utim, \u0161irenje poljoprivrednog zemlji\u0161ta kako bi se zadovoljila ta potreba nije odr\u017eivo. Preko 501 TP3T novog obradivog zemlji\u0161ta stvorenog od 2000. godine zamijenilo je \u0161ume i prirodne ekosustave, pogor\u0161avaju\u0107i klimatske promjene i gubitak bioraznolikosti.<\/p>\n<p>Kako bi izbjegli ovu krizu, znanstvenici se okre\u0107u oplemenjivanju biljaka - znanosti o razvoju usjeva s ve\u0107im prinosima, otporno\u0161\u0107u na bolesti i klimatske promjene. Me\u0111utim, tradicionalne metode oplemenjivanja prespore su da bi pratile hitnost problema.<\/p>\n<p>Tu dronovi i umjetna inteligencija (AI) stupaju na scenu kao revolucionarni igra\u010di, nude\u0107i br\u017ei i pametniji na\u010din uzgoja boljih usjeva.<\/p>\n<h2>Za\u0161to tradicionalno oplemenjivanje biljaka zaostaje<\/h2>\n<p>Oplemenjivanje biljaka oslanja se na odabir biljaka s po\u017eeljnim osobinama, poput otpornosti na su\u0161u ili \u0161tetnike, i njihovo kri\u017eanje tijekom vi\u0161e generacija. Najve\u0107e usko grlo u ovom procesu je fenotipizacija - ru\u010dno mjerenje karakteristika biljke poput visine, zdravlja lista ili prinosa.<\/p>\n<p>Na primjer, mjerenje visine biljaka na polju od 3000 parcela mo\u017ee trajati tjednima, a ljudske pogre\u0161ke uzrokuju nedosljednosti i do 20%. Osim toga, prinosi usjeva pobolj\u0161avaju se za samo 0,5\u20131% godi\u0161nje, \u0161to je daleko ispod stope rasta od 2,9% potrebne za zadovoljavanje potreba 2050. godine.<\/p>\n<p>Kukuruz, osnovna kultura za milijarde ljudi, ilustrira ovo usporavanje: njegov godi\u0161nji rast prinosa pao je s 2,21 TP3T u 1960-ima na 1,331 TP3T danas. Kako bi premostili taj jaz, znanstvenicima su potrebni alati koji automatiziraju prikupljanje podataka, smanjuju pogre\u0161ke i ubrzavaju dono\u0161enje odluka.<\/p>\n<h2>Kako tehnologija dronova transformira uzgoj biljaka<\/h2>\n<p>Dronovi ili bespilotne letjelice (UAS), opremljene naprednim senzorima i umjetnom inteligencijom, revolucioniraju poljoprivredu. Ovi ure\u0111aji mogu letjeti iznad polja i prikupljati precizne podatke o tisu\u0107ama biljaka u minutama, proces poznat kao visokopropusno fenotipiranje (HTP).<\/p>\n<p>Za razliku od tradicionalnih metoda, dronovi prikupljaju podatke s cijelih polja, eliminiraju\u0107i pristranost uzorkovanja. Koriste specijalizirane senzore za mjerenje svega, od visine biljaka do razine vodnog stresa.<\/p>\n<p>Na primjer, multispektralni senzori detektiraju blisko infracrveno svjetlo koje reflektira zdravo li\u0161\u0107e, dok termalne kamere identificiraju stres su\u0161e mjerenjem temperature kro\u0161nje.<\/p>\n<p>Automatizacijom prikupljanja podataka, dronovi smanjuju tro\u0161kove rada i ubrzavaju cikluse uzgoja, omogu\u0107uju\u0107i razvoj pobolj\u0161anih sorti usjeva u godinama umjesto desetlje\u0107ima.<\/p>\n<h2>Znanost iza senzora i prikupljanja podataka dronovima<\/h2>\n<p>Dronovi se oslanjaju na razne senzore za prikupljanje klju\u010dnih podataka o biljkama. RGB kamere, najpristupa\u010dnija opcija, hvataju vidljivu svjetlost za mjerenje pokrovnosti kro\u0161nje i visine biljke. U poljima \u0161e\u0107erne trske, ove kamere su postigle to\u010dnost od 64\u201369% u brojanju stabljika, zamijeniv\u0161i ru\u010dno brojanje sklono pogre\u0161kama.<\/p>\n<p>Multispektralni senzori idu dalje detektiranjem nevidljivih valnih duljina poput bliskog infracrvenog zra\u010denja, koje koreliraju s razinama klorofila i zdravljem biljaka. Na primjer, predvidjeli su toleranciju na su\u0161u kod \u0161e\u0107erne trske s to\u010dno\u0161\u0107u ve\u0107om od 80%.<\/p>\n<ul>\n<li><strong>RGB kamere<\/strong>: Snimanje crvenog, zelenog i plavog svjetla za stvaranje slika u boji.<\/li>\n<li><strong>Multispektralni senzori<\/strong>Detektiranje svjetlosti izvan vidljivog spektra (npr. bliskog infracrvenog zra\u010denja).<\/li>\n<li><strong>Termalni senzori<\/strong>Mjerenje topline koju emitiraju biljke.<\/li>\n<li><strong>LiDAR<\/strong>Koristi laserske impulse za stvaranje 3D mapa biljaka.<\/li>\n<li><strong>Hiperspektralni senzori<\/strong>Snimite vi\u0161e od 200 svjetlosnih valnih duljina za ultra detaljnu analizu.<\/li>\n<\/ul>\n<p>Termalni senzori detektiraju toplinske potpise, identificiraju\u0107i biljke pod stresom zbog vode koje se \u010dine toplijima od zdravih. Na poljima pamuka, termalni dronovi su uskladili mjerenja temperature s tla s pogre\u0161kom manjom od 5%.<\/p>\n<p>LiDAR senzori koriste laserske impulse za stvaranje 3D karata usjeva, mjere\u0107i biomasu i visinu s precizno\u0161\u0107u od 95% u pokusima s energetskom trskom. Najnapredniji alati, hiperspektralni senzori, analiziraju stotine svjetlosnih valnih duljina kako bi uo\u010dili nedostatke hranjivih tvari ili bolesti nevidljive golim okom.<\/p>\n<p>Ovi senzori pomogli su istra\u017eiva\u010dima da pove\u017eu 28 novih gena s odgo\u0111enim starenjem p\u0161enice, osobinom koja pove\u0107ava prinose.<\/p>\n<h2>Od leta do uvida: Kako dronovi analiziraju podatke o usjevima<\/h2>\n<p>Proces fenotipizacije dronovima zapo\u010dinje pa\u017eljivim planiranjem leta. Dronovi lete na nadmorskoj visini od 30 do 100 metara, snimaju\u0107i preklapaju\u0107e slike kako bi se osigurala potpuna pokrivenost. Polje od 10 hektara, na primjer, mo\u017ee se skenirati za 15 do 30 minuta.<\/p>\n<p>Nakon leta, softver poput Agisoft Metashapea spaja tisu\u0107e slika u detaljne karte koriste\u0107i Structure-from-Motion (SfM) - tehniku koja pretvara 2D fotografije u 3D modele. Ovi modeli omogu\u0107uju znanstvenicima da mjere osobine poput visine biljaka ili pokrovnosti kro\u0161nje jednim dodirom gumba.<\/p>\n<p>Algoritmi umjetne inteligencije zatim analiziraju podatke, predvi\u0111aju\u0107i prinose ili identificiraju\u0107i izbijanja bolesti. Na primjer, dronovi su skenirali 3132 parcele \u0161e\u0107erne trske za samo 7 sati - zadatak koji bi ru\u010dno trajao tri tjedna. Ova brzina i preciznost omogu\u0107uju uzgajiva\u010dima br\u017ee dono\u0161enje odluka, poput odbacivanja biljaka slabijeg u\u010dinka na po\u010detku sezone.<\/p>\n<h2>Klju\u010dne primjene dronova u modernoj poljoprivredi<\/h2>\n<p>Dronovi se koriste za rje\u0161avanje nekih od najve\u0107ih izazova u poljoprivredi. Jedna od glavnih primjena je izravno mjerenje osobina, gdje dronovi zamjenjuju ru\u010dni rad. U poljima kukuruza dronovi mjere visinu biljaka s to\u010dno\u0161\u0107u od 90%, smanjuju\u0107i pogre\u0161ke s 0,5 metara na 0,21 metar.<\/p>\n<p>Tako\u0111er prate pokrivenost kro\u0161njama, metriku koja pokazuje koliko dobro biljke zasjenjuju tlo kako bi suzbile korov. Uzgajiva\u010di energetske trske koristili su ove podatke kako bi identificirali sorte koje smanjuju rast korova za 40%.<\/p>\n<p>Jo\u0161 jedan napredak je prediktivni uzgoj, gdje modeli umjetne inteligencije koriste podatke dronova za predvi\u0111anje rezultata usjeva. Na primjer, multispektralne snimke predvidjele su prinose kukuruza s to\u010dno\u0161\u0107u od 80%, nadma\u0161uju\u0107i tradicionalno genomsko testiranje.<\/p>\n<p>Dronovi tako\u0111er poma\u017eu u otkrivanju gena, poma\u017eu\u0107i znanstvenicima da lociraju segmente DNK odgovorne za po\u017eeljne osobine. Kod p\u0161enice, dronovi su povezali zelenilo kro\u0161nje s 22 nova gena, potencijalno pove\u0107avaju\u0107i otpornost na su\u0161u.<\/p>\n<p>Osim toga, hiperspektralni senzori otkrivaju bolesti poput pozelenjivanja citrusa tjednima prije pojave simptoma, daju\u0107i poljoprivrednicima vremena za djelovanje.<\/p>\n<h2>Pove\u0107anje genetskih dobitaka preciznom tehnologijom<\/h2>\n<p>Genetska dobit - godi\u0161nje pobolj\u0161anje svojstava usjeva zbog oplemenjivanja - izra\u010dunava se pomo\u0107u jednostavne formule:<\/p>\n<p style=\"text-align: center;\"><strong>(Intenzitet selekcije \u00d7 Nasljednost \u00d7 Varijabilnost osobina) \u00f7 Vrijeme ciklusa oplemenjivanja.<\/strong><\/p>\n<p style=\"text-align: center;\">Genetski dobitak (\u0394G) izra\u010dunava se kao:<br \/>\n<strong>\u0394G = (i \u00d7 h\u00b2 \u00d7 \u03c3p) \/ L<\/strong><\/p>\n<p style=\"text-align: left;\">Gdje:<\/p>\n<ul>\n<li><strong>i<\/strong>\u00a0= Intenzitet selekcije (koliko su uzgajiva\u010di strogi).<\/li>\n<li><strong>h\u00b2<\/strong>\u00a0= Nasljednost (koliko se osobine prenosi s roditelja na potomstvo).<\/li>\n<li><strong>\u03c3p<\/strong>\u00a0= Varijabilnost osobina u populaciji.<\/li>\n<li><strong>L<\/strong>\u00a0= Vrijeme po ciklusu uzgoja.<\/li>\n<\/ul>\n<p><strong>Za\u0161to je to va\u017eno<\/strong>Dronovi pobolj\u0161avaju sve varijable:<\/p>\n<ol start=\"1\">\n<li><strong>i<\/strong>Skeniraj\u00a0<strong>10 puta vi\u0161e biljaka<\/strong>, \u0161to omogu\u0107uje stro\u017ei odabir.<\/li>\n<li><strong>h\u00b2<\/strong>Smanjiti pogre\u0161ke mjerenja, pobolj\u0161avaju\u0107i procjene heritabilnosti.<\/li>\n<li><strong>\u03c3p<\/strong>: Zabilje\u017eite suptilne varijacije osobina u cijelim poljima.<\/li>\n<li><strong>L<\/strong>Skratite vrijeme ciklusa od\u00a0<strong>5 godina do 2-3 godine<\/strong>\u00a0putem ranih predvi\u0111anja.<\/li>\n<\/ol>\n<p>Dronovi pobolj\u0161avaju svaki dio ove jednad\u017ebe. Skeniranjem cijelih polja omogu\u0107uju oplemenjiva\u010dima da odaberu najboljih 1% biljaka umjesto najboljih 10%, pove\u0107avaju\u0107i intenzitet selekcije. Tako\u0111er pobolj\u0161avaju procjene heritabilnosti smanjenjem pogre\u0161aka mjerenja.<\/p>\n<p>Na primjer, ru\u010dna procjena visine biljke uvodi varijabilnost 20%, dok dronovi to smanjuju na 5%. \u0160tovi\u0161e, dronovi bilje\u017ee suptilne varijacije osobina kod tisu\u0107a biljaka, maksimiziraju\u0107i varijabilnost osobina.<\/p>\n<p>Najva\u017enije je da skra\u0107uju cikluse uzgoja omogu\u0107uju\u0107i rana predvi\u0111anja. Uzgajiva\u010di \u0161e\u0107erne trske koji koriste dronove utrostru\u010dili su svoje genetske dobitke u usporedbi s tradicionalnim metodama, dokazuju\u0107i transformativni potencijal tehnologije.<\/p>\n<h2>Prevladavanje izazova i prihva\u0107anje budu\u0107nosti<\/h2>\n<p>Unato\u010d obe\u0107anjima, fenotipizacija temeljena na dronovima i dalje se suo\u010dava sa zna\u010dajnim izazovima. Visoka cijena naprednih senzora ostaje glavna prepreka - hiperspektralne kamere, na primjer, mogu prema\u0161iti $50.000, \u0161to ih \u010dini nedostupnima ve\u0107ini malih poljoprivrednika.<\/p>\n<p>Obrada ogromnih koli\u010dina prikupljenih podataka tako\u0111er zahtijeva zna\u010dajne resurse ra\u010dunalstva u oblaku, \u0161to pove\u0107ava tro\u0161kove. Platforme umjetne inteligencije poput AutoGIS-a automatiziraju analizu podataka, eliminiraju\u0107i potrebu za ru\u010dnim unosom.<\/p>\n<p>Istra\u017eiva\u010di tako\u0111er integriraju dronove sa senzorima tla i meteorolo\u0161kim stanicama, stvaraju\u0107i sustav pra\u0107enja u stvarnom vremenu koji upozorava poljoprivrednike na \u0161teto\u010dine ili su\u0161e. Ove inovacije utiru put novoj eri precizne poljoprivrede, gdje odluke temeljene na podacima zamjenjuju naga\u0111anja.<\/p>\n<h2>Zaklju\u010dak<\/h2>\n<p>Dronovi i umjetna inteligencija ne transformiraju samo uzgoj biljaka - oni redefiniraju odr\u017eivu poljoprivredu. Omogu\u0107avanjem br\u017eeg razvoja usjeva otpornih na su\u0161u i visokih prinosa, ove tehnologije mogle bi udvostru\u010diti proizvodnju hrane do 2050. bez \u0161irenja poljoprivrednog zemlji\u0161ta.<\/p>\n<p>To bi spasilo vi\u0161e od 100 milijuna hektara \u0161uma, \u0161to je ekvivalentno veli\u010dini Egipta, i smanjilo uglji\u010dni otisak poljoprivrede. Poljoprivrednici koji koriste podatke dronova ve\u0107 su smanjili potro\u0161nju vode i pesticida za do 30%, \u0161tite\u0107i ekosustave i smanjuju\u0107i tro\u0161kove.<\/p>\n<p>Kao \u0161to je jedan istra\u017eiva\u010d primijetio: \u201cVi\u0161e ne naga\u0111amo koje su biljke najbolje. Dronovi nam govore.\u201d S kontinuiranim inovacijama, ova fuzija biologije i tehnologije mogla bi osigurati sigurnost hrane za milijarde ljudi, a istovremeno za\u0161tititi na\u0161 planet.<\/p>\n<p><strong>Referenca<\/strong>: Khuimphukhieo, I. i da Silva, JA (2025). Visokopropusno fenotipiziranje (HTP) na polju temeljeno na bespilotnim zra\u010dnim sustavima (UAS) kao alatni set oplemenjiva\u010da biljaka: sveobuhvatan pregled. Smart Agricultural Technology, 100888.<\/p>","protected":false},"excerpt":{"rendered":"<p>Predvi\u0111a se da \u0107e do 2050. godine svjetska populacija dosegnuti 9,8 milijardi ljudi, \u0161to \u0107e udvostru\u010diti potra\u017enju za hranom. Me\u0111utim, \u0161irenje poljoprivrednog zemlji\u0161ta kako bi se zadovoljila ta potreba je\u2026<\/p>","protected":false},"author":210249433,"featured_media":11421,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","_eb_attr":"","_crdt_document":"","content-type":"","jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"_wpas_customize_per_network":false},"categories":[1377,1378],"tags":[],"class_list":["post-11416","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-crop-monitoring","category-remote-sensing"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.6 (Yoast SEO v27.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>How UAS-Based High-Throughput Phenotyping is Transforming Modern Plant Breeding - GeoPard Agriculture<\/title>\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\/hr\/blog\/kako-visokopropusno-fenotipiziranje-temeljeno-na-bespilotnim-sustavima-uas-transformira-moderno-oplemenjivanje-biljaka\/\" \/>\n<meta property=\"og:locale\" content=\"hr_HR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How UAS-Based High-Throughput Phenotyping is Transforming Modern Plant Breeding\" \/>\n<meta property=\"og:description\" content=\"By 2050, the global population is projected to reach 9.8 billion people, doubling the demand for food. 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