{"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":"ako-vysokovykonna-fenotypizacia-zalozena-na-uas-transformuje-moderne-slachtenie-rastlin","status":"publish","type":"post","link":"https:\/\/geopard.tech\/sk\/blog\/how-uas-based-high-throughput-phenotyping-is-transforming-modern-plant-breeding\/","title":{"rendered":"Ako vysokov\u00fdkonn\u00e1 fenotypiz\u00e1cia zalo\u017een\u00e1 na UAS transformuje modern\u00e9 \u0161\u013eachtenie rastl\u00edn"},"content":{"rendered":"<p>Predpoklad\u00e1 sa, \u017ee do roku 2050 dosiahne svetov\u00e1 popul\u00e1cia 9,8 miliardy \u013eud\u00ed, \u010d\u00edm sa zdvojn\u00e1sob\u00ed dopyt po potravin\u00e1ch. Roz\u0161irovanie po\u013enohospod\u00e1rskej p\u00f4dy na uspokojenie tejto potreby je v\u0161ak neudr\u017eate\u013en\u00e9. Viac ako 501 TP3 ton novej ornej p\u00f4dy vytvorenej od roku 2000 nahradilo lesy a prirodzen\u00e9 ekosyst\u00e9my, \u010d\u00edm sa zhor\u0161ila zmena kl\u00edmy a strata biodiverzity.<\/p>\n<p>Aby sa predi\u0161lo tejto kr\u00edze, vedci sa obracaj\u00fa na \u0161\u013eachtenie rastl\u00edn \u2013 vedu o v\u00fdvoji plod\u00edn s vy\u0161\u0161\u00edmi v\u00fdnosmi, odolnos\u0165ou vo\u010di chorob\u00e1m a odolnos\u0165ou vo\u010di zmene kl\u00edmy. Tradi\u010dn\u00e9 met\u00f3dy \u0161\u013eachtenia s\u00fa v\u0161ak pr\u00edli\u0161 pomal\u00e9 na to, aby dr\u017eali krok s naliehavos\u0165ou probl\u00e9mu.<\/p>\n<p>A pr\u00e1ve tu prich\u00e1dzaj\u00fa na sc\u00e9nu drony a umel\u00e1 inteligencia (AI), ktor\u00e9 menia pravidl\u00e1 hry a pon\u00fakaj\u00fa r\u00fdchlej\u0161\u00ed a inteligentnej\u0161\u00ed sp\u00f4sob pestovania lep\u0161\u00edch plod\u00edn.<\/p>\n<h2>Pre\u010do tradi\u010dn\u00e9 \u0161\u013eachtenie rastl\u00edn zaost\u00e1va<\/h2>\n<p>\u0160\u013eachtenie rastl\u00edn sa spolieha na v\u00fdber rastl\u00edn s po\u017eadovan\u00fdmi vlastnos\u0165ami, ako je odolnos\u0165 vo\u010di suchu alebo \u0161kodcom, a ich kr\u00ed\u017eenie po\u010das viacer\u00fdch gener\u00e1ci\u00ed. Najv\u00e4\u010d\u0161\u00edm probl\u00e9mom v tomto procese je fenotypiz\u00e1cia \u2013 manu\u00e1lne meranie charakterist\u00edk rastl\u00edn, ako je v\u00fd\u0161ka, zdravie listov alebo v\u00fdnos.<\/p>\n<p>Napr\u00edklad meranie v\u00fd\u0161ky rastl\u00edn na poli s 3 000 parcelami m\u00f4\u017ee trva\u0165 t\u00fd\u017edne, pri\u010dom \u013eudsk\u00e9 chyby sp\u00f4sobuj\u00fa nezrovnalosti a\u017e do v\u00fd\u0161ky 201 TP3T. Okrem toho sa v\u00fdnosy plod\u00edn zlep\u0161uj\u00fa len o 0,5 \u2013 11 TP3T ro\u010dne, \u010do je hlboko pod mierou rastu 2,91 TP3T potrebnou na splnenie po\u017eiadaviek v roku 2050.<\/p>\n<p>Kukurica, z\u00e1kladn\u00e1 plodina pre miliardy \u013eud\u00ed, ilustruje toto spomalenie: jej ro\u010dn\u00fd rast \u00farody klesol z 2,21 TP3T v 60. rokoch 20. storo\u010dia na 1,331 TP3T dnes. Na preklenutie tejto priepasti vedci potrebuj\u00fa n\u00e1stroje, ktor\u00e9 automatizuj\u00fa zber \u00fadajov, zni\u017euj\u00fa chyby a ur\u00fdch\u013euj\u00fa rozhodovanie.<\/p>\n<h2>Ako technol\u00f3gia dronov transformuje \u0161\u013eachtenie rastl\u00edn<\/h2>\n<p>Drony alebo bezpilotn\u00e9 leteck\u00e9 syst\u00e9my (UAS) vybaven\u00e9 pokro\u010dil\u00fdmi senzormi a umelou inteligenciou sp\u00f4sobuj\u00fa revol\u00faciu v po\u013enohospod\u00e1rstve. Tieto zariadenia dok\u00e1\u017eu lieta\u0165 nad po\u013eami a zhroma\u017e\u010fova\u0165 presn\u00e9 \u00fadaje o tis\u00edckach rastl\u00edn v priebehu nieko\u013ek\u00fdch min\u00fat, \u010do je proces zn\u00e1my ako vysokopriepustn\u00e9 fenotypovanie (HTP).<\/p>\n<p>Na rozdiel od tradi\u010dn\u00fdch met\u00f3d drony zachyt\u00e1vaj\u00fa \u00fadaje z cel\u00fdch pol\u00ed, \u010d\u00edm eliminuj\u00fa skreslenie vzorkovania. Pou\u017e\u00edvaj\u00fa \u0161pecializovan\u00e9 senzory na meranie v\u0161etk\u00e9ho od v\u00fd\u0161ky rastl\u00edn a\u017e po \u00farove\u0148 vodn\u00e9ho stresu.<\/p>\n<p>Napr\u00edklad multispektr\u00e1lne senzory detekuj\u00fa bl\u00edzke infra\u010derven\u00e9 svetlo odr\u00e1\u017ean\u00e9 zdrav\u00fdmi listami, zatia\u013e \u010do termokamery identifikuj\u00fa stres zo sucha meran\u00edm teploty kor\u00fanu.<\/p>\n<p>Automatiz\u00e1ciou zberu \u00fadajov drony zni\u017euj\u00fa n\u00e1klady na pracovn\u00fa silu a ur\u00fdch\u013euj\u00fa \u0161\u013eachtite\u013esk\u00e9 cykly, \u010do umo\u017e\u0148uje vyv\u00edja\u0165 vylep\u0161en\u00e9 odrody plod\u00edn v priebehu rokov namiesto desa\u0165ro\u010d\u00ed.<\/p>\n<h2>Veda o senzoroch a zbere \u00fadajov z dronov<\/h2>\n<p>Drony sa spoliehaj\u00fa na r\u00f4zne senzory na zhroma\u017e\u010fovanie d\u00f4le\u017eit\u00fdch \u00fadajov o rastlin\u00e1ch. RGB kamery, najdostupnej\u0161ia mo\u017enos\u0165, zachyt\u00e1vaj\u00fa vidite\u013en\u00e9 svetlo na meranie pokrytia koruny a v\u00fd\u0161ky rastl\u00edn. Na poliach s cukrovou trstinou dosiahli tieto kamery presnos\u0165 64 \u2013 691 TP3T pri po\u010d\u00edtan\u00ed stoniek a nahradili tak manu\u00e1lne po\u010d\u00edtanie n\u00e1chyln\u00e9 na chyby.<\/p>\n<p>Multispektr\u00e1lne senzory id\u00fa e\u0161te \u010falej a detekuj\u00fa nevidite\u013en\u00e9 vlnov\u00e9 d\u013a\u017eky, ako je bl\u00edzke infra\u010derven\u00e9 \u017eiarenie, ktor\u00e9 koreluj\u00fa s hladinami chlorofylu a zdrav\u00edm rastl\u00edn. Napr\u00edklad predpovedali toleranciu cukrovej trstiny vo\u010di suchu s presnos\u0165ou viac ako 80%.<\/p>\n<ul>\n<li><strong>RGB kamery<\/strong>: Zachytenie \u010derven\u00e9ho, zelen\u00e9ho a modr\u00e9ho svetla na vytvorenie farebn\u00fdch obr\u00e1zkov.<\/li>\n<li><strong>Multispektr\u00e1lne senzory<\/strong>Detekcia svetla za hranicami vidite\u013en\u00e9ho spektra (napr. bl\u00edzkeho infra\u010derven\u00e9ho \u017eiarenia).<\/li>\n<li><strong>Tepeln\u00e9 senzory<\/strong>Meranie tepla vy\u017earovan\u00e9ho rastlinami.<\/li>\n<li><strong>LiDAR<\/strong>Pou\u017e\u00edva laserov\u00e9 impulzy na vytv\u00e1ranie 3D m\u00e1p rastl\u00edn.<\/li>\n<li><strong>Hyperspektr\u00e1lne senzory<\/strong>Zachy\u0165te viac ako 200 sveteln\u00fdch vlnov\u00fdch d\u013a\u017eok pre ultra detailn\u00fa anal\u00fdzu.<\/li>\n<\/ul>\n<p>Tepeln\u00e9 senzory detekuj\u00fa tepeln\u00e9 sign\u00e1ly a identifikuj\u00fa rastliny vystaven\u00e9 stresu z vody, ktor\u00e9 sa zdaj\u00fa by\u0165 teplej\u0161ie ako zdrav\u00e9 rastliny. Na bavln\u00edkov\u00fdch poliach sa term\u00e1lne drony zhodovali s pozemn\u00fdmi meraniami teploty s chybou men\u0161ou ako 5%.<\/p>\n<p>LiDAR senzory vyu\u017e\u00edvaj\u00fa laserov\u00e9 impulzy na vytv\u00e1ranie 3D m\u00e1p plod\u00edn, pri\u010dom v pokusoch s energetickou trstinou meraj\u00fa biomasu a v\u00fd\u0161ku s presnos\u0165ou 95%. Najmodernej\u0161ie n\u00e1stroje, hyperspektr\u00e1lne senzory, analyzuj\u00fa stovky sveteln\u00fdch vlnov\u00fdch d\u013a\u017eok, aby odhalili nedostatok \u017eiv\u00edn alebo choroby nevidite\u013en\u00e9 vo\u013en\u00fdm okom.<\/p>\n<p>Tieto senzory pomohli v\u00fdskumn\u00edkom prepoji\u0165 28 nov\u00fdch g\u00e9nov s oneskoren\u00fdm starnut\u00edm p\u0161enice, \u010do je vlastnos\u0165, ktor\u00e1 zvy\u0161uje v\u00fdnosy.<\/p>\n<h2>Od letu k poznatkom: Ako drony analyzuj\u00fa \u00fadaje o plodin\u00e1ch<\/h2>\n<p>Proces fenotypiz\u00e1cie dronov za\u010d\u00edna starostliv\u00fdm pl\u00e1novan\u00edm letu. Drony lietaj\u00fa vo v\u00fd\u0161ke 30 \u2013 100 metrov a zachyt\u00e1vaj\u00fa prekr\u00fdvaj\u00face sa sn\u00edmky, aby sa zabezpe\u010dilo \u00fapln\u00e9 pokrytie. Napr\u00edklad pole s rozlohou 10 hekt\u00e1rov je mo\u017en\u00e9 naskenova\u0165 za 15 \u2013 30 min\u00fat.<\/p>\n<p>Po lete softv\u00e9r ako Agisoft Metashape spoj\u00ed tis\u00edce obr\u00e1zkov do podrobn\u00fdch m\u00e1p pomocou techniky Structure-from-Motion (SfM) \u2013 techniky, ktor\u00e1 prev\u00e1dza 2D fotografie na 3D modely. Tieto modely umo\u017e\u0148uj\u00fa vedcom mera\u0165 vlastnosti, ako je v\u00fd\u0161ka rastl\u00edn alebo ich pokryvnos\u0165, jedin\u00fdm stla\u010den\u00edm tla\u010didla.<\/p>\n<p>Algoritmy umelej inteligencie potom analyzuj\u00fa \u00fadaje, predpovedaj\u00fa v\u00fdnosy alebo identifikuj\u00fa prepuknutia chor\u00f4b. Napr\u00edklad drony naskenovali 3 132 parciel cukrovej trstiny za p\u00fahych 7 hod\u00edn \u2013 \u00faloha, ktor\u00e1 by manu\u00e1lne trvala tri t\u00fd\u017edne. T\u00e1to r\u00fdchlos\u0165 a presnos\u0165 umo\u017e\u0148uj\u00fa \u0161\u013eachtite\u013eom robi\u0165 r\u00fdchlej\u0161ie rozhodnutia, ako napr\u00edklad vyra\u010fova\u0165 rastliny s n\u00edzkou v\u00fdkonnos\u0165ou na za\u010diatku sez\u00f3ny.<\/p>\n<h2>K\u013e\u00fa\u010dov\u00e9 aplik\u00e1cie dronov v modernom po\u013enohospod\u00e1rstve<\/h2>\n<p>Drony sa pou\u017e\u00edvaj\u00fa na rie\u0161enie niektor\u00fdch z najv\u00e4\u010d\u0161\u00edch v\u00fdziev v po\u013enohospod\u00e1rstve. Jednou z hlavn\u00fdch aplik\u00e1ci\u00ed je priame meranie znakov, kde drony nahr\u00e1dzaj\u00fa manu\u00e1lnu pr\u00e1cu. Na kukuri\u010dn\u00fdch poliach drony meraj\u00fa v\u00fd\u0161ku rastl\u00edn s presnos\u0165ou 90%, \u010d\u00edm zni\u017euj\u00fa chyby z 0,5 metra na 0,21 metra.<\/p>\n<p>Sleduj\u00fa tie\u017e pokrytie koruny, \u010do je metrika ukazuj\u00faca, ako dobre rastliny tienia p\u00f4du, aby potla\u010dili burinu. Pestovatelia energetickej trstiny pou\u017eili tieto \u00fadaje na identifik\u00e1ciu odr\u00f4d, ktor\u00e9 zni\u017euj\u00fa rast buriny o 40%.<\/p>\n<p>\u010eal\u0161\u00edm prelomom je predikt\u00edvne \u0161\u013eachtenie, kde modely umelej inteligencie vyu\u017e\u00edvaj\u00fa \u00fadaje z dronov na predpovedanie v\u00fdnosov plod\u00edn. Napr\u00edklad multispektr\u00e1lne sn\u00edmky predpovedali v\u00fdnosy kukurice s presnos\u0165ou 80%, \u010d\u00edm prekonali tradi\u010dn\u00e9 genomick\u00e9 testovanie.<\/p>\n<p>Drony tie\u017e pom\u00e1haj\u00fa pri objavovan\u00ed g\u00e9nov, \u010do vedcom pom\u00e1ha lokalizova\u0165 segmenty DNA zodpovedn\u00e9 za \u017eiaduce vlastnosti. V pr\u00edpade p\u0161enice drony prepojili zele\u0148 porastu s 22 nov\u00fdmi g\u00e9nmi, \u010do potenci\u00e1lne zvy\u0161uje odolnos\u0165 vo\u010di suchu.<\/p>\n<p>Hyperspektr\u00e1lne senzory navy\u0161e detekuj\u00fa choroby, ako je napr\u00edklad zelenanie citrusov, t\u00fd\u017edne pred objaven\u00edm sa pr\u00edznakov, \u010do d\u00e1va po\u013enohospod\u00e1rom \u010das kona\u0165.<\/p>\n<h2>Zvy\u0161ovanie genetick\u00fdch ziskov pomocou presnej technol\u00f3gie<\/h2>\n<p>Genetick\u00fd zisk \u2013 ro\u010dn\u00e9 zlep\u0161enie vlastnost\u00ed plod\u00edn v d\u00f4sledku \u0161\u013eachtenia \u2013 sa vypo\u010d\u00edta pomocou jednoduch\u00e9ho vzorca:<\/p>\n<p style=\"text-align: center;\"><strong>(Intenzita v\u00fdberu \u00d7 Dedi\u010dnos\u0165 \u00d7 Variabilita znakov) \u00f7 \u010cas \u0161\u013eachtite\u013esk\u00e9ho cyklu.<\/strong><\/p>\n<p style=\"text-align: center;\">Genetick\u00fd zisk (\u0394G) sa vypo\u010d\u00edta ako:<br \/>\n<strong>\u0394G = (i \u00d7 h\u00b2 \u00d7 \u03c3p) \/ L<\/strong><\/p>\n<p style=\"text-align: left;\">Kde:<\/p>\n<ul>\n<li><strong>i<\/strong>\u00a0= Intenzita v\u00fdberu (ak\u00ed pr\u00edsni s\u00fa chovatelia).<\/li>\n<li><strong>h\u00b2<\/strong>\u00a0= Dedi\u010dnos\u0165 (do akej miery sa dan\u00e1 vlastnos\u0165 pren\u00e1\u0161a z rodi\u010dov na potomkov).<\/li>\n<li><strong>\u03c3p<\/strong>\u00a0= Variabilita znakov v popul\u00e1cii.<\/li>\n<li><strong>L<\/strong>\u00a0= \u010cas na cyklus rozmno\u017eovania.<\/li>\n<\/ul>\n<p><strong>Pre\u010do na tom z\u00e1le\u017e\u00ed<\/strong>Drony zlep\u0161uj\u00fa v\u0161etky premenn\u00e9:<\/p>\n<ol start=\"1\">\n<li><strong>i<\/strong>Skenova\u0165\u00a0<strong>10x viac rastl\u00edn<\/strong>, \u010do umo\u017e\u0148uje pr\u00edsnej\u0161\u00ed v\u00fdber.<\/li>\n<li><strong>h\u00b2<\/strong>Zn\u00ed\u017eenie ch\u00fdb merania, zlep\u0161enie odhadov dedi\u010dnosti.<\/li>\n<li><strong>\u03c3p<\/strong>Zachytenie jemn\u00fdch vari\u00e1ci\u00ed znakov v cel\u00fdch poliach.<\/li>\n<li><strong>L<\/strong>Skr\u00e1\u0165te \u010das cyklu z\u00a0<strong>5 rokov a\u017e 2\u20133 roky<\/strong>\u00a0prostredn\u00edctvom skor\u00fdch predpoved\u00ed.<\/li>\n<\/ol>\n<p>Drony vylep\u0161uj\u00fa ka\u017ed\u00fa \u010das\u0165 tejto rovnice. Skenovan\u00edm cel\u00fdch pol\u00ed umo\u017e\u0148uj\u00fa \u0161\u013eachtite\u013eom vybra\u0165 najlep\u0161\u00edch 1% rastl\u00edn namiesto najlep\u0161\u00edch 10%, \u010d\u00edm zvy\u0161uj\u00fa intenzitu v\u00fdberu. Zlep\u0161uj\u00fa tie\u017e odhady dedi\u010dnosti zn\u00ed\u017een\u00edm ch\u00fdb merania.<\/p>\n<p>Napr\u00edklad manu\u00e1lne hodnotenie v\u00fd\u0161ky rastl\u00edn zav\u00e1dza variabilitu 20%, zatia\u013e \u010do drony ju zni\u017euj\u00fa na 5%. Drony navy\u0161e zachyt\u00e1vaj\u00fa jemn\u00e9 vari\u00e1cie znakov v tis\u00edckach rastl\u00edn, \u010d\u00edm maximalizuj\u00fa variabilitu znakov.<\/p>\n<p>A \u010do je najd\u00f4le\u017eitej\u0161ie, skracuj\u00fa cykly rozmno\u017eovania t\u00fdm, \u017ee umo\u017e\u0148uj\u00fa v\u010dasn\u00e9 predpovede. Pestovatelia cukrovej trstiny pou\u017e\u00edvaj\u00faci drony strojn\u00e1sobili svoje genetick\u00e9 zisky v porovnan\u00ed s tradi\u010dn\u00fdmi met\u00f3dami, \u010do dokazuje transforma\u010dn\u00fd potenci\u00e1l tejto technol\u00f3gie.<\/p>\n<h2>Prekon\u00e1vanie v\u00fdziev a prij\u00edmanie bud\u00facnosti<\/h2>\n<p>Napriek s\u013eubn\u00fdm v\u00fdsledkom \u010del\u00ed fenotypiz\u00e1cia pomocou dronov zna\u010dn\u00fdm v\u00fdzvam. Hlavnou prek\u00e1\u017ekou zost\u00e1vaj\u00fa vysok\u00e9 n\u00e1klady na pokro\u010dil\u00e9 senzory \u2013 napr\u00edklad hyperspektr\u00e1lne kamery m\u00f4\u017eu presiahnu\u0165 $50 000, \u010do ich rob\u00ed pre v\u00e4\u010d\u0161inu mal\u00fdch po\u013enohospod\u00e1rov nedostupn\u00fdmi.<\/p>\n<p>Spracovanie obrovsk\u00e9ho mno\u017estva zozbieran\u00fdch \u00fadajov si tie\u017e vy\u017eaduje zna\u010dn\u00e9 cloudov\u00e9 v\u00fdpo\u010dtov\u00e9 zdroje, \u010do zvy\u0161uje n\u00e1klady. Platformy umelej inteligencie, ako napr\u00edklad AutoGIS, automatizuj\u00fa anal\u00fdzu \u00fadajov, \u010d\u00edm eliminuj\u00fa potrebu manu\u00e1lneho zad\u00e1vania.<\/p>\n<p>V\u00fdskumn\u00edci tie\u017e integruj\u00fa drony s p\u00f4dnymi senzormi a meteorologick\u00fdmi stanicami, \u010d\u00edm vytv\u00e1raj\u00fa syst\u00e9m monitorovania v re\u00e1lnom \u010dase, ktor\u00fd upozor\u0148uje po\u013enohospod\u00e1rov na \u0161kodcov alebo such\u00e1. Tieto inov\u00e1cie pripravuj\u00fa cestu pre nov\u00fa \u00e9ru presn\u00e9ho po\u013enohospod\u00e1rstva, kde rozhodnutia zalo\u017een\u00e9 na d\u00e1tach nahr\u00e1dzaj\u00fa dohady.<\/p>\n<h2>Z\u00e1ver<\/h2>\n<p>Drony a umel\u00e1 inteligencia nielen transformuj\u00fa \u0161\u013eachtenie rastl\u00edn \u2013 nanovo definuj\u00fa udr\u017eate\u013en\u00e9 po\u013enohospod\u00e1rstvo. Umo\u017enen\u00edm r\u00fdchlej\u0161ieho v\u00fdvoja plod\u00edn odoln\u00fdch vo\u010di suchu s vysok\u00fdm v\u00fdnosom by tieto technol\u00f3gie mohli do roku 2050 zdvojn\u00e1sobi\u0165 produkciu potrav\u00edn bez roz\u0161irovania po\u013enohospod\u00e1rskej p\u00f4dy.<\/p>\n<p>T\u00fdm by sa zachr\u00e1nilo viac ako 100 mili\u00f3nov hekt\u00e1rov lesov, \u010do zodpoved\u00e1 rozlohe Egypta, a zn\u00ed\u017eila by sa uhl\u00edkov\u00e1 stopa po\u013enohospod\u00e1rstva. Po\u013enohospod\u00e1ri vyu\u017e\u00edvaj\u00faci \u00fadaje z dronov u\u017e zn\u00ed\u017eili spotrebu vody a pestic\u00eddov a\u017e o 301 TP3T, \u010d\u00edm chr\u00e1nia ekosyst\u00e9my a zni\u017euj\u00fa n\u00e1klady.<\/p>\n<p>Ako poznamenal jeden v\u00fdskumn\u00edk: \u201cU\u017e neh\u00e1dame, ktor\u00e9 rastliny s\u00fa najlep\u0161ie. Hovoria n\u00e1m to drony.\u201d S pokra\u010duj\u00facimi inov\u00e1ciami by t\u00e1to f\u00fazia biol\u00f3gie a technol\u00f3gie mohla zabezpe\u010di\u0165 potravinov\u00fa bezpe\u010dnos\u0165 pre miliardy \u013eud\u00ed a z\u00e1rove\u0148 chr\u00e1ni\u0165 na\u0161u plan\u00e9tu.<\/p>\n<p><strong>Referencia<\/strong>: Khuimphukhieo, I. a da Silva, JA (2025). Vysokokapacitn\u00e9 fenotypovanie (HTP) v ter\u00e9ne zalo\u017een\u00e9 na bezpilotn\u00fdch leteck\u00fdch syst\u00e9moch (UAS) ako sada n\u00e1strojov \u0161\u013eachtite\u013eov rastl\u00edn: komplexn\u00fd preh\u013ead. Smart Agricultural Technology, 100888.<\/p>","protected":false},"excerpt":{"rendered":"<p>Predpoklad\u00e1 sa, \u017ee do roku 2050 dosiahne svetov\u00e1 popul\u00e1cia 9,8 miliardy \u013eud\u00ed, \u010d\u00edm sa zdvojn\u00e1sob\u00ed dopyt po potravin\u00e1ch. Roz\u0161irovanie po\u013enohospod\u00e1rskej p\u00f4dy na uspokojenie tejto potreby je v\u0161ak\u2026<\/p>","protected":false},"author":210157960,"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":"","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","default_image_id":0,"font":"","enabled":false},"version":2},"_wpas_customize_per_network":false,"jetpack_post_was_ever_published":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 plugin v28.5 - https:\/\/yoast.com\/product\/yoast-seo-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\/sk\/blog\/ako-vysokovykonna-fenotypizacia-zalozena-na-uas-transformuje-moderne-slachtenie-rastlin\/\" \/>\n<meta property=\"og:locale\" content=\"sk_SK\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How UAS-Based High-Throughput Phenotyping is Transforming Modern Plant Breeding - GeoPard Agriculture\" \/>\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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