{"id":11525,"date":"2025-04-27T00:19:39","date_gmt":"2025-04-26T22:19:39","guid":{"rendered":"https:\/\/geopard.tech\/?p=11525"},"modified":"2025-04-27T00:19:39","modified_gmt":"2025-04-26T22:19:39","slug":"ako-detekcia-viacerych-druhov-burin-na-baze-yolov8-zvysuje-presne-polnohospodarstvo-bavlny","status":"publish","type":"post","link":"https:\/\/geopard.tech\/sk\/blog\/how-yolov8-based-multi-weed-detection-boosts-cotton-precision-agriculture\/","title":{"rendered":"Ako detekcia viacer\u00fdch druhov bur\u00edn na b\u00e1ze YOLOv8 zvy\u0161uje presnos\u0165 pestovania bavlny?"},"content":{"rendered":"<p class=\"ds-markdown-paragraph\">Pestovanie bavlny je d\u00f4le\u017eitou s\u00fa\u010das\u0165ou po\u013enohospod\u00e1rstva v Spojen\u00fdch \u0161t\u00e1toch a v\u00fdznamne prispieva k hospod\u00e1rstvu. Len v roku 2021 farm\u00e1ri zozbierali viac ako 10 mili\u00f3nov akrov bavlny a vyprodukovali viac ako 18 mili\u00f3nov bal\u00edkov v hodnote takmer <span class=\"katex\"><span class=\"katex-mathml\">7,5 miliardy eur. Napriek svojmu hospod\u00e1rskemu v\u00fdznamu \u010del\u00ed pestovanie bavlny ve\u013ekej v\u00fdzve: burine. <\/span><\/span><\/p>\n<p class=\"ds-markdown-paragraph\"><span class=\"katex\"><span class=\"katex-mathml\">Buriny, ktor\u00e9 s\u00fa ne\u017eiaducimi rastlinami rast\u00facimi popri plodin\u00e1ch, s\u00faperia s bavln\u00edkov\u00fdmi rastlinami o z\u00e1kladn\u00e9 zdroje, ako je voda, \u017eiviny a slne\u010dn\u00e9 svetlo. Ak sa nekontroluj\u00fa, m\u00f4\u017eu zn\u00ed\u017ei\u0165 v\u00fdnosy plod\u00edn a\u017e o 50<\/span><span class=\"katex-html\" aria-hidden=\"true\"><span class=\"base\"><span class=\"mord\">7.5 <\/span><span class=\"mord mathnormal\">bi<\/span><span class=\"mord mathnormal\">ll<\/span><span class=\"mord mathnormal\">i<\/span><span class=\"mord mathnormal\">o<\/span><span class=\"mord mathnormal\">n<\/span><span class=\"mord\">.\u00a0<\/span><\/span><\/span><\/span>Okrem finan\u010dnej z\u00e1\u0165a\u017ee vyvol\u00e1va nadmern\u00e9 pou\u017e\u00edvanie herbic\u00eddov obavy o \u017eivotn\u00e9 prostredie, preto\u017ee kontaminuje p\u00f4du a vodn\u00e9 zdroje.<\/p>\n<p class=\"ds-markdown-paragraph\">Na rie\u0161enie t\u00fdchto v\u00fdziev sa v\u00fdskumn\u00edci zameriavaj\u00fa na technol\u00f3gie presn\u00e9ho po\u013enohospod\u00e1rstva - po\u013enohospod\u00e1rsky pr\u00edstup, ktor\u00fd vyu\u017e\u00edva n\u00e1stroje zalo\u017een\u00e9 na \u00fadajoch na optimaliz\u00e1ciu riadenia na \u00farovni po\u013ea. Jedn\u00fdm z prelomov\u00fdch rie\u0161en\u00ed je model YOLOv8 - \u0161pi\u010dkov\u00fd n\u00e1stroj umelej inteligencie na detekciu buriny v re\u00e1lnom \u010dase.<\/p>\n<h2 class=\"ds-markdown-paragraph\">Vzostup rezistencie vo\u010di herbic\u00eddom a jej vplyv<\/h2>\n<p class=\"ds-markdown-paragraph\">Roz\u0161\u00edren\u00e9 pou\u017e\u00edvanie osiva bavlny odoln\u00e9ho vo\u010di herbic\u00eddom (HR) od roku 1996 zmenilo po\u013enohospod\u00e1rske postupy. HR plodiny s\u00fa geneticky modifikovan\u00e9 tak, aby pre\u017eili \u0161pecifick\u00e9 herbic\u00eddy, \u010do umo\u017e\u0148uje po\u013enohospod\u00e1rom strieka\u0165 chemik\u00e1lie ako glyfos\u00e1t priamo na plodiny bez toho, aby im ubl\u00ed\u017eili.<\/p>\n<p class=\"ds-markdown-paragraph\">Do roku 2020 sa na 96% plochy bavlny v USA bud\u00fa pou\u017e\u00edva\u0165 HR odrody, \u010d\u00edm sa vytvor\u00ed cyklus z\u00e1vislosti od herbic\u00eddov. Spo\u010diatku bol tento pr\u00edstup \u00fa\u010dinn\u00fd, ale \u010dasom sa v d\u00f4sledku prirodzen\u00e9ho v\u00fdberu vyvinula rezistencia bur\u00edn.<\/p>\n<p class=\"ds-markdown-paragraph\">V s\u00fa\u010dasnosti burina odoln\u00e1 vo\u010di herbic\u00eddom zamoruje 70% americk\u00fdch fariem, \u010do n\u00fati po\u013enohospod\u00e1rov pou\u017e\u00edva\u0165 30% viac chemik\u00e1li\u00ed ako pred desiatimi rokmi. Napr\u00edklad Palmer Amaranth, r\u00fdchlo rast\u00faca burina s vysokou reproduk\u010dnou r\u00fdchlos\u0165ou, m\u00f4\u017ee zn\u00ed\u017ei\u0165 \u00farodu bavlny o 79%, ak sa v\u010das nekontroluje.<\/p>\n<p><img data-recalc-dims=\"1\" fetchpriority=\"high\" decoding=\"async\" data-attachment-id=\"11537\" data-permalink=\"https:\/\/geopard.tech\/sk\/blog\/how-yolov8-based-multi-weed-detection-boosts-cotton-precision-agriculture\/impact-of-herbicide-resistance-on-u-s-farms\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/04\/Impact-of-Herbicide-Resistance-on-U.S.-Farms.png?fit=2592%2C1869&amp;ssl=1\" data-orig-size=\"2592,1869\" data-comments-opened=\"1\" data-image-meta=\"{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;0&quot;}\" data-image-title=\"Impact of Herbicide Resistance on U.S. Farms\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/04\/Impact-of-Herbicide-Resistance-on-U.S.-Farms.png?fit=1024%2C738&amp;ssl=1\" class=\"aligncenter wp-image-11537 size-full\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/04\/Impact-of-Herbicide-Resistance-on-U.S.-Farms.png?resize=810%2C584&#038;ssl=1\" alt=\"Vplyv rezistencie vo\u010di herbic\u00eddom na farmy v USA\" width=\"810\" height=\"584\" srcset=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/04\/Impact-of-Herbicide-Resistance-on-U.S.-Farms.png?w=2592&amp;ssl=1 2592w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/04\/Impact-of-Herbicide-Resistance-on-U.S.-Farms.png?resize=300%2C216&amp;ssl=1 300w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/04\/Impact-of-Herbicide-Resistance-on-U.S.-Farms.png?resize=1024%2C738&amp;ssl=1 1024w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/04\/Impact-of-Herbicide-Resistance-on-U.S.-Farms.png?resize=768%2C554&amp;ssl=1 768w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/04\/Impact-of-Herbicide-Resistance-on-U.S.-Farms.png?resize=1536%2C1108&amp;ssl=1 1536w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/04\/Impact-of-Herbicide-Resistance-on-U.S.-Farms.png?resize=2048%2C1477&amp;ssl=1 2048w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/04\/Impact-of-Herbicide-Resistance-on-U.S.-Farms.png?w=1620&amp;ssl=1 1620w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/04\/Impact-of-Herbicide-Resistance-on-U.S.-Farms.png?w=2430&amp;ssl=1 2430w\" sizes=\"(max-width: 810px) 100vw, 810px\" \/><\/p>\n<p class=\"ds-markdown-paragraph\">Finan\u010dn\u00e1 z\u00e1\u0165a\u017e je obrovsk\u00e1: zvl\u00e1danie odoln\u00fdch bur\u00edn stoj\u00ed po\u013enohospod\u00e1rov miliardy ro\u010dne, zatia\u013e \u010do odtok herbic\u00eddov kontaminuje 41% sladkovodn\u00fdch zdrojov v bl\u00edzkosti po\u013enohospod\u00e1rskej p\u00f4dy. Tieto v\u00fdzvy zd\u00f4raz\u0148uj\u00fa naliehav\u00fa potrebu inovat\u00edvnych rie\u0161en\u00ed, ktor\u00e9 zni\u017euj\u00fa z\u00e1vislos\u0165 od chemik\u00e1li\u00ed a z\u00e1rove\u0148 zachov\u00e1vaj\u00fa produktivitu plod\u00edn.<\/p>\n<h2 class=\"ds-markdown-paragraph\">Strojov\u00e9 videnie: Udr\u017eate\u013en\u00e1 alternat\u00edva pre mana\u017ement bur\u00edn<\/h2>\n<p class=\"ds-markdown-paragraph\">V reakcii na kr\u00edzu rezistencie vo\u010di herbic\u00eddom vyv\u00edjaj\u00fa v\u00fdskumn\u00edci syst\u00e9my strojov\u00e9ho videnia - technol\u00f3gie, ktor\u00e9 kombinuj\u00fa kamery, senzory a algoritmy umelej inteligencie - na presn\u00fa detekciu a klasifik\u00e1ciu buriny. Strojov\u00e9 videnie napodob\u0148uje \u013eudsk\u00e9 vizu\u00e1lne vn\u00edmanie, ale s v\u00e4\u010d\u0161ou r\u00fdchlos\u0165ou a presnos\u0165ou, \u010do umo\u017e\u0148uje automatizovan\u00e9 rozhodovanie.<\/p>\n<p class=\"ds-markdown-paragraph\">Tieto syst\u00e9my umo\u017e\u0148uj\u00fa cielen\u00e9 z\u00e1sahy, napr\u00edklad robotick\u00e9 odstra\u0148ova\u010de buriny, ktor\u00e9 mechanicky odstra\u0148uj\u00fa rastliny, alebo inteligentn\u00e9 postrekova\u010de, ktor\u00e9 aplikuj\u00fa herbic\u00eddy len tam, kde je to potrebn\u00e9. Prv\u00e9 verzie t\u00fdchto technol\u00f3gi\u00ed mali probl\u00e9my s presnos\u0165ou, \u010dasto nespr\u00e1vne identifikovali plodiny ako burinu alebo nedok\u00e1zali odhali\u0165 mal\u00e9 rastliny.<\/p>\n<p class=\"ds-markdown-paragraph\">Pokroky v oblasti hlbok\u00e9ho u\u010denia - podmno\u017einy strojov\u00e9ho u\u010denia, ktor\u00e1 na anal\u00fdzu \u00fadajov vyu\u017e\u00edva neur\u00f3nov\u00e9 siete s viacer\u00fdmi vrstvami - v\u0161ak v\u00fdrazne zlep\u0161ili v\u00fdkon. Konvolu\u010dn\u00e9 neur\u00f3nov\u00e9 siete (CNN), typ modelu hlbok\u00e9ho u\u010denia optimalizovan\u00e9ho na anal\u00fdzu obrazu, vynikaj\u00fa v rozpozn\u00e1van\u00ed vzorov vo vizu\u00e1lnych \u00fadajoch.<\/p>\n<p class=\"ds-markdown-paragraph\">Rodina modelov YOLO (You Only Look Once), ktor\u00e1 je zn\u00e1ma svojou r\u00fdchlos\u0165ou a presnos\u0165ou pri detekcii objektov, sa stala mimoriadne popul\u00e1rnou v po\u013enohospod\u00e1rstve. Najnov\u0161ia iter\u00e1cia, YOLOv8, dosahuje presnos\u0165 viac ako 90% pri detekcii buriny, \u010d\u00edm men\u00ed pravidl\u00e1 hry v presnom po\u013enohospod\u00e1rstve.<\/p>\n<h2 class=\"ds-markdown-paragraph\">S\u00fabor \u00fadajov CottonWeedDet12: Z\u00e1klad \u00faspechu<\/h2>\n<p class=\"ds-markdown-paragraph\">Tr\u00e9novanie spo\u013eahliv\u00fdch modelov umelej inteligencie si vy\u017eaduje vysokokvalitn\u00e9 \u00fadaje a s\u00fabor \u00fadajov CottonWeedDet12 je d\u00f4le\u017eit\u00fdm zdrojom pre v\u00fdskum detekcie bur\u00edn. S\u00fabor \u00fadajov je \u0161trukt\u00farovan\u00fd s\u00fabor \u00fadajov, ktor\u00fd sa pou\u017e\u00edva na tr\u00e9novanie a testovanie modelov strojov\u00e9ho u\u010denia.<\/p>\n<p class=\"ds-markdown-paragraph\">Tento s\u00fabor \u00fadajov zozbieran\u00fd z v\u00fdskumn\u00fdch fariem na Mississippi State University obsahuje 5 648 sn\u00edmok bavln\u00edkov\u00fdch pol\u00ed s vysok\u00fdm rozl\u00ed\u0161en\u00edm, ktor\u00e9 s\u00fa anotovan\u00e9 9 370 ohrani\u010duj\u00facimi pol\u00ed\u010dkami identifikuj\u00facimi 12 be\u017en\u00fdch druhov bur\u00edn. Ohrani\u010duj\u00face polia s\u00fa obd\u013a\u017enikov\u00e9 r\u00e1m\u010deky nakreslen\u00e9 okolo objektov z\u00e1ujmu (napr. bur\u00edn) na sn\u00edmkach, ktor\u00e9 poskytuj\u00fa presn\u00e9 umiestnenie na tr\u00e9novanie modelov umelej inteligencie. Medzi k\u013e\u00fa\u010dov\u00e9 vlastnosti patria:<\/p>\n<ul>\n<li class=\"ds-markdown-paragraph\"><strong>12 tried bur\u00edn<\/strong>: vodn\u00e1 hrachovina (naj\u010dastej\u0161ie), jitrocel, amarant, ostrica \u0161kvrnit\u00e1 a in\u00e9.<\/li>\n<li class=\"ds-markdown-paragraph\"><strong>9 370 anot\u00e1ci\u00ed ohrani\u010duj\u00facich pol\u00ed<\/strong>: Odborne ozna\u010den\u00e9 pomocou VGG Image Annotator (VIA).<\/li>\n<li class=\"ds-markdown-paragraph\"><strong>R\u00f4zne podmienky<\/strong>: Sn\u00edmky zachyten\u00e9 pri r\u00f4znom osvetlen\u00ed (slne\u010dno, zamra\u010den\u00e9), v r\u00f4znych f\u00e1zach rastu a na r\u00f4znom p\u00f4dnom pozad\u00ed<\/li>\n<\/ul>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" data-attachment-id=\"11531\" data-permalink=\"https:\/\/geopard.tech\/sk\/blog\/how-yolov8-based-multi-weed-detection-boosts-cotton-precision-agriculture\/cottonweeddet12-dataset\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/04\/CottonWeedDet12-Dataset.jpg?fit=622%2C843&amp;ssl=1\" data-orig-size=\"622,843\" data-comments-opened=\"1\" data-image-meta=\"{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;0&quot;}\" data-image-title=\"CottonWeedDet12 Dataset\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/04\/CottonWeedDet12-Dataset.jpg?fit=622%2C843&amp;ssl=1\" class=\"aligncenter wp-image-11531 size-full\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/04\/CottonWeedDet12-Dataset.jpg?resize=622%2C843&#038;ssl=1\" alt=\"CottonWeedDet12 S\u00fabor \u00fadajov\" width=\"622\" height=\"843\" srcset=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/04\/CottonWeedDet12-Dataset.jpg?w=622&amp;ssl=1 622w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/04\/CottonWeedDet12-Dataset.jpg?resize=221%2C300&amp;ssl=1 221w\" sizes=\"(max-width: 622px) 100vw, 622px\" \/><\/p>\n<p class=\"ds-markdown-paragraph\">Ide o r\u00f4zne druhy bur\u00edn, od vodn\u00e9ho lip\u0148a (naj\u010dastej\u0161ie sa vyskytuj\u00faci druh) a\u017e po mrl\u00edk, amarant a ostricu \u0161kvrnit\u00fa. Aby sa zabezpe\u010dilo, \u017ee s\u00fabor \u00fadajov odr\u00e1\u017ea re\u00e1lne podmienky, sn\u00edmky boli nasn\u00edman\u00e9 pri r\u00f4znom osvetlen\u00ed (slne\u010dno, zamra\u010den\u00e9) a v r\u00f4znych f\u00e1zach rastu.<\/p>\n<p class=\"ds-markdown-paragraph\">Niektor\u00e9 buriny sa napr\u00edklad objavuj\u00fa ako mal\u00e9 semen\u00e1\u010diky, zatia\u013e \u010do in\u00e9 s\u00fa u\u017e plne vyvinut\u00e9. Okrem toho s\u00fabor \u00fadajov obsahuje r\u00f4znorod\u00e9 p\u00f4dne pozadie a usporiadanie rastl\u00edn, \u010do napodob\u0148uje zlo\u017eitos\u0165 skuto\u010dn\u00fdch bavln\u00edkov\u00fdch pol\u00ed.<\/p>\n<p class=\"ds-markdown-paragraph\">Pred tr\u00e9novan\u00edm modelu YOLOv8 v\u00fdskumn\u00edci \u00fadaje predspracovali, aby zv\u00fd\u0161ili jeho robustnos\u0165. Predbe\u017en\u00e9 spracovanie zah\u0155\u0148a \u00fapravu nespracovan\u00fdch \u00fadajov s cie\u013eom zlep\u0161i\u0165 ich vhodnos\u0165 na tr\u00e9novanie umelej inteligencie. Techniky, ako je napr\u00edklad roz\u0161\u00edrenie mozaiky - ktor\u00e9 sp\u00e1ja \u0161tyri obr\u00e1zky do jedn\u00e9ho - pomohli simulova\u0165 hust\u00e9 popul\u00e1cie bur\u00edn.<\/p>\n<p class=\"ds-markdown-paragraph\">\u010eal\u0161ie met\u00f3dy, ako napr\u00edklad n\u00e1hodn\u00e9 \u0161k\u00e1lovanie a prevracanie, pripravili model na zvl\u00e1dnutie zmien vo ve\u013ekosti a orient\u00e1cii rastl\u00edn.<\/p>\n<ul>\n<li class=\"ds-markdown-paragraph\">\u0160k\u00e1lovanie (\u00b150%), strihanie (\u00b130\u00b0) a prevracanie, aby sa napodobnila variabilita v re\u00e1lnom svete.<\/li>\n<\/ul>\n<p class=\"ds-markdown-paragraph\">Vizualiza\u010dn\u00e1 technika naz\u00fdvan\u00e1 t-SNE (t-Distributed Stochastic Neighbor Embedding) - algoritmus strojov\u00e9ho u\u010denia, ktor\u00fd zni\u017euje rozmery \u00fadajov s cie\u013eom vytvori\u0165 vizu\u00e1lne zhluky - odhalila odli\u0161n\u00e9 skupiny pre ka\u017ed\u00fa triedu bur\u00edn, \u010do potvrdilo vhodnos\u0165 s\u00faboru \u00fadajov na tr\u00e9novanie modelov na rozpozn\u00e1vanie jemn\u00fdch rozdielov medzi druhmi.<\/p>\n<h2 class=\"ds-markdown-paragraph\">YOLOv8: Technick\u00e9 inov\u00e1cie a architektonick\u00fd pokrok<\/h2>\n<p class=\"ds-markdown-paragraph\">Model YOLOv8 nadv\u00e4zuje na \u00faspech predch\u00e1dzaj\u00facich modelov YOLO s architektonick\u00fdmi vylep\u0161eniami prisp\u00f4soben\u00fdmi pre po\u013enohospod\u00e1rske aplik\u00e1cie. Jeho jadrom je CSPDarknet53, chrbtica neur\u00f3novej siete navrhnut\u00e1 na extrakciu hierarchick\u00fdch funkci\u00ed z obr\u00e1zkov. Chrbtica neur\u00f3novej siete je hlavnou zlo\u017ekou modelu zodpovednou za spracovanie vstupn\u00fdch \u00fadajov a extrakciu relevantn\u00fdch funkci\u00ed.<\/p>\n<p class=\"ds-markdown-paragraph\">Sie\u0165 CSPDarknet53 vyu\u017e\u00edva kr\u00ed\u017eov\u00e9 \u010diasto\u010dn\u00e9 spojenia (Cross Stage Partial - CSP) - dizajn, ktor\u00fd rozde\u013euje mapy funkci\u00ed siete na dve \u010dasti, spracov\u00e1va ich oddelene a nesk\u00f4r ich sp\u00e1ja - na zlep\u0161enie gradientov\u00e9ho toku po\u010das tr\u00e9novania.<\/p>\n<p class=\"ds-markdown-paragraph\">Gradientn\u00fd tok sa vz\u0165ahuje na to, ako efekt\u00edvne neur\u00f3nov\u00e1 sie\u0165 aktualizuje svoje parametre, aby minimalizovala chyby, a jeho zlep\u0161ovanie zabezpe\u010duje, \u017ee sa model efekt\u00edvne u\u010d\u00ed. Architekt\u00fara tie\u017e integruje sie\u0165 s pyram\u00eddou funkci\u00ed (FPN) a sie\u0165 s agreg\u00e1ciou ciest (PAN), ktor\u00e9 spolupracuj\u00fa na zis\u0165ovan\u00ed bur\u00edn vo viacer\u00fdch mierkach.<\/p>\n<ul>\n<li class=\"ds-markdown-paragraph\"><strong>FPN<\/strong>: Rozpozn\u00e1va objekty vo viacer\u00fdch mierkach (napr. mal\u00e9 semen\u00e1\u010diky vs. zrel\u00e1 burina).<\/li>\n<li class=\"ds-markdown-paragraph\"><strong>PAN<\/strong>: Zlep\u0161uje presnos\u0165 lokaliz\u00e1cie sp\u00e1jan\u00edm funkci\u00ed v sie\u0165ov\u00fdch vrstv\u00e1ch.<\/li>\n<\/ul>\n<p>FPN je \u0161trukt\u00fara, ktor\u00e1 kombinuje funkcie s vysok\u00fdm rozl\u00ed\u0161en\u00edm (na detekciu mal\u00fdch objektov) so s\u00e9manticky bohat\u00fdmi funkciami (na rozpozn\u00e1vanie ve\u013ek\u00fdch objektov), zatia\u013e \u010do PAN spres\u0148uje presnos\u0165 lokaliz\u00e1cie sp\u00e1jan\u00edm funkci\u00ed v sie\u0165ov\u00fdch vrstv\u00e1ch. Napr\u00edklad FPN identifikuje mal\u00e9 semen\u00e1\u010diky, zatia\u013e \u010do PAN spres\u0148uje lokaliz\u00e1ciu zrel\u00fdch bur\u00edn.<\/p>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" data-attachment-id=\"11532\" data-permalink=\"https:\/\/geopard.tech\/sk\/blog\/how-yolov8-based-multi-weed-detection-boosts-cotton-precision-agriculture\/yolov8-technical-innovations-and-architectural-advancements\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/04\/YOLOv8-Technical-Innovations-and-Architectural-Advancements.jpg?fit=800%2C426&amp;ssl=1\" data-orig-size=\"800,426\" data-comments-opened=\"1\" data-image-meta=\"{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;0&quot;}\" data-image-title=\"YOLOv8 Technical Innovations and Architectural Advancements\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/04\/YOLOv8-Technical-Innovations-and-Architectural-Advancements.jpg?fit=800%2C426&amp;ssl=1\" class=\"aligncenter wp-image-11532 size-full\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/04\/YOLOv8-Technical-Innovations-and-Architectural-Advancements.jpg?resize=800%2C426&#038;ssl=1\" alt=\"Technick\u00e9 inov\u00e1cie a architektonick\u00e9 vylep\u0161enia YOLOv8\" width=\"800\" height=\"426\" srcset=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/04\/YOLOv8-Technical-Innovations-and-Architectural-Advancements.jpg?w=800&amp;ssl=1 800w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/04\/YOLOv8-Technical-Innovations-and-Architectural-Advancements.jpg?resize=300%2C160&amp;ssl=1 300w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/04\/YOLOv8-Technical-Innovations-and-Architectural-Advancements.jpg?resize=768%2C409&amp;ssl=1 768w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/><\/p>\n<p class=\"ds-markdown-paragraph\">Na rozdiel od star\u0161\u00edch modelov, ktor\u00e9 sa spoliehaj\u00fa na preddefinovan\u00e9 kotviace polia - vopred nastaven\u00e9 tvary ohrani\u010duj\u00facich pol\u00ed, ktor\u00e9 sa pou\u017e\u00edvaj\u00fa na predpovedanie umiestnenia objektov -, pou\u017e\u00edva YOLOv8 detek\u010dn\u00e9 hlavy bez kotiev. Tieto hlavy predpovedaj\u00fa stredy objektov priamo, \u010d\u00edm sa eliminuj\u00fa zlo\u017eit\u00e9 v\u00fdpo\u010dty a zni\u017euje sa po\u010det falo\u0161ne pozit\u00edvnych v\u00fdsledkov.<\/p>\n<p class=\"ds-markdown-paragraph\">T\u00e1to inov\u00e1cia nielen zvy\u0161uje presnos\u0165, ale aj zr\u00fdch\u013euje spracovanie, pri\u010dom YOLOv8 analyzuje obr\u00e1zok len za 6,3 milisekundy na grafickom procesore NVIDIA T4 - vysoko v\u00fdkonnom grafickom procesore optimalizovanom na \u00falohy AI.<\/p>\n<p class=\"ds-markdown-paragraph\">Stratov\u00e1 funkcia modelu - matematick\u00fd vzorec, ktor\u00fd meria, ako dobre sa predpovede modelu zhoduj\u00fa so skuto\u010dn\u00fdmi \u00fadajmi - kombinuje stratu CloU na presnos\u0165 ohrani\u010denia, stratu kr\u00ed\u017eovej entropie na klasifik\u00e1ciu a stratu ohniska rozdelenia na spracovanie nevyv\u00e1\u017een\u00fdch \u00fadajov. Strata CloU (Complete Intersection over Union) zlep\u0161uje zarovnanie ohrani\u010duj\u00facich boxov t\u00fdm, \u017ee berie do \u00favahy oblas\u0165 prekrytia, vzdialenos\u0165 stredov a pomer str\u00e1n medzi predpovedan\u00fdmi a skuto\u010dn\u00fdmi boxmi.<\/p>\n<p class=\"ds-markdown-paragraph\" style=\"text-align: center;\"><strong>Matematicky<\/strong>, celkov\u00e1 strata je: <span class=\"katex-display ds-markdown-math\"><span class=\"katex\"><span class=\"katex-mathml\">L(\u03b8)=7,5\u22c5Lbox+0,5\u22c5Lcls+0,375\u22c5Ldfl+Regulariz\u00e1cia<\/span><span class=\"katex-html\" aria-hidden=\"true\"><span class=\"base\"><span class=\"mord mathnormal\">L<\/span><span class=\"mopen\">(<\/span><span class=\"mord mathnormal\">\u03b8<\/span><span class=\"mclose\">)<\/span><span class=\"mrel\">=<\/span><\/span><span class=\"base\"><span class=\"mord\">7.5<\/span><span class=\"mbin\">\u22c5<\/span><\/span><span class=\"base\"><span class=\"mord\"><span class=\"mord mathnormal\">L<\/span><span class=\"msupsub\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\"><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mtight\"><span class=\"mord text mtight\">box<\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><\/span><\/span><\/span><span class=\"mbin\">+<\/span><\/span><span class=\"base\"><span class=\"mord\">0.5<\/span><span class=\"mbin\">\u22c5<\/span><\/span><span class=\"base\"><span class=\"mord\"><span class=\"mord mathnormal\">L<\/span><span class=\"msupsub\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\"><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mtight\"><span class=\"mord text mtight\">Cls<\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><\/span><\/span><\/span><span class=\"mbin\">+<\/span><\/span><span class=\"base\"><span class=\"mord\">0.375<\/span><span class=\"mbin\">\u22c5<\/span><\/span><span class=\"base\"><span class=\"mord\"><span class=\"mord mathnormal\">L<\/span><span class=\"msupsub\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\"><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mtight\"><span class=\"mord text mtight\">dfl<\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><\/span><\/span><\/span><span class=\"mbin\">+<\/span><\/span><span class=\"base\"><span class=\"mord text\"><span class=\"mord\">Regulariz\u00e1cia<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p class=\"ds-markdown-paragraph\">Strata kr\u00ed\u017eovej entropie hodnot\u00ed presnos\u0165 klasifik\u00e1cie porovnan\u00edm predpovedan\u00fdch pravdepodobnost\u00ed so skuto\u010dn\u00fdmi \u0161t\u00edtkami, zatia\u013e \u010do strata ohniska distrib\u00facie rie\u0161i nerovnov\u00e1hu tried t\u00fdm, \u017ee model viac penalizuje za nespr\u00e1vnu klasifik\u00e1ciu zriedkav\u00fdch bur\u00edn.<\/p>\n<p class=\"ds-markdown-paragraph\">V porovnan\u00ed s predch\u00e1dzaj\u00facimi verziami YOLO je YOLOv8 lep\u0161\u00ed ako v\u0161etky ostatn\u00e9. Napr\u00edklad YOLOv4 dosiahol priemern\u00fa priemern\u00fa presnos\u0165 (mAP) 95,22% pri prekryt\u00ed 50% ohrani\u010duj\u00faceho boxu, zatia\u013e \u010do YOLOv8 dosiahol 96,10%. mAP je metrika, ktor\u00e1 spriemeruje sk\u00f3re presnosti vo v\u0161etk\u00fdch kateg\u00f3ri\u00e1ch, pri\u010dom vy\u0161\u0161ie hodnoty znamenaj\u00fa lep\u0161iu presnos\u0165 detekcie.<\/p>\n<p class=\"ds-markdown-paragraph\">Podobne mAP YOLOv8 pri viacer\u00fdch prahov\u00fdch hodnot\u00e1ch prekr\u00fdvania (0,5 a\u017e 0,95) bola 93,20%, \u010d\u00edm prekonala hodnotu 89,48% YOLOv4. V\u010faka t\u00fdmto zlep\u0161eniam je YOLOv8 najpresnej\u0161\u00edm a najefekt\u00edvnej\u0161\u00edm modelom na detekciu bur\u00edn na bavln\u00edkov\u00fdch poliach.<\/p>\n<h2 class=\"ds-markdown-paragraph\">Tr\u00e9ning modelu: Metodika a v\u00fdsledky<\/h2>\n<p class=\"ds-markdown-paragraph\">Na tr\u00e9novanie YOLOv8 v\u00fdskumn\u00edci pou\u017eili transferov\u00e9 u\u010denie - techniku, pri ktorej sa vopred natr\u00e9novan\u00fd model (u\u017e natr\u00e9novan\u00fd na ve\u013ekom s\u00fabore \u00fadajov) dola\u010fuje na nov\u00fdch \u00fadajoch. Transferov\u00e9 u\u010denie skracuje \u010das tr\u00e9novania a zvy\u0161uje presnos\u0165 t\u00fdm, \u017ee vyu\u017e\u00edva znalosti z\u00edskan\u00e9 z predch\u00e1dzaj\u00facich \u00faloh.<\/p>\n<p class=\"ds-markdown-paragraph\">Model sprac\u00faval obr\u00e1zky v d\u00e1vkach po 32, pri\u010dom pou\u017e\u00edval optimaliz\u00e1tor AdamW - variant optimaliza\u010dn\u00e9ho algoritmu Adam, ktor\u00fd obsahuje rozpad v\u00e1h, aby sa zabr\u00e1nilo nadmern\u00e9mu prisp\u00f4sobeniu - s mierou u\u010denia 0,001.<\/p>\n<p class=\"ds-markdown-paragraph\">Po\u010das 100 epoch (tr\u00e9ningov\u00fdch cyklov) sa model nau\u010dil rozli\u0161ova\u0165 burinu od rastl\u00edn bavlny s pozoruhodnou presnos\u0165ou. Strat\u00e9gie roz\u0161\u00edrenia \u00fadajov, ako napr\u00edklad n\u00e1hodn\u00e9 obracanie obr\u00e1zkov a \u00faprava ich jasu, zabezpe\u010dili, \u017ee model zvl\u00e1dne premenlivos\u0165 re\u00e1lneho sveta.<\/p>\n<p><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" data-attachment-id=\"11538\" data-permalink=\"https:\/\/geopard.tech\/sk\/blog\/how-yolov8-based-multi-weed-detection-boosts-cotton-precision-agriculture\/to-train-yolov8-researchers-used-transfer-learning-a-technique\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/04\/To-train-YOLOv8-researchers-used-transfer-learning%E2%80%94a-technique.png?fit=1024%2C1300&amp;ssl=1\" data-orig-size=\"1024,1300\" data-comments-opened=\"1\" data-image-meta=\"{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;0&quot;}\" data-image-title=\"To train YOLOv8, researchers used transfer learning\u2014a technique\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/04\/To-train-YOLOv8-researchers-used-transfer-learning%E2%80%94a-technique.png?fit=807%2C1024&amp;ssl=1\" class=\"aligncenter wp-image-11538 size-full\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/04\/To-train-YOLOv8-researchers-used-transfer-learning%E2%80%94a-technique.png?resize=810%2C1028&#038;ssl=1\" alt=\"Na tr\u00e9novanie YOLOv8 v\u00fdskumn\u00edci pou\u017eili transferov\u00e9 u\u010denie - techniku\" width=\"810\" height=\"1028\" srcset=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/04\/To-train-YOLOv8-researchers-used-transfer-learning%E2%80%94a-technique.png?w=1024&amp;ssl=1 1024w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/04\/To-train-YOLOv8-researchers-used-transfer-learning%E2%80%94a-technique.png?resize=236%2C300&amp;ssl=1 236w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/04\/To-train-YOLOv8-researchers-used-transfer-learning%E2%80%94a-technique.png?resize=807%2C1024&amp;ssl=1 807w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/04\/To-train-YOLOv8-researchers-used-transfer-learning%E2%80%94a-technique.png?resize=768%2C975&amp;ssl=1 768w\" sizes=\"(max-width: 810px) 100vw, 810px\" \/><\/p>\n<p class=\"ds-markdown-paragraph\">V\u00fdsledky boli p\u00f4sobiv\u00e9. Po\u010das prv\u00fdch 20 epoch dosiahol model presnos\u0165 viac ako 90%, \u010do sved\u010d\u00ed o r\u00fdchlom u\u010den\u00ed. Na konci tr\u00e9ningu model YOLOv8 detekoval ve\u013ek\u00e9 buriny s presnos\u0165ou 94,40%.<\/p>\n<p class=\"ds-markdown-paragraph\">Men\u0161ie buriny sa v\u0161ak uk\u00e1zali ako n\u00e1ro\u010dnej\u0161ie, pri\u010dom presnos\u0165 klesla na 11,90%. Tento rozdiel vypl\u00fdva z nevyv\u00e1\u017eenosti s\u00faboru \u00fadajov: ve\u013ek\u00e9 buriny boli nadmerne zast\u00fapen\u00e9, zatia\u013e \u010do mal\u00e9 semen\u00e1\u010diky boli zriedkav\u00e9. Napriek tomuto obmedzeniu celkov\u00fd v\u00fdkon YOLOv8 predstavuje v\u00fdznamn\u00fd skok vpred.<\/p>\n<h2 class=\"ds-markdown-paragraph\">V\u00fdzvy a bud\u00face smerovanie<\/h2>\n<p class=\"ds-markdown-paragraph\">Hoci je YOLOv8 obrovsk\u00fdm pr\u00eds\u013eubom, v\u00fdzvy pretrv\u00e1vaj\u00fa. Zis\u0165ovanie mal\u00fdch bur\u00edn je rozhoduj\u00face pre v\u010dasn\u00fd z\u00e1sah, preto\u017ee sadenice sa daj\u00fa \u013eah\u0161ie zvl\u00e1dnu\u0165.<\/p>\n<p class=\"ds-markdown-paragraph\">Na rie\u0161enie tohto probl\u00e9mu v\u00fdskumn\u00edci navrhuj\u00fa pou\u017ei\u0165 generat\u00edvne adverzn\u00e9 siete (GAN) - triedu modelov umelej inteligencie, v ktor\u00fdch dve neur\u00f3nov\u00e9 siete (gener\u00e1tor a diskrimin\u00e1tor) s\u00fa\u0165a\u017eia pri vytv\u00e1ran\u00ed realistick\u00fdch syntetick\u00fdch \u00fadajov - na generovanie umel\u00fdch obrazov mal\u00fdch bur\u00edn, \u010d\u00edm sa vyva\u017euje s\u00fabor \u00fadajov.<\/p>\n<p class=\"ds-markdown-paragraph\">\u010eal\u0161ie rie\u0161enie zah\u0155\u0148a integr\u00e1ciu multispektr\u00e1lneho zobrazovania, ktor\u00e9 zachyt\u00e1va \u00fadaje mimo vidite\u013en\u00e9ho svetla (napr. bl\u00edzke infra\u010derven\u00e9 \u017eiarenie), aby sa zv\u00fd\u0161il kontrast medzi plodinami a burinou. Senzory v bl\u00edzkom infra\u010dervenom spektre zis\u0165uj\u00fa obsah chlorofylu, v\u010faka \u010domu sa rastliny javia jasnej\u0161ie a \u013eah\u0161ie sa odli\u0161uj\u00fa od p\u00f4dy.<\/p>\n<p class=\"ds-markdown-paragraph\">Bud\u00face verzie YOLO, ako napr\u00edklad YOLOv9 a YOLOv10, m\u00f4\u017eu presnos\u0165 e\u0161te zlep\u0161i\u0165. O\u010dak\u00e1va sa, \u017ee tieto modely bud\u00fa obsahova\u0165 transforma\u010dn\u00e9 vrstvy - typ architekt\u00fary neur\u00f3novej siete, ktor\u00e1 sprac\u00fava \u00fadaje paralelne a zachyt\u00e1va z\u00e1vislosti na dlh\u00e9 vzdialenosti efekt\u00edvnej\u0161ie ako tradi\u010dn\u00e9 CNN - a dynamick\u00e9 pyram\u00eddy funkci\u00ed, ktor\u00e9 sa prisp\u00f4sobuj\u00fa ve\u013ekosti objektov. Tak\u00fdto pokrok by mohol pom\u00f4c\u0165 spo\u013eahlivej\u0161ie odhali\u0165 mal\u00e9 buriny.<\/p>\n<p class=\"ds-markdown-paragraph\">\u010eal\u0161\u00edm krokom pre po\u013enohospod\u00e1rov je testovanie v ter\u00e9ne. Auton\u00f3mne ple\u010dky vybaven\u00e9 syst\u00e9mom YOLOv8 a kamerami by sa mohli pohybova\u0165 po riadkoch bavlny a mechanicky odstra\u0148ova\u0165 burinu. Podobne by mohli drony s postrekova\u010dmi poh\u00e1\u0148an\u00fdmi umelou inteligenciou presne zacieli\u0165 herbic\u00eddy, \u010d\u00edm by sa zn\u00ed\u017eila spotreba chemik\u00e1li\u00ed a\u017e o 90%.<\/p>\n<p class=\"ds-markdown-paragraph\">Tieto technol\u00f3gie nielen zni\u017euj\u00fa n\u00e1klady, ale aj chr\u00e1nia ekosyst\u00e9my, \u010do je v s\u00falade s cie\u013emi udr\u017eate\u013en\u00e9ho po\u013enohospod\u00e1rstva - filozofie po\u013enohospod\u00e1rstva, ktor\u00e1 uprednost\u0148uje zdravie \u017eivotn\u00e9ho prostredia, ekonomick\u00fa ziskovos\u0165 a soci\u00e1lnu spravodlivos\u0165.<\/p>\n<h2 class=\"ds-markdown-paragraph\">Z\u00e1ver<\/h2>\n<p class=\"ds-markdown-paragraph\">N\u00e1rast bur\u00edn odoln\u00fdch vo\u010di herbic\u00eddom prin\u00fatil po\u013enohospod\u00e1rstvo k inov\u00e1ci\u00e1m a YOLOv8 predstavuje prelom v presnej regul\u00e1cii bur\u00edn. T\u00fdm, \u017ee tento model dosahuje presnos\u0165 96,10% pri detekcii v re\u00e1lnom \u010dase, umo\u017e\u0148uje po\u013enohospod\u00e1rom zn\u00ed\u017ei\u0165 pou\u017e\u00edvanie herbic\u00eddov, zn\u00ed\u017ei\u0165 n\u00e1klady a chr\u00e1ni\u0165 \u017eivotn\u00e9 prostredie.<\/p>\n<p class=\"ds-markdown-paragraph\">Hoci probl\u00e9my, ako je detekcia mal\u00fdch bur\u00edn, pretrv\u00e1vaj\u00fa, neust\u00e1ly pokrok v oblasti umelej inteligencie a senzorov\u00fdch technol\u00f3gi\u00ed pon\u00faka rie\u0161enia. V\u00fdvoj t\u00fdchto n\u00e1strojov s\u013eubuje transform\u00e1ciu pestovania bavlny na udr\u017eate\u013enej\u0161iu a efekt\u00edvnej\u0161iu prax. V nasleduj\u00facich rokoch by integr\u00e1cia syst\u00e9mu YOLOv8 do auton\u00f3mnych syst\u00e9mov mohla sp\u00f4sobi\u0165 revol\u00faciu v po\u013enohospod\u00e1rstve.<\/p>\n<p class=\"ds-markdown-paragraph\">Po\u013enohospod\u00e1ri sa m\u00f4\u017eu spolieha\u0165 na inteligentn\u00e9 roboty a bezpilotn\u00e9 lietadl\u00e1, aby zvl\u00e1dli burinu, \u010d\u00edm sa uvo\u013en\u00ed \u010das a zdroje na in\u00e9 \u00falohy. Tento posun k po\u013enohospod\u00e1rstvu riaden\u00e9mu \u00fadajmi nielen\u017ee zabezpe\u010d\u00ed v\u00fdnosy plod\u00edn, ale aj zdrav\u0161iu plan\u00e9tu pre bud\u00face gener\u00e1cie. Prijat\u00edm technol\u00f3gi\u00ed, ako je YOLOv8, m\u00f4\u017ee po\u013enohospod\u00e1rsky priemysel prekona\u0165 probl\u00e9my rezistencie vo\u010di herbic\u00eddom a pripravi\u0165 p\u00f4du pre ekologickej\u0161iu a produkt\u00edvnej\u0161iu bud\u00facnos\u0165.<\/p>\n<p><strong>Referencia<\/strong>: Khan, A. T., Jensen, S. M., &amp; Khan, A. R. (2025). Pokrok v presnom po\u013enohospod\u00e1rstve: A comparative analysis of YOLOv8 for multi-class weed detection in cotton cultivation (Porovn\u00e1vacia anal\u00fdza syst\u00e9mu YOLOv8 na detekciu bur\u00edn viacer\u00fdch tried pri pestovan\u00ed bavlny). Artificial Intelligence in Agriculture, 15, 182-191. <a href=\"https:\/\/doi.org\/10.1016\/j.aiia.2025.01.013\" rel=\"nofollow\">https:\/\/doi.org\/10.1016\/j.aiia.2025.01.013<\/a><\/p>","protected":false},"excerpt":{"rendered":"<p>Pestovanie bavlny je d\u00f4le\u017eitou s\u00fa\u010das\u0165ou po\u013enohospod\u00e1rstva v Spojen\u00fdch \u0161t\u00e1toch a v\u00fdznamne prispieva k ekonomike. Len v roku 2021 po\u013enohospod\u00e1ri zo\u017eali viac ako 10 mili\u00f3nov\u2026<\/p>","protected":false},"author":210157960,"featured_media":11530,"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":[1657,1658],"tags":[],"class_list":["post-11525","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-precision-farming","category-weed-control"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.6 - 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