{"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":"kaip-didelio-nasumo-fenotipavimas-bepilociu-orlaiviu-pagalba-keicia-siuolaikini-augalu-veisima","status":"publish","type":"post","link":"https:\/\/geopard.tech\/lt\/blog\/how-uas-based-high-throughput-phenotyping-is-transforming-modern-plant-breeding\/","title":{"rendered":"Kaip bepilo\u010di\u0173 orlaivi\u0173 sistema pagr\u012fsta didelio na\u0161umo fenotip\u0173 analiz\u0117 kei\u010dia \u0161iuolaikin\u012f augal\u0173 veisim\u0105"},"content":{"rendered":"<p>Prognozuojama, kad iki 2050 m. pasaulio gyventoj\u0173 skai\u010dius pasieks 9,8 milijardo, o tai padvigubins maisto paklaus\u0105. Ta\u010diau dirbamos \u017eem\u0117s pl\u0117tra siekiant patenkinti \u0161\u012f poreik\u012f yra netvari. Nuo 2000 m. sukurta daugiau nei 501 t3 t naujos dirbamos \u017eem\u0117s pakeit\u0117 mi\u0161kus ir nat\u016bralias ekosistemas, o tai pablogina klimato kait\u0105 ir biologin\u0117s \u012fvairov\u0117s nykim\u0105.<\/p>\n<p>Siekdami i\u0161vengti \u0161ios kriz\u0117s, mokslininkai kreipiasi \u012f augal\u0173 selekcij\u0105 \u2013 moksl\u0105, kaip sukurti didesnio derlingumo, atsparesnius ligoms ir klimato kaitai augalus. Ta\u010diau tradiciniai selekcijos metodai yra per l\u0117ti, kad susp\u0117t\u0173 spr\u0119sti problemos skubum\u0105.<\/p>\n<p>B\u016btent \u010dia \u012f \u017eaidim\u0105 \u012f\u017eengia dronai ir dirbtinis intelektas (DI), kurie kei\u010dia \u017eaidimo taisykles ir si\u016blo greitesn\u012f bei sumanesn\u012f b\u016bd\u0105 veisti geresnius pas\u0117lius.<\/p>\n<h2>Kod\u0117l tradicinis augal\u0173 veisimas atsilieka<\/h2>\n<p>Augal\u0173 veisimas remiasi augal\u0173, turin\u010di\u0173 pageidaujam\u0173 savybi\u0173, toki\u0173 kaip atsparumas sausrai ar kenk\u0117jams, atranka ir j\u0173 kry\u017eminimas per kelias kartas. Did\u017eiausia \u0161io proceso kli\u016btis yra fenotipavimas \u2013 rankinis augal\u0173 savybi\u0173, toki\u0173 kaip auk\u0161tis, lap\u0173 sveikata ar derlius, matavimas.<\/p>\n<p>Pavyzd\u017eiui, augal\u0173 auk\u0161\u010dio matavimas 3000 sklypeli\u0173 lauke gali u\u017etrukti savaites, o d\u0117l \u017emogi\u0161k\u0173j\u0173 klaid\u0173 neatitikimai gali siekti iki 20%. Be to, pas\u0117li\u0173 derlius ger\u0117ja vos 0,5\u20131% per metus, o tai gerokai ma\u017eiau nei 2,9% augimo tempas, reikalingas 2050 m. poreikiams patenkinti.<\/p>\n<p>Kukur\u016bzai, pagrindinis milijard\u0173 \u017emoni\u0173 auginamas augalas, iliustruoja \u0161\u012f sul\u0117t\u0117jim\u0105: j\u0173 metinis derliaus augimas suma\u017e\u0117jo nuo 2,21 TP3 T 1960-aisiais iki 1,331 TP3 T \u0161iandien. Norint panaikinti \u0161\u012f atotr\u016bk\u012f, mokslininkams reikia \u012franki\u0173, kurie automatizuot\u0173 duomen\u0173 rinkim\u0105, suma\u017eint\u0173 klaidas ir pagreitint\u0173 sprendim\u0173 pri\u0117mim\u0105.<\/p>\n<h2>Kaip dron\u0173 technologija kei\u010dia augal\u0173 veisim\u0105<\/h2>\n<p>Dronai, arba nepilotuojamos orlaivi\u0173 sistemos (UAS), apr\u016bpintos pa\u017eangiais jutikliais ir dirbtiniu intelektu, kei\u010dia \u017eem\u0117s \u016bk\u012f. \u0160ie \u012frenginiai gali skristi vir\u0161 lauk\u0173 ir per kelias minutes surinkti tikslius duomenis apie t\u016bkstan\u010dius augal\u0173 \u2013 \u0161is procesas vadinamas didelio na\u0161umo fenotipavimu (HTP).<\/p>\n<p>Skirtingai nuo tradicini\u0173 metod\u0173, dronai fiksuoja duomenis i\u0161tisuose laukuose, pa\u0161alindami imties \u0161ali\u0161kum\u0105. Jie naudoja specializuotus jutiklius, kad matuot\u0173 visk\u0105 \u2013 nuo augal\u0173 auk\u0161\u010dio iki vandens streso lygio.<\/p>\n<p>Pavyzd\u017eiui, daugiaspektriniai jutikliai aptinka sveik\u0173 lap\u0173 atspind\u0117t\u0105 artimojo infraraudonojo spektro \u0161vies\u0105, o termin\u0117s kameros nustato sausros stres\u0105 matuodamos lajos temperat\u016br\u0105.<\/p>\n<p>Automatizuodami duomen\u0173 rinkim\u0105, dronai suma\u017eina darbo s\u0105naudas ir pagreitina veisimo ciklus, tod\u0117l galima sukurti geresnes augal\u0173 veisles per metus, o ne de\u0161imtme\u010dius.<\/p>\n<h2>Dron\u0173 jutikli\u0173 ir duomen\u0173 rinkimo mokslas<\/h2>\n<p>Dronai renka svarbius augal\u0173 duomenis naudodami \u012fvairius jutiklius. RGB kameros, pigiausias pasirinkimas, fiksuoja matom\u0105 \u0161vies\u0105, kad matuot\u0173 lajos dang\u0105 ir augal\u0173 auk\u0161t\u012f. Cukranendri\u0173 laukuose \u0161ios kameros pasiek\u0117 64\u201369% tikslum\u0105 skai\u010diuojant stiebus, taip pakeisdamos klaid\u0173 kupin\u0105 rankin\u012f skai\u010diavim\u0105.<\/p>\n<p>Daugiaspektriniai jutikliai dar labiau aptinka nematomus bangos ilgius, tokius kaip artimoji infraraudonoji spinduliuot\u0117, kurie koreliuoja su chlorofilo kiekiu ir augal\u0173 sveikata. Pavyzd\u017eiui, jie numat\u0117 cukranendri\u0173 atsparum\u0105 sausrai daugiau nei 80% tikslumu.<\/p>\n<ul>\n<li><strong>RGB kameros<\/strong>: U\u017efiksuokite raudon\u0105, \u017eali\u0105 ir m\u0117lyn\u0105 \u0161vies\u0105, kad sukurtum\u0117te spalvotus vaizdus.<\/li>\n<li><strong>Daugiaspektriniai jutikliai<\/strong>Aptikti \u0161vies\u0105 u\u017e matomo spektro rib\u0173 (pvz., artim\u0105j\u012f infraraudon\u0105j\u012f spektr\u0105).<\/li>\n<li><strong>Terminiai jutikliai<\/strong>I\u0161matuokite augal\u0173 skleid\u017eiam\u0105 \u0161ilum\u0105.<\/li>\n<li><strong>LiDAR<\/strong>Naudoja lazerio impulsus, kad sukurt\u0173 3D augal\u0173 \u017eem\u0117lapius.<\/li>\n<li><strong>Hiperspektriniai jutikliai<\/strong>U\u017efiksuokite daugiau nei 200 \u0161viesos bangos ilgi\u0173 itin detaliai analizei.<\/li>\n<\/ul>\n<p>Terminiai jutikliai aptinka \u0161ilumos signalus, atpa\u017eindami vandens stres\u0105 patirian\u010dius augalus, kurie atrodo kar\u0161tesni nei sveiki. Medviln\u0117s laukuose terminiai dronai suderino ant\u017eeminius temperat\u016bros matavimus su ma\u017eesne nei 5% paklaida.<\/p>\n<p>LiDAR jutikliai naudoja lazerio impulsus, kad sukurt\u0173 3D pas\u0117li\u0173 \u017eem\u0117lapius, matuodami biomas\u0119 ir auk\u0161t\u012f 95% tikslumu energetini\u0173 cukranendri\u0173 bandymuose. Pa\u017eangiausi \u012frankiai, hiperspektriniai jutikliai, analizuoja \u0161imtus \u0161viesos bangos ilgi\u0173, kad nustatyt\u0173 maistini\u0173 med\u017eiag\u0173 tr\u016bkum\u0105 ar ligas, nematomas plika akimi.<\/p>\n<p>\u0160ie jutikliai pad\u0117jo tyr\u0117jams susieti 28 naujus genus su u\u017edelstu kvie\u010di\u0173 sen\u0117jimu \u2013 savybe, kuri didina derli\u0173.<\/p>\n<h2>Nuo skryd\u017eio iki \u012f\u017evalgos: kaip dronai analizuoja pas\u0117li\u0173 duomenis<\/h2>\n<p>Dron\u0173 fenotipavimo procesas prasideda nuo kruop\u0161taus skryd\u017eio planavimo. Dronai skraido 30\u2013100 metr\u0173 auk\u0161tyje, fiksuodami persidengian\u010dius vaizdus, kad u\u017etikrint\u0173 visi\u0161k\u0105 apr\u0117pt\u012f. Pavyzd\u017eiui, 10 hektar\u0173 lauk\u0105 galima nuskenuoti per 15\u201330 minu\u010di\u0173.<\/p>\n<p>Po skryd\u017eio programin\u0117 \u012franga, tokia kaip \u201eAgisoft Metashape\u201c, sujungia t\u016bkstan\u010dius vaizd\u0173 \u012f detalius \u017eem\u0117lapius, naudodama \u201eStructure-from-Motion\u201c (SfM) \u2013 technik\u0105, kuri 2D nuotraukas konvertuoja \u012f 3D modelius. \u0160ie modeliai leid\u017eia mokslininkams vienu mygtuko paspaudimu i\u0161matuoti tokias savybes kaip augal\u0173 auk\u0161tis ar lajos danga.<\/p>\n<p>Tada dirbtinio intelekto algoritmai analizuoja duomenis, prognozuodami derli\u0173 arba nustatydami lig\u0173 protr\u016bkius. Pavyzd\u017eiui, dronai nuskenavo 3132 cukranendri\u0173 sklypus vos per 7 valandas \u2013 u\u017eduotis, kuri rankiniu b\u016bdu u\u017etrukt\u0173 tris savaites. Toks greitis ir tikslumas leid\u017eia selekcininkams grei\u010diau priimti sprendimus, pavyzd\u017eiui, sezono prad\u017eioje atmesti neproduktyvius augalus.<\/p>\n<h2>Pagrindiniai dron\u0173 pritaikymai \u0161iuolaikiniame \u017eem\u0117s \u016bkyje<\/h2>\n<p>Dronai naudojami sprend\u017eiant kai kuriuos did\u017eiausius \u016bkininkavimo i\u0161\u0161\u016bkius. Viena i\u0161 pagrindini\u0173 j\u0173 taikymo sri\u010di\u0173 yra tiesioginis savybi\u0173 matavimas, kai dronai pakei\u010dia rankin\u012f darb\u0105. Kukur\u016bz\u0173 laukuose dronai matuoja augal\u0173 auk\u0161t\u012f 90% tikslumu, o pjovimo paklaidos siekia nuo 0,5 metro iki 0,21 metro.<\/p>\n<p>Jie taip pat stebi lajos padengim\u0105 \u2013 rodikl\u012f, rodant\u012f, kaip gerai augalai u\u017edengia \u017eem\u0119, kad slopint\u0173 pikt\u017eoles. Energetini\u0173 cukranendri\u0173 selekcininkai naudojo \u0161iuos duomenis, kad nustatyt\u0173 veisles, kurios suma\u017eina pikt\u017eoli\u0173 augim\u0105 40%.<\/p>\n<p>Kitas prover\u017eis \u2013 nusp\u0117jamasis veisimas, kai dirbtinio intelekto modeliai naudoja dron\u0173 duomenis pas\u0117li\u0173 derliui prognozuoti. Pavyzd\u017eiui, daugiaspektriniai vaizdai kukur\u016bz\u0173 derli\u0173 numat\u0117 80% tikslumu, pranokdami tradicinius genominius tyrimus.<\/p>\n<p>Dronai taip pat padeda atrasti genus, pad\u0117dami mokslininkams rasti DNR segmentus, atsakingus u\u017e pageidaujamus po\u017eymius. Kvie\u010diuose dronai susiejo lajos \u017ealum\u0105 su 22 naujais genais, o tai gali padidinti atsparum\u0105 sausrai.<\/p>\n<p>Be to, hiperspektriniai jutikliai aptinka ligas, tokias kaip citrusini\u0173 vaisi\u0173 \u017ealumas, keliomis savait\u0117mis anks\u010diau nei pasirei\u0161kia simptomai, suteikdami \u016bkininkams laiko imtis veiksm\u0173.<\/p>\n<h2>Genetini\u0173 rezultat\u0173 didinimas naudojant tiksli\u0105sias technologijas<\/h2>\n<p>Genetinis prieaugis \u2013 metinis pas\u0117li\u0173 savybi\u0173 pager\u0117jimas d\u0117l veisimo \u2013 apskai\u010diuojamas pagal paprast\u0105 formul\u0119:<\/p>\n<p style=\"text-align: center;\"><strong>(Atrankos intensyvumas \u00d7 Paveldimumas \u00d7 Po\u017eymi\u0173 kintamumas) \u00f7 Veisimo ciklo laikas.<\/strong><\/p>\n<p style=\"text-align: center;\">Genetinis prieaugis (\u0394G) apskai\u010diuojamas taip:<br \/>\n<strong>\u0394G = (i \u00d7 h\u00b2 \u00d7 \u03c3p) \/ L<\/strong><\/p>\n<p style=\"text-align: left;\">Kur:<\/p>\n<ul>\n<li><strong>i<\/strong>\u00a0= Atrankos intensyvumas (kiek grie\u017eti yra veis\u0117jai).<\/li>\n<li><strong>h\u00b2<\/strong>\u00a0= Paveldimumas (kiek bruo\u017eo perduodama i\u0161 t\u0117v\u0173 palikuonims).<\/li>\n<li><strong>\u03c3p<\/strong>\u00a0= Po\u017eymi\u0173 kintamumas populiacijoje.<\/li>\n<li><strong>L<\/strong>\u00a0= Laikas per veisimo cikl\u0105.<\/li>\n<\/ul>\n<p><strong>Kod\u0117l tai svarbu<\/strong>Dronai pagerina visus kintamuosius:<\/p>\n<ol start=\"1\">\n<li><strong>i<\/strong>: Nuskaityti\u00a0<strong>10 kart\u0173 daugiau augal\u0173<\/strong>, leid\u017eianti grie\u017e\u010diau rinktis.<\/li>\n<li><strong>h\u00b2<\/strong>Suma\u017einti matavimo paklaidas, pagerinant paveldimumo \u012fver\u010dius.<\/li>\n<li><strong>\u03c3p<\/strong>U\u017efiksuokite subtilius po\u017eymi\u0173 skirtumus visuose laukuose.<\/li>\n<li><strong>L<\/strong>Suma\u017einti ciklo laik\u0105 nuo\u00a0<strong>Nuo 5 met\u0173 iki 2\u20133 met\u0173<\/strong>\u00a0per ankstyvas prognozes.<\/li>\n<\/ol>\n<p>Dronai pagerina kiekvien\u0105 \u0161ios lygties dal\u012f. Skenuodami i\u0161tisus laukus, jie leid\u017eia selekcininkams atrinkti geriausius 1% augalus, o ne geriausius 10%, taip padidindami atrankos intensyvum\u0105. Jie taip pat pagerina paveldimumo \u012fver\u010dius, suma\u017eindami matavimo paklaidas.<\/p>\n<p>Pavyzd\u017eiui, rankiniu b\u016bdu vertinant augalo auk\u0161t\u012f, atsiranda 20% kintamumas, o dronai \u0161\u012f rodikl\u012f suma\u017eina iki 5%. Be to, dronai fiksuoja subtilius po\u017eymi\u0173 skirtumus t\u016bkstan\u010diuose augal\u0173, taip maksimaliai padidindami po\u017eymi\u0173 kintamum\u0105.<\/p>\n<p>Svarbiausia, kad jie sutrumpina veisimo ciklus, nes leid\u017eia anksti prognozuoti. Cukranendri\u0173 augintojai, naudodami dronus, patrigubino savo genetin\u012f prana\u0161um\u0105, palyginti su tradiciniais metodais, o tai \u012frodo technologijos transformacin\u012f potencial\u0105.<\/p>\n<h2>I\u0161\u0161\u016bki\u0173 \u012fveikimas ir ateities pri\u0117mimas<\/h2>\n<p>Nepaisant daug \u017eadan\u010dio potencialo, dronais pagr\u012fsta fenotip\u0173 analiz\u0117 vis dar susiduria su dideliais i\u0161\u0161\u016bkiais. Didel\u0117 pa\u017eangi\u0173 jutikli\u0173 kaina i\u0161lieka pagrindine kli\u016btimi \u2013 pavyzd\u017eiui, hiperspektrin\u0117s kameros gali kainuoti daugiau nei $50 000, tod\u0117l jos ne\u012fperkamos daugumai smulki\u0173j\u0173 \u016bkinink\u0173.<\/p>\n<p>Dideli\u0173 surinkt\u0173 duomen\u0173 kieki\u0173 apdorojimas taip pat reikalauja dideli\u0173 debes\u0173 kompiuterijos i\u0161tekli\u0173, o tai padidina i\u0161laidas. Dirbtinio intelekto platformos, tokios kaip \u201eAutoGIS\u201c, automatizuoja duomen\u0173 analiz\u0119, tod\u0117l nereikia rankinio \u012fvedimo.<\/p>\n<p>Tyr\u0117jai taip pat integruoja dronus su dirvo\u017eemio jutikliais ir meteorologin\u0117mis stotimis, kurdami realaus laiko steb\u0117jimo sistem\u0105, kuri \u012fsp\u0117ja \u016bkininkus apie kenk\u0117jus ar sausras. \u0160ios inovacijos atveria keli\u0105 naujai tiksliosios \u017eemdirbyst\u0117s erai, kurioje duomenimis pagr\u012fsti sprendimai pakei\u010dia sp\u0117liones.<\/p>\n<h2>I\u0161vada<\/h2>\n<p>Dronai ir dirbtinis intelektas ne tik kei\u010dia augal\u0173 veisim\u0105 \u2013 jie i\u0161 naujo apibr\u0117\u017eia tvar\u0173 \u017eem\u0117s \u016bk\u012f. Sudarydamos s\u0105lygas grei\u010diau auginti sausrai atsparius, didelio derlingumo pas\u0117lius, \u0161ios technologijos iki 2050 m. gal\u0117t\u0173 padvigubinti maisto gamyb\u0105 neple\u010diant dirbamos \u017eem\u0117s plot\u0173.<\/p>\n<p>Tai pad\u0117t\u0173 i\u0161saugoti daugiau nei 100 milijon\u0173 hektar\u0173 mi\u0161k\u0173 \u2013 tai prilygsta Egipto dyd\u017eiui \u2013 ir suma\u017eint\u0173 \u017eem\u0117s \u016bkio anglies p\u0117dsak\u0105. \u016akininkai, naudojantys dron\u0173 duomenis, jau suma\u017eino vandens ir pesticid\u0173 naudojim\u0105 iki 301 TP3 T, apsaugodami ekosistemas ir suma\u017eindami i\u0161laidas.<\/p>\n<p>Kaip pasteb\u0117jo vienas tyr\u0117jas: \u201cMes nebesp\u0117liojame, kurie augalai yra geriausi. Dronai mums tai pasako.\u201d Nuolat diegiant inovacijas, \u0161is biologijos ir technologij\u0173 derinys gal\u0117t\u0173 u\u017etikrinti milijard\u0173 \u017emoni\u0173 apr\u016bpinim\u0105 maistu ir kartu apsaugoti m\u016bs\u0173 planet\u0105.<\/p>\n<p><strong>Nuoroda<\/strong>Khuimphukhieo, I. ir da Silva, JA (2025). Nepilotuojam\u0173 orlaivi\u0173 sistemomis (UAS) pagr\u012fstas didelio na\u0161umo fenotipavimas (HTP) kaip augal\u0173 selekcinink\u0173 \u012franki\u0173 rinkinys: i\u0161sami ap\u017evalga. \u2019Smart Agricultural Technology\u201c, 100888.<\/p>","protected":false},"excerpt":{"rendered":"<p>Prognozuojama, kad iki 2050 m. pasaulio gyventoj\u0173 skai\u010dius pasieks 9,8 milijardo, o tai padvigubins maisto paklaus\u0105. Ta\u010diau dirbamos \u017eem\u0117s pl\u0117tra siekiant patenkinti \u0161\u012f poreik\u012f yra...<\/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_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,"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 Premium plugin v21.6 (Yoast SEO v27.4) - 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\/lt\/tinklarastis\/kaip-didelio-nasumo-fenotipavimas-bepilociu-orlaiviu-pagalba-keicia-siuolaikini-augalu-veisima\/\" \/>\n<meta property=\"og:locale\" content=\"lt_LT\" \/>\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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