{"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":"kuidas-mehitamata-ohusoidukitel-pohinev-suure-labilaskevoimega-fenotuupimine-muudab-tanapaevast-taimekasvatust","status":"publish","type":"post","link":"https:\/\/geopard.tech\/est\/blog\/how-uas-based-high-throughput-phenotyping-is-transforming-modern-plant-breeding\/","title":{"rendered":"Kuidas mehitamata \u00f5hus\u00f5idukite p\u00f5hine suure l\u00e4bilaskev\u00f5imega fenot\u00fc\u00fcpimine muudab t\u00e4nap\u00e4evast taimekasvatust"},"content":{"rendered":"<p>Prognooside kohaselt ulatub maailma rahvaarv 2050. aastaks 9,8 miljardi inimeseni, mis kahekordistab toidun\u00f5udluse. P\u00f5llumaa laiendamine selle vajaduse rahuldamiseks ei ole aga j\u00e4tkusuutlik. Alates 2000. aastast on loodud \u00fcle 501 300 000 uue p\u00f5llumaa, mis on asendanud metsi ja looduslikke \u00f6kos\u00fcsteeme, s\u00fcvendades kliimamuutusi ja bioloogilise mitmekesisuse kadu.<\/p>\n<p>Selle kriisi v\u00e4ltimiseks p\u00f6\u00f6rduvad teadlased sordiaretuse poole \u2013 teaduse poole, mille eesm\u00e4rk on arendada suurema saagikusega, haiguskindlaid ja kliimamuutustele vastupidavaid p\u00f5llukultuure. Traditsioonilised aretusmeetodid on aga probleemi pakilisusega toimetulekuks liiga aeglased.<\/p>\n<p>Siin tulevadki m\u00e4ngu droonid ja tehisintellekt (AI) kui m\u00e4ngumuutjad, pakkudes kiiremat ja nutikamat viisi paremate p\u00f5llukultuuride aretamiseks.<\/p>\n<h2>Miks traditsiooniline taimekasvatus maha j\u00e4\u00e4b<\/h2>\n<p>Taimearetus tugineb soovitavate omadustega (nt p\u00f5uakindlus v\u00f5i kahjurikindlus) taimede valimisele ja nende ristamisele mitme p\u00f5lvkonna jooksul. Selle protsessi suurim kitsaskoht on fenot\u00fc\u00fcpimine \u2013 taime omaduste, n\u00e4iteks k\u00f5rguse, lehtede tervise v\u00f5i saagikuse k\u00e4sitsi m\u00f5\u00f5tmine.<\/p>\n<p>N\u00e4iteks taimede k\u00f5rguse m\u00f5\u00f5tmine 3000 katselapiga p\u00f5llul v\u00f5ib v\u00f5tta n\u00e4dalaid ning inimlikud vead v\u00f5ivad p\u00f5hjustada kuni 20% ulatuses vastuolusid. Lisaks paraneb saagikus vaid 0,5\u20131% aastas, mis on tunduvalt alla 2050. aasta n\u00f5udluse rahuldamiseks vajaliku 2,9% kasvum\u00e4\u00e4ra.<\/p>\n<p>Mais, mis on miljardite inimeste jaoks peamine p\u00f5llukultuur, illustreerib seda aeglustumist: selle aastane saagikuse kasv on langenud 2,21 TP3T-lt 1960. aastatel 1,331 TP3T-ni t\u00e4nap\u00e4eval. Selle l\u00f5he \u00fcletamiseks vajavad teadlased t\u00f6\u00f6riistu, mis automatiseerivad andmete kogumist, v\u00e4hendavad vigu ja kiirendavad otsuste tegemist.<\/p>\n<h2>Kuidas droonitehnoloogia muudab taimekasvatust<\/h2>\n<p>Droonid ehk mehitamata \u00f5hus\u00f5idukid (UAS), mis on varustatud t\u00e4iustatud andurite ja tehisintellektiga, on p\u00f5llumajanduses revolutsiooniliselt muutmas. Need seadmed suudavad lennata \u00fcle p\u00f5ldude ja koguda minutitega t\u00e4pseid andmeid tuhandete taimede kohta \u2013 seda protsessi nimetatakse suure l\u00e4bilaskev\u00f5imega fenot\u00fc\u00fcpimiseks (HTP).<\/p>\n<p>Erinevalt traditsioonilistest meetoditest koguvad droonid andmeid tervetelt p\u00f5ldudelt, k\u00f5rvaldades valimi kallutatuse. Nad kasutavad spetsiaalseid andureid, et m\u00f5\u00f5ta k\u00f5ike alates taimede k\u00f5rgusest kuni veestressi tasemeni.<\/p>\n<p>N\u00e4iteks multispektraalsed andurid tuvastavad tervetelt lehtedelt peegelduvat l\u00e4hiinfrapunavalgust, samas kui termokaamerad tuvastavad p\u00f5uastressi v\u00f5ra temperatuuri m\u00f5\u00f5tmise teel.<\/p>\n<p>Andmete kogumise automatiseerimise abil v\u00e4hendavad droonid t\u00f6\u00f6j\u00f5ukulusid ja kiirendavad aretusts\u00fcklit, v\u00f5imaldades t\u00e4iustatud p\u00f5llukultuuride sorte arendada aastate, mitte aastak\u00fcmnete jooksul.<\/p>\n<h2>Droonide andurite ja andmete kogumise taga peituv teadus<\/h2>\n<p>Droonid kasutavad oluliste taimeandmete kogumiseks mitmesuguseid andureid. RGB-kaamerad, mis on k\u00f5ige soodsam variant, p\u00fc\u00fcavad kinni n\u00e4htavat valgust, et m\u00f5\u00f5ta v\u00f5rade katvust ja taimede k\u00f5rgust. Suhkruroo p\u00f5ldudel on need kaamerad saavutanud varte loendamise t\u00e4psuse 64\u201369%, asendades veaaltid k\u00e4sitsi loendamise meetodid.<\/p>\n<p>Multispektraalsed andurid l\u00e4hevad kaugemale, tuvastades n\u00e4htamatuid lainepikkusi, n\u00e4iteks l\u00e4hiinfrapunakiirgust, mis korreleeruvad klorof\u00fclli taseme ja taimede tervisega. N\u00e4iteks on nad ennustanud suhkruroo p\u00f5uakindlust t\u00e4psusega \u00fcle 80%.<\/p>\n<ul>\n<li><strong>RGB-kaamerad<\/strong>: J\u00e4\u00e4dvustage punast, rohelist ja sinist valgust v\u00e4rviliste piltide loomiseks.<\/li>\n<li><strong>Multispektraalsed andurid<\/strong>Tuvastage valgust, mis j\u00e4\u00e4b n\u00e4htava spektri piiridest v\u00e4ljapoole (nt l\u00e4hiinfrapuna).<\/li>\n<li><strong>Termoandurid<\/strong>M\u00f5\u00f5da taimede poolt eraldatavat soojust.<\/li>\n<li><strong>LiDAR<\/strong>Kasutab laserimpulsse taimede 3D-kaartide loomiseks.<\/li>\n<li><strong>H\u00fcperspektraalsed andurid<\/strong>: J\u00e4\u00e4dvusta \u00fclidetailseks anal\u00fc\u00fcsiks \u00fcle 200 valguslainepikkuse.<\/li>\n<\/ul>\n<p>Termosensorid tuvastavad soojussignaale, tuvastades veestressis taimed, mis tunduvad tervetest taimedest kuumemad. Puuvillap\u00f5ldudel on termodroonid sobitanud maapinnal tehtud temperatuurim\u00f5\u00f5tmisi v\u00e4iksema veaga kui 5%.<\/p>\n<p>LiDAR-andurid kasutavad laserimpulsse p\u00f5llukultuuride 3D-kaartide loomiseks, m\u00f5\u00f5tes energiasuhkru katsetes biomassi ja k\u00f5rgust t\u00e4psusega 95%. K\u00f5ige kaasaegsemad t\u00f6\u00f6riistad, h\u00fcperspektraalandurid, anal\u00fc\u00fcsivad sadu valguse lainepikkusi, et tuvastada palja silmaga n\u00e4htamatuid toitainete puudusi v\u00f5i haigusi.<\/p>\n<p>Need sensorid aitasid teadlastel siduda 28 uut geeni nisu vananemise edasil\u00fckkamisega, mis suurendab saagikust.<\/p>\n<h2>Lennust arusaamiseni: kuidas droonid anal\u00fc\u00fcsivad saagiandmeid<\/h2>\n<p>Droonide fenot\u00fc\u00fcpimise protsess algab hoolika lennuplaneerimisega. Droonid lendavad 30\u2013100 meetri k\u00f5rgusel, j\u00e4\u00e4dvustades kattuvaid pilte, et tagada t\u00e4ielik katvus. N\u00e4iteks 10-hektarise p\u00f5llu skaneerimine v\u00f5tab aega 15\u201330 minutit.<\/p>\n<p>P\u00e4rast lendu \u00f5mbleb tarkvara, n\u00e4iteks Agisoft Metashape, tuhandeid pilte detailseteks kaartideks, kasutades struktuuri liikumisest (SfM) \u2013 tehnikat, mis teisendab 2D-fotod 3D-mudeliteks. Need mudelid v\u00f5imaldavad teadlastel nupuvajutusega m\u00f5\u00f5ta selliseid omadusi nagu taimede k\u00f5rgus v\u00f5i v\u00f5rade katvus.<\/p>\n<p>Seej\u00e4rel anal\u00fc\u00fcsivad tehisintellekti algoritmid andmeid, ennustades saagikust v\u00f5i tuvastades haiguspuhanguid. N\u00e4iteks skaneerisid droonid 3132 suhkruroo p\u00f5ldu k\u00f5igest 7 tunniga \u2013 \u00fclesanne, mille k\u00e4sitsi tegemine v\u00f5taks kolm n\u00e4dalat. See kiirus ja t\u00e4psus v\u00f5imaldavad aretajatel teha kiiremaid otsuseid, n\u00e4iteks visata madala saagikusega taimed hooaja alguses \u00e4ra.<\/p>\n<h2>Droonide peamised rakendused t\u00e4nap\u00e4eva p\u00f5llumajanduses<\/h2>\n<p>Droone kasutatakse p\u00f5llumajanduse suurimate v\u00e4ljakutsete lahendamiseks. \u00dcks peamine rakendusala on otsene omaduste m\u00f5\u00f5tmine, kus droonid asendavad k\u00e4sitsi t\u00f6\u00f6d. Maisip\u00f5ldudel m\u00f5\u00f5davad droonid taimede k\u00f5rgust 90% t\u00e4psusega, niitmisvead 0,5 meetrist kuni 0,21 meetrini.<\/p>\n<p>Samuti j\u00e4lgivad nad v\u00f5rade katvust, mis n\u00e4itab, kui h\u00e4sti taimed maapinda umbrohu t\u00f5rjumiseks varjutavad. Energiaroo kasvatajad kasutasid neid andmeid sortide tuvastamiseks, mis v\u00e4hendavad umbrohu kasvu 40% v\u00f5rra.<\/p>\n<p>Teine l\u00e4bimurre on ennustav aretus, kus tehisintellekti mudelid kasutavad droonide andmeid saagikuse prognoosimiseks. N\u00e4iteks on multispektraalsed pildid ennustanud maisi saagikust 80% t\u00e4psusega, \u00fcletades traditsioonilise genoomse testimise tulemusi.<\/p>\n<p>Droonid aitavad kaasa ka geenide avastamisele, aidates teadlastel leida DNA segmente, mis vastutavad soovitavate omaduste eest. Nisu puhul seostasid droonid v\u00f5ra rohelust 22 uue geeniga, mis potentsiaalselt suurendas p\u00f5uakindlust.<\/p>\n<p>Lisaks tuvastavad h\u00fcperspektraalsed andurid haigusi, n\u00e4iteks tsitruseliste rohelust, n\u00e4dalaid enne s\u00fcmptomite ilmnemist, andes p\u00f5llumeestele aega tegutsemiseks.<\/p>\n<h2>Geneetilise kasu suurendamine t\u00e4ppistehnoloogia abil<\/h2>\n<p>Geneetiline juurdekasv \u2013 aretusprotsessist tulenev saagi omaduste aastane paranemine \u2013 arvutatakse lihtsa valemi abil:<\/p>\n<p style=\"text-align: center;\"><strong>(Valiku intensiivsus \u00d7 P\u00e4rilikkus \u00d7 Tunnuste varieeruvus) \u00f7 Aretusts\u00fckli aeg.<\/strong><\/p>\n<p style=\"text-align: center;\">Geneetiline juurdekasv (\u0394G) arvutatakse j\u00e4rgmiselt:<br \/>\n<strong>\u0394G = (i \u00d7 h\u00b2 \u00d7 \u03c3p) \/ L<\/strong><\/p>\n<p style=\"text-align: left;\">Kus:<\/p>\n<ul>\n<li><strong>i<\/strong>\u00a0= Valiku intensiivsus (kui ranged on aretajad).<\/li>\n<li><strong>h\u00b2<\/strong>\u00a0= P\u00e4rilikkus (kui palju tunnusest kandub vanematelt j\u00e4rglastele).<\/li>\n<li><strong>\u03c3p<\/strong>\u00a0= Tunnuste varieeruvus populatsioonis.<\/li>\n<li><strong>L<\/strong>\u00a0= Aeg aretusts\u00fckli kohta.<\/li>\n<\/ul>\n<p><strong>Miks see on oluline<\/strong>Droonid parandavad k\u00f5iki muutujaid:<\/p>\n<ol start=\"1\">\n<li><strong>i<\/strong>Skannimine\u00a0<strong>10 korda rohkem taimi<\/strong>, mis v\u00f5imaldab rangemat valikut.<\/li>\n<li><strong>h\u00b2<\/strong>V\u00e4hendada m\u00f5\u00f5tmisvigu, parandades p\u00e4rilikkuse hinnanguid.<\/li>\n<li><strong>\u03c3p<\/strong>J\u00e4\u00e4dvusta peeneid tunnuste variatsioone tervetel p\u00f5ldudel.<\/li>\n<li><strong>L<\/strong>L\u00fchendada ts\u00fckliaega\u00a0<strong>5 aastat kuni 2\u20133 aastat<\/strong>\u00a0varajaste ennustuste kaudu.<\/li>\n<\/ol>\n<p>Droonid t\u00e4iustavad selle v\u00f5rrandi iga osa. Tervete p\u00f5ldude skaneerimise abil saavad aretajad valida parimad 1% taimed parimate 10% asemel, suurendades valiku intensiivsust. Samuti parandavad nad p\u00e4rilikkuse hinnanguid, v\u00e4hendades m\u00f5\u00f5tmisvigu.<\/p>\n<p>N\u00e4iteks taimede k\u00f5rguse k\u00e4sitsi hindamine toob kaasa 20% varieeruvuse, samas kui droonid v\u00e4hendavad seda 5%-ni. Lisaks sellele j\u00e4\u00e4dvustavad droonid tuhandete taimede peeneid tunnuste varieeruvust, maksimeerides tunnuste varieeruvust.<\/p>\n<p>Mis k\u00f5ige t\u00e4htsam, need l\u00fchendavad aretusts\u00fcklit, v\u00f5imaldades varajasi ennustusi. Droonide abil tegutsevad suhkruroo kasvatajad on oma geneetilist kasu traditsiooniliste meetoditega v\u00f5rreldes kolmekordistanud, mis t\u00f5estab tehnoloogia transformatiivset potentsiaali.<\/p>\n<h2>V\u00e4ljakutsetest \u00fclesaamine ja tuleviku omaksv\u00f5tmine<\/h2>\n<p>Vaatamata paljulubavatele v\u00f5imalustele seisab droonidel p\u00f5hinev fenot\u00fc\u00fcpimine endiselt silmitsi oluliste v\u00e4ljakutsetega. T\u00e4iustatud andurite k\u00f5rge hind on endiselt peamine takistus \u2013 n\u00e4iteks h\u00fcperspektraalkaamerad v\u00f5ivad maksta \u00fcle $50 000, mist\u00f5ttu on need enamiku v\u00e4ikep\u00f5llumeeste jaoks k\u00e4ttesaamatud.<\/p>\n<p>Kogutud tohutu hulga andmete t\u00f6\u00f6tlemine n\u00f5uab ka m\u00e4rkimisv\u00e4\u00e4rseid pilvandmet\u00f6\u00f6tluse ressursse, mis lisab kulusid. Tehisintellekti platvormid, nagu AutoGIS, automatiseerivad andmete anal\u00fc\u00fcsi, v\u00e4listades k\u00e4sitsi sisestamise vajaduse.<\/p>\n<p>Teadlased integreerivad droone ka mullasensorite ja ilmajaamadega, luues reaalajas j\u00e4lgimiss\u00fcsteemi, mis hoiatab p\u00f5llumehi kahjurite v\u00f5i p\u00f5ua eest. Need uuendused sillutavad teed t\u00e4ppisp\u00f5llumajanduse uuele ajastule, kus andmep\u00f5hised otsused asendavad oletusi.<\/p>\n<h2>Kokkuv\u00f5te<\/h2>\n<p>Droonid ja tehisintellekt ei muuda mitte ainult taimekasvatust \u2013 nad m\u00e4\u00e4ratlevad uuesti ka s\u00e4\u00e4stva p\u00f5llumajanduse. V\u00f5imaldades p\u00f5uakindlate ja suure saagikusega p\u00f5llukultuuride kiiremat arendamist, v\u00f5iksid need tehnoloogiad toidutootmise 2050. aastaks kahekordistada ilma p\u00f5llumaad laiendamata.<\/p>\n<p>See s\u00e4\u00e4staks \u00fcle 100 miljoni hektari metsa, mis on v\u00f5rdne Egiptuse suurusega, ja v\u00e4hendaks p\u00f5llumajanduse s\u00fcsiniku jalaj\u00e4lge. Drooniandmeid kasutavad p\u00f5llumehed on juba v\u00e4hendanud vee ja pestitsiidide kasutamist kuni 30% v\u00f5rra, kaitstes \u00f6kos\u00fcsteeme ja v\u00e4hendades kulusid.<\/p>\n<p>Nagu \u00fcks teadlane m\u00e4rkis: \u201cMe ei tegele enam aimamisega, millised taimed on parimad. Droonid \u00fctlevad meile.\u201d J\u00e4tkuva innovatsiooni abil v\u00f5iks see bioloogia ja tehnoloogia \u00fchendamine tagada toiduga kindlustatuse miljarditele inimestele, kaitstes samal ajal meie planeeti.<\/p>\n<p><strong>Viide<\/strong>Khuimphukhieo, I. ja da Silva, JA (2025). Mehitamata \u00f5hus\u00f5idukitel (UAS) p\u00f5hinev suure l\u00e4bilaskev\u00f5imega fenot\u00fc\u00fcpimine (HTP) kui taimekasvatajate t\u00f6\u00f6riistakast: p\u00f5hjalik \u00fclevaade. Smart Agricultural Technology, 100888.<\/p>","protected":false},"excerpt":{"rendered":"<p>2050. aastaks peaks maailma rahvaarv ulatuma 9,8 miljardi inimeseni, mis kahekordistab toiduvajaduse. P\u00f5llumajandusmaa laiendamine selle vajaduse rahuldamiseks on aga...<\/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_feature_clip_id":0,"_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 plugin v27.6 - 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\/est\/blogi\/kuidas-mehitamata-ohusoidukitel-pohinev-suure-labilaskevoimega-fenotuupimine-muudab-tanapaevast-taimekasvatust\/\" \/>\n<meta property=\"og:locale\" content=\"et_EE\" \/>\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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