{"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":"kaip-yolov8-pagristas-keliu-piktzoliu-aptikimas-pagerina-medvilnes-tiksliaja-zemdirbyste","status":"publish","type":"post","link":"https:\/\/geopard.tech\/lt\/blog\/how-yolov8-based-multi-weed-detection-boosts-cotton-precision-agriculture\/","title":{"rendered":"Kaip daugialypi\u0173 pikt\u017eoli\u0173 aptikimas YOLOv8 pagrindu padidina medviln\u0117s tiksliojo \u016bkininkavimo efektyvum\u0105?"},"content":{"rendered":"<p class=\"ds-markdown-paragraph\">Medviln\u0117s auginimas yra labai svarbi Jungtini\u0173 Valstij\u0173 \u017eem\u0117s \u016bkio dalis, kuri reik\u0161mingai prisideda prie ekonomikos. Vien 2021 m. \u016bkininkai nu\u0117m\u0117 daugiau kaip 10 mln. hektar\u0173 medviln\u0117s, i\u0161 kuri\u0173 buvo pagaminta daugiau kaip 18 mln. ry\u0161uli\u0173 medviln\u0117s, kuri\u0173 vert\u0117 beveik <span class=\"katex\"><span class=\"katex-mathml\">7,5 mlrd. Nepaisant medviln\u0117s svarbos ekonomikai, jos auginimas susiduria su dideliu i\u0161\u0161\u016bkiu - pikt\u017eol\u0117mis. <\/span><\/span><\/p>\n<p class=\"ds-markdown-paragraph\"><span class=\"katex\"><span class=\"katex-mathml\">Pikt\u017eol\u0117s - nepageidaujami augalai, augantys \u0161alia pas\u0117li\u0173, konkuruoja su medviln\u0117s augalais d\u0117l pagrindini\u0173 i\u0161tekli\u0173, pavyzd\u017eiui, vandens, maistini\u0173 med\u017eiag\u0173 ir saul\u0117s \u0161viesos. Nekontroliuojamos jos gali suma\u017einti derli\u0173 iki 50 proc.<\/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>Per didelis herbicid\u0173 naudojimas kelia ne tik finansin\u0119 \u012ftamp\u0105, bet ir aplinkosaugos problemas, nes ter\u0161ia dirvo\u017eem\u012f ir vandens \u0161altinius.<\/p>\n<p class=\"ds-markdown-paragraph\">Siekdami spr\u0119sti \u0161iuos i\u0161\u0161\u016bkius, mokslininkai imasi tiksliosios \u017eemdirbyst\u0117s technologij\u0173 - \u016bkininkavimo metod\u0173, kuriuose naudojami duomenimis pagr\u012fsti \u012frankiai, kad b\u016bt\u0173 optimizuotas lauko valdymas. Vienas i\u0161 novatori\u0161k\u0173 sprendim\u0173 yra YOLOv8 modelis - pa\u017eangiausia dirbtinio intelekto priemon\u0117, skirta pikt\u017eol\u0117ms aptikti realiuoju laiku.<\/p>\n<h2 class=\"ds-markdown-paragraph\">Atsparumo herbicidams did\u0117jimas ir jo poveikis<\/h2>\n<p class=\"ds-markdown-paragraph\">Nuo 1996 m. pla\u010diai prad\u0117jus naudoti herbicidams atsparias (HR) medviln\u0117s s\u0117klas, pasikeit\u0117 \u016bkininkavimo praktika. HR augalai yra geneti\u0161kai modifikuoti taip, kad b\u016bt\u0173 atspar\u016bs tam tikriems herbicidams, tod\u0117l \u016bkininkai gali purk\u0161ti chemines med\u017eiagas, pavyzd\u017eiui, glifosat\u0105, tiesiai ant pas\u0117li\u0173 j\u0173 nepa\u017eeisdami.<\/p>\n<p class=\"ds-markdown-paragraph\">Iki 2020 m. 96% JAV medviln\u0117s ploto bus naudojama HR veisli\u0173 medviln\u0117, taip sukuriant priklausomyb\u0117s nuo herbicid\u0173 cikl\u0105. I\u0161 prad\u017ei\u0173 \u0161is metodas buvo veiksmingas, ta\u010diau ilgainiui d\u0117l nat\u016bralios atrankos pikt\u017eol\u0117s i\u0161siugd\u0117 atsparum\u0105.<\/p>\n<p class=\"ds-markdown-paragraph\">\u0160iuo metu 70% JAV \u016bki\u0173 u\u017ekr\u0117sta herbicidams atsparios pikt\u017eol\u0117s, tod\u0117l \u016bkininkai priversti naudoti 30% daugiau chemini\u0173 med\u017eiag\u0173 nei prie\u0161 de\u0161imtmet\u012f. Pavyzd\u017eiui, Palmerio amarantas, greitai auganti ir daug kart\u0173 dauginanti pikt\u017eol\u0117, gali suma\u017einti medviln\u0117s derli\u0173 79%, jei n\u0117ra laiku suvaldomas.<\/p>\n<p><img data-recalc-dims=\"1\" fetchpriority=\"high\" decoding=\"async\" data-attachment-id=\"11537\" data-permalink=\"https:\/\/geopard.tech\/lt\/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=\"Atsparumo herbicidams poveikis JAV \u016bkiams\" 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\">Finansin\u0117 na\u0161ta did\u017eiul\u0117: atspari\u0173 pikt\u017eoli\u0173 naikinimas \u016bkininkams kasmet kainuoja milijardus, o herbicid\u0173 nuot\u0117kis u\u017eter\u0161ia 41% g\u0117lo vandens \u0161altini\u0173 netoli \u017eem\u0117s \u016bkio naudmen\u0173. \u0160ie i\u0161\u0161\u016bkiai rodo, kad reikia skubiai ie\u0161koti novatori\u0161k\u0173 sprendim\u0173, kurie suma\u017eint\u0173 priklausomyb\u0119 nuo chemini\u0173 med\u017eiag\u0173 ir kartu i\u0161laikyt\u0173 pas\u0117li\u0173 produktyvum\u0105.<\/p>\n<h2 class=\"ds-markdown-paragraph\">Ma\u0161in\u0173 matymas: Tvari alternatyva pikt\u017eoli\u0173 tvarkymui<\/h2>\n<p class=\"ds-markdown-paragraph\">Reaguodami \u012f atsparumo herbicidams kriz\u0119, mokslininkai kuria ma\u0161ininio matymo sistemas - technologijas, kurios sujungia kameras, jutiklius ir dirbtinio intelekto algoritmus, kad gal\u0117t\u0173 tiksliai aptikti ir klasifikuoti pikt\u017eoles. Ma\u0161inin\u0117 vizija imituoja \u017emogaus regim\u0105j\u012f suvokim\u0105, ta\u010diau grei\u010diau ir tiksliau, tod\u0117l galima automatizuotai priimti sprendimus.<\/p>\n<p class=\"ds-markdown-paragraph\">\u0160ios sistemos leid\u017eia tikslingai atlikti intervencinius veiksmus, pavyzd\u017eiui, robotai rav\u0117tojai mechani\u0161kai pa\u0161alina augalus arba i\u0161manieji purk\u0161tuvai i\u0161pur\u0161kia herbicidus tik ten, kur reikia. Ankstyvosios \u0161i\u0173 technologij\u0173 versijos buvo netikslios, nes da\u017enai neteisingai atpa\u017eindavo augalus kaip pikt\u017eoles arba neaptikdavo ma\u017e\u0173 augal\u0173.<\/p>\n<p class=\"ds-markdown-paragraph\">Ta\u010diau pa\u017eanga gilaus mokymosi srityje - ma\u0161ininio mokymosi poskyryje, kuriame duomenims analizuoti naudojami daugiasluoksniai neuroniniai tinklai - gerokai pagerino na\u0161um\u0105. Konvoliuciniai neuroniniai tinklai (CNN) - gilaus mokymosi modelio tipas, optimizuotas vaizd\u0173 analizei - puikiai atpa\u017e\u012fsta vaizdini\u0173 duomen\u0173 modelius.<\/p>\n<p class=\"ds-markdown-paragraph\">YOLO (You Only Look Once) \u0161eimos modeliai, \u017einomi d\u0117l savo grei\u010dio ir tikslumo aptinkant objektus, tapo ypa\u010d populiar\u016bs \u017eem\u0117s \u016bkyje. Naujausia iteracija, YOLOv8, pikt\u017eoli\u0173 aptikimo tikslumas vir\u0161ija 90%, tod\u0117l ji kei\u010dia \u017eaidimo taisykles tiksliojoje \u017eemdirbyst\u0117je.<\/p>\n<h2 class=\"ds-markdown-paragraph\">CottonWeedDet12 duomen\u0173 rinkinys: S\u0117km\u0117s pagrindas<\/h2>\n<p class=\"ds-markdown-paragraph\">Patikimiems dirbtinio intelekto modeliams mokyti reikia auk\u0161tos kokyb\u0117s duomen\u0173, o CottonWeedDet12 duomen\u0173 rinkinys yra labai svarbus \u0161altinis pikt\u017eoli\u0173 aptikimo tyrimams. Duomen\u0173 rinkinys - tai strukt\u016brizuotas duomen\u0173 rinkinys, naudojamas ma\u0161ininio mokymosi modeliams mokyti ir testuoti.<\/p>\n<p class=\"ds-markdown-paragraph\">\u0160\u012f duomen\u0173 rinkin\u012f, surinkt\u0105 i\u0161 Misisip\u0117s valstijos universiteto mokslini\u0173 tyrim\u0173 \u016bki\u0173, sudaro 5 648 didel\u0117s skiriamosios gebos medviln\u0117s lauk\u0173 vaizdai, prie kuri\u0173 prid\u0117ta 9 370 ribo\u017eenkli\u0173, identifikuojan\u010di\u0173 12 \u012fprast\u0173 pikt\u017eoli\u0173 r\u016b\u0161i\u0173. Ribiniai langeliai - tai sta\u010diakampiai r\u0117meliai, nubr\u0117\u017eti aplink dominan\u010dius objektus (pvz., pikt\u017eoles) vaizduose, kuriuose nurodomos tikslios vietos dirbtinio intelekto modeliams mokyti. Pagrindin\u0117s funkcijos:<\/p>\n<ul>\n<li class=\"ds-markdown-paragraph\"><strong>12 pikt\u017eoli\u0173 klasi\u0173<\/strong>: Vandens amaras (da\u017eniausias), rytinis amaras, Palmerio amarantas, d\u0117m\u0117toji spurga ir kt.<\/li>\n<li class=\"ds-markdown-paragraph\"><strong>9 370 ribo\u017eenkli\u0173 anotacij\u0173<\/strong>: \u017denklinama naudojant VGG Image Annotator (VIA).<\/li>\n<li class=\"ds-markdown-paragraph\"><strong>\u012evairios s\u0105lygos<\/strong>: Vaizdai, u\u017efiksuoti esant skirtingam ap\u0161vietimui (saul\u0117ta, debesuota), augimo etapams ir dirvo\u017eemio fonui<\/li>\n<\/ul>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" data-attachment-id=\"11531\" data-permalink=\"https:\/\/geopard.tech\/lt\/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 duomen\u0173 rinkinys\" 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\">Pikt\u017eol\u0117s yra \u012fvairios - nuo vandenini\u0173 kanapi\u0173 (da\u017eniausia) iki dirvini\u0173 na\u0161lai\u010di\u0173, Palmerio amaranto ir d\u0117m\u0117tosios pluk\u0117s. Siekiant u\u017etikrinti, kad duomen\u0173 rinkinys atspind\u0117t\u0173 realias s\u0105lygas, vaizdai buvo fiksuojami esant skirtingam ap\u0161vietimui (saul\u0117ta, debesuota) ir skirtingais augimo etapais.<\/p>\n<p class=\"ds-markdown-paragraph\">Pavyzd\u017eiui, kai kurios pikt\u017eol\u0117s pasirodo kaip ma\u017ei daigeliai, o kitos yra visi\u0161kai suaugusios. Be to, duomen\u0173 rinkinyje yra \u012fvairi\u0173 dirvo\u017eemio fon\u0173 ir augal\u0173 i\u0161sid\u0117stymo, tod\u0117l imituojamas reali\u0173 medviln\u0117s lauk\u0173 sud\u0117tingumas.<\/p>\n<p class=\"ds-markdown-paragraph\">Prie\u0161 prad\u0117dami mokyti YOLOv8 model\u012f, tyr\u0117jai i\u0161 anksto apdorojo duomenis, kad padidint\u0173 jo patikimum\u0105. I\u0161ankstinis apdorojimas apima neapdorot\u0173 duomen\u0173 modifikavim\u0105, siekiant pagerinti j\u0173 tinkamum\u0105 dirbtinio intelekto mokymui. Tokie metodai, kaip mozaikos didinimas, kuris sujungia keturis vaizdus \u012f vien\u0105, pad\u0117jo imituoti tankias pikt\u017eoli\u0173 populiacijas.<\/p>\n<p class=\"ds-markdown-paragraph\">Kitais metodais, pavyzd\u017eiui, atsitiktinio mastelio keitimo ir apvertimo, modelis buvo parengtas taip, kad b\u016bt\u0173 galima apdoroti augal\u0173 dyd\u017eio ir orientacijos poky\u010dius.<\/p>\n<ul>\n<li class=\"ds-markdown-paragraph\">mastelio keitimas (\u00b150%), kirpimas (\u00b130\u00b0) ir apvertimas, kad b\u016bt\u0173 imituojamas realaus pasaulio kintamumas.<\/li>\n<\/ul>\n<p class=\"ds-markdown-paragraph\">Naudojant vizualizavimo metod\u0105, vadinam\u0105 t-SNE (t-Distributed Stochastic Neighbor Embedding) - ma\u0161ininio mokymosi algoritm\u0105, kuris suma\u017eina duomen\u0173 matmenis, kad b\u016bt\u0173 galima sukurti vizualinius klasterius - buvo atskleistos atskiros kiekvienos pikt\u017eoli\u0173 klas\u0117s grup\u0117s, o tai patvirtino, kad duomen\u0173 rinkinys tinkamas modeliams, kuriais galima mokyti atpa\u017einti subtilius r\u016b\u0161i\u0173 skirtumus, kurti.<\/p>\n<h2 class=\"ds-markdown-paragraph\">YOLOv8: technin\u0117s naujov\u0117s ir architekt\u016briniai pasiekimai<\/h2>\n<p class=\"ds-markdown-paragraph\">\"YOLOv8\" remiasi ankstesni\u0173 YOLO modeli\u0173 s\u0117kme ir yra patobulinta \u017eem\u0117s \u016bkio reikm\u0117ms pritaikyta architekt\u016bra. Jo pagrindas - CSPDarknet53 - neuroninio tinklo pagrindas, skirtas hierarchin\u0117ms savyb\u0117ms i\u0161 vaizd\u0173 i\u0161gauti. Neuroninio tinklo pagrindas yra pagrindinis modelio komponentas, atsakingas u\u017e \u012fvesties duomen\u0173 apdorojim\u0105 ir atitinkam\u0173 po\u017eymi\u0173 i\u0161skyrim\u0105.<\/p>\n<p class=\"ds-markdown-paragraph\">\"CSPDarknet53\" naudoja kry\u017eminio etapo dalines (CSP) jungtis - konstrukcij\u0105, pagal kuri\u0105 tinklo po\u017eymi\u0173 \u017eem\u0117lapiai padalijami \u012f dvi dalis, apdorojami atskirai ir v\u0117liau sujungiami, siekiant pagerinti gradiento sraut\u0105 mokymo metu.<\/p>\n<p class=\"ds-markdown-paragraph\">Gradientinis srautas rei\u0161kia, kaip efektyviai neuroninis tinklas atnaujina savo parametrus, kad b\u016bt\u0173 suma\u017eintos klaidos, o jo didinimas u\u017etikrina, kad modelis efektyviai mokyt\u0173si. Architekt\u016broje taip pat integruotas po\u017eymi\u0173 piramid\u0117s tinklas (FPN) ir kelio agregacijos tinklas (PAN), kurie veikia kartu, kad aptikt\u0173 pikt\u017eoles \u012fvairiais mastais.<\/p>\n<ul>\n<li class=\"ds-markdown-paragraph\"><strong>FPN<\/strong>: aptinka \u012fvairaus mastelio objektus (pvz., ma\u017eus daigus ir subrendusias pikt\u017eoles).<\/li>\n<li class=\"ds-markdown-paragraph\"><strong>PAN<\/strong>: Padidina vietos nustatymo tikslum\u0105 sujungiant tinklo sluoksni\u0173 funkcijas.<\/li>\n<\/ul>\n<p>FPN - tai strukt\u016bra, kuri sujungia didel\u0117s skiriamosios gebos po\u017eymius (ma\u017eiems objektams aptikti) ir semanti\u0161kai turtingus po\u017eymius (dideliems objektams atpa\u017einti), o PAN pagerina vietos nustatymo tikslum\u0105 sujungdama tinklo sluoksni\u0173 po\u017eymius. Pavyzd\u017eiui, FPN atpa\u017e\u012fsta ma\u017eus daigus, o PAN patikslina subrendusi\u0173 pikt\u017eoli\u0173 lokalizacij\u0105.<\/p>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" data-attachment-id=\"11532\" data-permalink=\"https:\/\/geopard.tech\/lt\/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=\"YOLOv8 technin\u0117s naujov\u0117s ir architekt\u016briniai pasiekimai\" 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\">Skirtingai nuo senesni\u0173 modeli\u0173, kurie remiasi i\u0161 anksto nustatytais inkariniais langeliais - i\u0161 anksto nustatytomis ribini\u0173 langeli\u0173 formomis, naudojamomis objekto vietai nusp\u0117ti, - \"YOLOv8\" naudoja aptikimo galvutes be inkar\u0173. \u0160ios galvut\u0117s tiesiogiai numato objekt\u0173 centrus, tod\u0117l nereikia atlikti sud\u0117ting\u0173 skai\u010diavim\u0173 ir suma\u017e\u0117ja klaiding\u0173 teigiam\u0173 rezultat\u0173.<\/p>\n<p class=\"ds-markdown-paragraph\">\u0160i naujov\u0117 ne tik padidina tikslum\u0105, bet ir pagreitina apdorojim\u0105 - \"YOLOv8\" analizuoja vaizd\u0105 vos per 6,3 milisekund\u0117s, naudodamas NVIDIA T4 GPU - didelio na\u0161umo grafikos procesori\u0173, optimizuot\u0105 dirbtinio intelekto u\u017eduotims.<\/p>\n<p class=\"ds-markdown-paragraph\">Modelio nuostoli\u0173 funkcija - matematin\u0117 formul\u0117, pagal kuri\u0105 nustatoma, kaip gerai modelio prognoz\u0117s atitinka faktinius duomenis, - sujungia CloU nuostolius, kad b\u016bt\u0173 u\u017etikrintas ribinio lauko tikslumas, kry\u017emin\u0117s entropijos nuostolius, kad b\u016bt\u0173 galima klasifikuoti, ir pasiskirstymo \u017eidinio nuostolius, kad b\u016bt\u0173 galima apdoroti nesubalansuotus duomenis. CloU (Complete Intersection over Union) nuostoliai pagerina ribo\u017eenkli\u0173 suderinim\u0105, atsi\u017evelgiant \u012f persidengimo plot\u0105, centro atstum\u0105 ir aspekt\u0173 santyk\u012f tarp prognozuojam\u0173 ir faktini\u0173 langeli\u0173.<\/p>\n<p class=\"ds-markdown-paragraph\" style=\"text-align: center;\"><strong>Matemati\u0161kai<\/strong>, bendras nuostolis yra: <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+Reguliarizacija<\/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\">langelis<\/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\">Reguliavimas<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p class=\"ds-markdown-paragraph\">Kry\u017emin\u0117s entropijos nuostoliais klasifikavimo tikslumas vertinamas lyginant prognozuojamas tikimybes su tikrosiomis etiket\u0117mis, o pasiskirstymo \u017eidinio nuostoliais sprend\u017eiama klasi\u0173 disbalanso problema, nes modelis labiau baud\u017eiamas u\u017e neteising\u0105 ret\u0173 pikt\u017eoli\u0173 klasifikavim\u0105.<\/p>\n<p class=\"ds-markdown-paragraph\">Lyginant su ankstesn\u0117mis YOLO versijomis, YOLOv8 jas visas lenkia. Pavyzd\u017eiui, YOLOv4 pasiek\u0117 95,22% vidutin\u012f tikslum\u0105 (mAP) esant 50% ribojan\u010dio lauko persidengimui, o YOLOv8 - 96,10%. mAP yra metrika, pagal kuri\u0105 apskai\u010diuojamas vis\u0173 kategorij\u0173 tikslumo bal\u0173 vidurkis, o didesn\u0117s reik\u0161m\u0117s rodo geresn\u012f aptikimo tikslum\u0105.<\/p>\n<p class=\"ds-markdown-paragraph\">Pana\u0161iai, YOLOv8 mAP, esant kelioms persidengimo riboms (nuo 0,5 iki 0,95), buvo 93,20% ir vir\u0161ijo YOLOv4 89,48%. D\u0117l \u0161i\u0173 patobulinim\u0173 YOLOv8 tapo tiksliausiu ir veiksmingiausiu pikt\u017eoli\u0173 medviln\u0117s laukuose aptikimo modeliu.<\/p>\n<h2 class=\"ds-markdown-paragraph\">Modelio mokymas: Metodika ir rezultatai<\/h2>\n<p class=\"ds-markdown-paragraph\">Mokydami YOLOv8 tyr\u0117jai naudojo perk\u0117limo mokym\u0105si - metod\u0105, kai i\u0161 anksto apmokytas modelis (jau apmokytas naudojant didel\u012f duomen\u0173 rinkin\u012f) tikslinamas naudojant naujus duomenis. Perk\u0117limo mokymasis sutrumpina mokymo laik\u0105 ir padidina tikslum\u0105, nes panaudojamos \u017einios, \u012fgytos atliekant ankstesnes u\u017eduotis.<\/p>\n<p class=\"ds-markdown-paragraph\">Modelis vaizdus apdorojo 32 vaizd\u0173 partijomis, naudodamas AdamW optimizatori\u0173 - Adamo optimizavimo algoritmo variant\u0105, \u012f kur\u012f \u012ftrauktas svorio ma\u017e\u0117jimas, kad b\u016bt\u0173 i\u0161vengta perteklinio pritaikymo - su 0,001 mokymosi koeficientu.<\/p>\n<p class=\"ds-markdown-paragraph\">Per 100 epoch\u0173 (mokymo cikl\u0173) modelis i\u0161moko itin tiksliai atskirti pikt\u017eoles nuo medviln\u0117s augal\u0173. Duomen\u0173 papildymo strategijos, pavyzd\u017eiui, atsitiktinis vaizd\u0173 apvertimas ir j\u0173 ry\u0161kumo reguliavimas, u\u017etikrino, kad modelis gal\u0117t\u0173 susidoroti su realaus pasaulio kintamumu.<\/p>\n<p><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" data-attachment-id=\"11538\" data-permalink=\"https:\/\/geopard.tech\/lt\/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=\"Nor\u0117dami apmokyti YOLOv8, tyr\u0117jai taik\u0117 perk\u0117limo mokym\u0105si - metod\u0105.\" 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\">Rezultatai buvo \u012fsp\u016bdingi. Per pirm\u0105sias 20 epoch\u0173 modelis pasiek\u0117 daugiau nei 90% tikslum\u0105, o tai rodo greit\u0105 mokym\u0105si. Iki mokymo pabaigos YOLOv8 aptiko dideles pikt\u017eoles 94,40% tikslumu.<\/p>\n<p class=\"ds-markdown-paragraph\">Ta\u010diau ma\u017eesn\u0117s pikt\u017eol\u0117s pasirod\u0117 esan\u010dios sud\u0117tingesn\u0117s - tikslumas suma\u017e\u0117jo iki 11,90%. \u0160is neatitikimas atsirado d\u0117l duomen\u0173 rinkinio nesubalansuotumo: dideli\u0173 pikt\u017eoli\u0173 buvo per daug, o ma\u017e\u0173 daig\u0173 - retai. Nepaisant \u0161io apribojimo, bendras YOLOv8 na\u0161umas yra didelis \u0161uolis \u012f priek\u012f.<\/p>\n<h2 class=\"ds-markdown-paragraph\">I\u0161\u0161\u016bkiai ir ateities kryptys<\/h2>\n<p class=\"ds-markdown-paragraph\">Nors \"YOLOv8\" teikia daug vil\u010di\u0173, i\u0161\u0161\u016bki\u0173 i\u0161lieka. Labai svarbu anksti aptikti ma\u017eas pikt\u017eoles, nes daigus lengviau suvaldyti.<\/p>\n<p class=\"ds-markdown-paragraph\">Siekdami i\u0161spr\u0119sti \u0161i\u0105 problem\u0105, tyr\u0117jai si\u016blo naudoti generatyvinius prie\u0161prie\u0161os tinklus (GAN) - dirbtinio intelekto modeli\u0173 klas\u0119, kurioje du neuroniniai tinklai (generatorius ir diskriminatorius) konkuruoja, kad sukurt\u0173 tikrovi\u0161kus sintetinius duomenis, kad b\u016bt\u0173 sukurti dirbtiniai ma\u017e\u0173 pikt\u017eoli\u0173 atvaizdai ir taip subalansuotas duomen\u0173 rinkinys.<\/p>\n<p class=\"ds-markdown-paragraph\">Kitas sprendimas - integruoti daugiaspektrinius vaizdus, kurie fiksuoja duomenis ne tik matomoje \u0161viesoje (pvz., artimoje infraraudon\u0173j\u0173 spinduli\u0173 srityje), kad padidint\u0173 pas\u0117li\u0173 ir pikt\u017eoli\u0173 kontrast\u0105. Artim\u0173j\u0173 infraraudon\u0173j\u0173 spinduli\u0173 jutikliai nustato chlorofilo kiek\u012f, tod\u0117l augalai atrodo ry\u0161kesni ir juos lengviau atskirti nuo dirvos.<\/p>\n<p class=\"ds-markdown-paragraph\">B\u016bsimos YOLO versijos, pavyzd\u017eiui, YOLOv9 ir YOLOv10, gali dar labiau padidinti tikslum\u0105. Tikimasi, kad \u012f \u0161iuos modelius bus \u012ftraukti transformuojamieji sluoksniai - neuronini\u0173 tinkl\u0173 architekt\u016bros tipas, kuris duomenis apdoroja lygiagre\u010diai, efektyviau nei tradiciniai CNN fiksuodamas tolim\u0105sias priklausomybes, ir dinamin\u0117s po\u017eymi\u0173 piramid\u0117s, prisitaikan\u010dios prie objekt\u0173 dyd\u017ei\u0173. Tokie patobulinimai gal\u0117t\u0173 pad\u0117ti patikimiau aptikti ma\u017eas pikt\u017eoles.<\/p>\n<p class=\"ds-markdown-paragraph\">Kitas \u016bkinink\u0173 \u017eingsnis - lauko bandymai. Autonominiai rav\u0117tuvai su YOLOv8 ir kameromis gal\u0117t\u0173 jud\u0117ti medviln\u0117s eil\u0117mis ir mechani\u0161kai \u0161alinti pikt\u017eoles. Pana\u0161iai dronai su dirbtinio intelekto valdomais purk\u0161tuvais gal\u0117t\u0173 tiksliai nukreipti herbicidus, taip suma\u017eindami chemini\u0173 med\u017eiag\u0173 naudojim\u0105 iki 90%.<\/p>\n<p class=\"ds-markdown-paragraph\">\u0160ios technologijos ne tik ma\u017eina s\u0105naudas, bet ir saugo ekosistemas, nes atitinka tvaraus \u017eem\u0117s \u016bkio - \u016bkininkavimo filosofijos, kurioje pirmenyb\u0117 teikiama aplinkos sveikatai, ekonominiam pelningumui ir socialiniam teisingumui - tikslus.<\/p>\n<h2 class=\"ds-markdown-paragraph\">I\u0161vada<\/h2>\n<p class=\"ds-markdown-paragraph\">Pikt\u017eoli\u0173, atspari\u0173 herbicidams, plitimas privert\u0117 \u017eem\u0117s \u016bk\u012f diegti naujoves, o YOLOv8 yra prover\u017eis tiksliojo pikt\u017eoli\u0173 valdymo srityje. Pasiekdamas 96,10% tikslum\u0105 nustatant pikt\u017eoles realiuoju laiku, \u0161is modelis suteikia \u016bkininkams galimyb\u0119 suma\u017einti herbicid\u0173 naudojim\u0105, suma\u017einti i\u0161laidas ir apsaugoti aplink\u0105.<\/p>\n<p class=\"ds-markdown-paragraph\">Nors tokie i\u0161\u0161\u016bkiai, kaip ma\u017e\u0173 pikt\u017eoli\u0173 aptikimas, i\u0161lieka, nuolatin\u0117 dirbtinio intelekto ir jutikli\u0173 technologij\u0173 pa\u017eanga si\u016blo sprendimus. Tobul\u0117jant \u0161ioms priemon\u0117ms medviln\u0117s auginimas taps tvaresnis ir efektyvesnis. Ateinan\u010diais metais YOLOv8 integravimas \u012f autonomines sistemas gali sukelti revoliucij\u0105 \u017eem\u0117s \u016bkyje.<\/p>\n<p class=\"ds-markdown-paragraph\">\u016akininkai gali pasikliauti i\u0161maniaisiais robotais ir bepilo\u010diais orlaiviais pikt\u017eol\u0117ms naikinti, taip atlaisvindami laik\u0105 ir i\u0161teklius kitoms u\u017eduotims atlikti. \u0160is per\u0117jimas prie duomenimis pagr\u012fsto \u016bkininkavimo ne tik u\u017etikrina derli\u0173, bet ir sveikesn\u0119 planet\u0105 ateities kartoms. Naudodama tokias technologijas kaip YOLOv8, \u017eem\u0117s \u016bkio pramon\u0117 gali \u012fveikti atsparumo herbicidams i\u0161\u0161\u016bkius ir nutiesti keli\u0105 ekologi\u0161kesnei ir produktyvesnei atei\u010diai.<\/p>\n<p><strong>Nuoroda<\/strong>: Khan, A. T., Jensen, S. M., &amp; Khan, A. R. (2025). Tiksliosios \u017eemdirbyst\u0117s pa\u017eanga: A comparative analysis of YOLOv8 for multi-class weed detection in cotton cultivation. 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>Medviln\u0117s auginimas yra gyvybi\u0161kai svarbi Jungtini\u0173 Valstij\u0173 \u017eem\u0117s \u016bkio dalis, reik\u0161mingai prisidedanti prie ekonomikos. Vien 2021 m. \u016bkininkai nu\u0117m\u0117 daugiau nei 10 mln.\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":"<!-- 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